blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2 values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46 values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14 values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60 values | src_encoding stringclasses 24 values | language stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 7 9.18M | extension stringclasses 20 values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
751733160e37e333ed1204d70f24113ac562c751 | 83b0b99eab88998bdd24ad7671feaeb9f586d0a4 | /man/ggpreview.Rd | 02f9bc0a54735716f46ef32ac07384a88a6c2356 | [] | no_license | GuangchuangYu/ggimage | d3b8a2ada540d500436879f6e6b6ab279d25ad50 | 6aea2a64ba12f3268040f87817154ac9675e2df0 | refs/heads/master | 2023-06-30T15:02:43.149063 | 2023-06-19T03:36:47 | 2023-06-19T03:36:47 | 82,661,218 | 165 | 38 | null | 2023-06-19T03:33:09 | 2017-02-21T09:29:32 | R | UTF-8 | R | false | true | 734 | rd | ggpreview.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggpreview.R
\name{ggpreview}
\alias{ggpreview}
\title{ggpreview}
\usage{
ggpreview(
filename = NULL,
plot = last_plot(),
width = NA,
height = NA,
units = "in",
...
)
}
\arguments{
\item{filename}{If it is not NULL, the previewed figure will be save to the file}
\item{plot}{any plot that supported by the 'ggplotify' package}
\item{width}{width of the figure}
\item{height}{height of the figure}
\item{units}{units of the 'width' and 'height'}
\item{...}{additional parameters pass to ggsave() if filename is not NULL}
}
\value{
a preview of the figure
}
\description{
preview a plot befor saving it to a file.
}
\author{
Guangchuang Yu
}
|
a1e0cced52d83596e35f4a1a64367be86582aefd | 708f744bc98651fd3f78f2d59307509118c16879 | /RKEEL/man/writeDatFromDataframe.Rd | 14af8fed46db09f4ce275c0fe507efb66b75fbec | [] | no_license | i02momuj/RKEEL | 726efa0409193a1ebc6ff82ef195e2708f3fa397 | 445cd8cceade2316bc12d40406c7c1248e2daeaa | refs/heads/master | 2021-01-10T10:13:36.242589 | 2019-07-19T07:43:32 | 2019-07-19T07:43:32 | 49,633,299 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 450 | rd | writeDatFromDataframe.Rd | \docType{methods}
\name{writeDatFromDataframe}
\alias{writeDatFromDataframe}
\title{Write .dat from data.frame}
\description{
Method for writing a .dat dataset file in KEEL format given a data.frame dataset
}
\usage{
writeDatFromDataframe(data, fileName)
}
\arguments{
\item{data}{data.frame dataset}
\item{fileName}{String with the file name to store the dataset}
}
\examples{
#data(iris)
#writeDatFromDataframe(iris, "iris.dat")
}
\keyword{utils}
|
316457a6b7f9825706e1c490e94017da76980b20 | ff70b276b8ac674c4f0aa3771ade4ae1d063d1a0 | /R/createSMDplot.R | a107d4d7d95bf57dc6f92c177b28e11ccc54c164 | [] | no_license | lhenneman/sourceOrientedApproach | f7e4bf1368db44f40aeffe614bd920a0ccde40e4 | 1d4bdf01e0dd2464c3bbeb04e09eb73d985ef00a | refs/heads/master | 2020-12-30T08:53:21.704462 | 2020-03-22T16:35:14 | 2020-03-22T16:35:14 | 238,937,456 | 0 | 1 | null | 2020-02-07T14:04:22 | 2020-02-07T14:04:21 | null | UTF-8 | R | false | false | 3,229 | r | createSMDplot.R | #' Create a plot of the standardized mean differences in covariates
#'
#' This function takes a matchitobject and returns a plot of the standardized
#' mean differences (SMD) between the high exposed and control locations for
#' each covariate in the propensity score model. The SMD is a common way to
#' evalulate whether covariates were balanced between the two groups during matching.
#' Code for this plot was adopted from a vignette to the R tableone package (Yoshida and Bohn).
#'
#' @param matched.model A matchitobject returned from the matchit function. \code{matched.model}
#'
#' @return NA
#'
#' @keywords keywords
#'
#' @export
#'
#' @references
#' \insertRef{austin2011introduction}{sourceOrientedApproach}
#'
#' \insertRef{yoshidapackage}{sourceOrientedApproach}
#'
#' \insertRef{ho2011matchit}{sourceOrientedApproach}
#'
#' @examples
#' # Include these regions
#' regions <- c("IndustrialMidwest", "Northeast", "Southeast")
#'
#' # Covariates to adjust for using propensity score matching
#' covariate.vars <- c("logPop", "PctUrban","MedianHHInc", "PctPoor", "smokerate2000")
#'
#' dataset <- getMatchedDataset(exposure = inmap2005, covariates, covariate.vars, regions)
#'
#' createSMDplot(dataset$matched.model)
createSMDplot <- function(matched.model){
require(ggplot2)
require(MatchIt)
sum.all <- summary(matched.model, standardize = TRUE)[[3]]
SMD.vars <- rownames(sum.all)[-1]
SMD.all <- sum.all[-1,4]
SMD.matched <- summary(matched.model, standardize = TRUE)$sum.matched[-1,4]
SMD.vars <- SMD.vars[!SMD.all %in% c(-Inf, Inf)]
SMD.matched <- SMD.matched[!SMD.all %in% c(-Inf, Inf)]
SMD.all <- SMD.all[!SMD.all %in% c(-Inf, Inf)]
dataPlot <- data.frame(variable = SMD.vars,
Before = SMD.all,
After = SMD.matched)
dataPlotMelt <- melt(data = dataPlot,
id.vars = c("variable"),
variable.name = "Dataset",
value.name = "SMD")
varNames <- as.character(dataPlot$variable)[order(dataPlot$Before)]
dataPlotMelt$variable <- factor(dataPlotMelt$variable, levels = varNames)
cbbPalette <- c("#E69F00", "#0072B2", "#D55E00", "#CC79A7", "#56B4E9", "#009E73","#F0E442")
x.lab <- "Standardized Mean Difference \n (High/Low)"
ggplot(data = dataPlotMelt, mapping = aes(x = variable,
y = SMD,
group = Dataset,
color = Dataset)) +
scale_colour_manual(values=cbbPalette) +
geom_line() +
geom_point() +
geom_hline(yintercept = c(0), color = "black", size = 0.1) +
coord_flip(ylim = c(-2,2)) +
theme_bw() +
theme(text = element_text(family = "Times New Roman"),
legend.key = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_text(size=10),
axis.text.x = element_text(size=12),
axis.title.x = element_text(size=12),
legend.text = element_text(size=12),
legend.title = element_blank(),
legend.position = "right",
plot.title = element_text(size = 12, hjust = 0.5)) +
labs(y = x.lab, title = "")
}
|
db511589ec26a86b0506d54b680381db01dedd46 | ca3791da3a2e7b84b8366cf6b1247814fe6d6daa | /code/5. CART.R | 8abf6a5011501504f6ce1ea255def9d29f6c1753 | [] | no_license | jodyndaniel/pprbirds-agriculture | 3630267044e6c194c14a66ff0df397c405bcfdef | db67087ebbb4ea2fa15aa2f3137af7f360f4da88 | refs/heads/main | 2023-01-03T10:59:00.183421 | 2020-10-30T21:00:31 | 2020-10-30T21:00:31 | 308,741,594 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,197 | r | 5. CART.R | ##################################################################
##################################################################
library(tree)
library(caret)
library(maptree)
require(rattle)
library(RColorBrewer)
library(diversity)
library(vegan)
library(vegetarian)
library(plotrix)
library(RVAideMemoire)
library(DescTools)
###################################################################
Wet.Covariets.CART.GP <- Wet.Covariets.CART
# give a new clustering number based on wetland affinity
Wet.Covariets.CART.GP$Group[wetncluster.bd.isa[[5]] == 1] <- 5 # 8
Wet.Covariets.CART.GP$Group[wetncluster.bd.isa[[5]] == 2] <- 1 # 7
Wet.Covariets.CART.GP$Group[wetncluster.bd.isa[[5]]== 3] <- 3 # 10
Wet.Covariets.CART.GP$Group[wetncluster.bd.isa[[5]] == 4] <- 4 # 11
Wet.Covariets.CART.GP$Group[wetncluster.bd.isa[[5]] == 5] <- 2 # 12
# after running the CART, we will need to prune the tree (using a function we made)
# selecting where we see a drop in error as the number of nodes we should prune at
WETBirds.CART.Raw <- tree(as.factor(Group) ~ .,
data = Wet.Covariets.CART.GP, split = "deviance")
# examine the error based on the number of nods
Prune.WEB <- PruneTreeDeviance(x=100,y=WETBirds.CART.Raw,z = 64)
plot(rownames(Prune.WEB), Prune.WEB[,1], type="b") # 8 seems optimal
# now we can prune
WETBirds.CART.PR <- prune.tree(WETBirds.CART.Raw, best = 8)
# pull out tree staistics
sum(sapply(resid(WETBirds.CART.PR),function(x)(x-mean(resid(WETBirds.CART.PR)))^2))
sum(sapply(resid(WETBirds.CART.Raw),function(x)(x-mean(resid(WETBirds.CART.Raw)))^2))
# visualize the final tree and save
draw.tree(WETBirds.CART.Raw,size = 3, digits = 3,nodeinfo = TRUE,print.levels=FALSE)
draw.tree(WETBirds.CART.PR,size = 3, digits = 3,nodeinfo = TRUE,print.levels=FALSE)
png("Output/WETBCART.png",width = 7, height = 7,units = 'in', res = 600)
draw.tree(WETBirds.CART.PR,size = 3, digits = 3,nodeinfo = TRUE,print.levels=FALSE)
dev.off()
# Now we need to examine the classification error rates for the tree
# is their a large difference in what the tree predicts and what the data suggests?
WetBirds.CART.CM <- data.frame(Group = as.factor(predict(WETBirds.CART.PR,Wet.Covariets.CART.GP,type="class")))
# a confusion matrix could be helpful
confusionMatrix(WetBirds.CART.CM$Group,as.factor(Wet.Covariets.CART.GP$Group))
WCMatrix <- cbind(Prediction = predict(WETBirds.CART.PR,Wet.Covariets.CART.GP,type="class"),
Group = Wet.Covariets.CART.GP$Group) # kappa is fair, but not great
# a g-test could help in figuring out if the group membership frequency
# differences between the predictions and observations
GTest(WCMatrix, correct = "williams")
# no differences
WETNODE <- data.frame(Node = WETBirds.CART.PR$where,# node membership of each site
Group_Predicted = Wet.Covariets.CART.GP$Group, # assemblage they were predicted to belong to
Group_Observed = WetBirds.CART.CM$Group)
# based on an examination of WETBirds.CART.PR$frame, and WETBirds.CART.PR$where
# we are able to tell which node each site was predicted to
# this, we could use to work back the error rates in each node
# the actual assemblage they belong to
# and site covariates (e.g., Region, Disturbance Class)
# if an assemblage has more than one terminal node, the node on the left is listed as
# A and the one to the right as B - this is based on the visualized tree
WETNODE$Class[WETNODE$Node == 5] = "3A"
WETNODE$Class[WETNODE$Node == 6] = 2
WETNODE$Class[WETNODE$Node == 7] = "4A"
WETNODE$Class[WETNODE$Node == 8] = "4B"
WETNODE$Class[WETNODE$Node == 12] = 1
WETNODE$Class[WETNODE$Node == 13] = "5A"
WETNODE$Class[WETNODE$Node == 14] = "3B"
WETNODE$Class[WETNODE$Node == 15] = "5B"
WETNODEF <- data.frame(WETNODE,
Disturb = LandCover.Polan[WetEmptyISA,"Disturb"],
Region = Wet.Covariets.CART.GP$Region,
Permanence = Wet.Covariets.CART.GP$Permanence )
######################################################################################################################
######################################################################################################################
######################################################################################################################
# Now, we can create a table of site covariates - mean and standard error for each predicted group
CART.Table.WetMean <- matrix(data=NA,nrow=15,ncol=5)
CART.Table.WetError <- matrix(data=NA,nrow=15,ncol=5)
# covariates that are numeric
for (j in 4:13){
red <- aggregate(Wet.Covariets.CART.GP[,j]~ Group, Wet.Covariets.CART.GP, mean)
redb <- aggregate(Wet.Covariets.CART.GP[,j]~ Group, Wet.Covariets.CART.GP, std.error)
CART.Table.WetMean[j,] <- red[,2]
CART.Table.WetError[j,] <- redb[,2]
}
# add rownames
rownames(CART.Table.WetMean) <- colnames(Wet.Covariets.CART.GP)
rownames(CART.Table.WetError) <- colnames(Wet.Covariets.CART.GP)
# categorical covariates
table(WETNODEF$Class,WETNODEF$Region)
table(WETNODEF$Class,WETNODEF$Permanence)
table(WETNODEF$Class,WETNODEF$Disturb)
table(WETNODEF$Class, WETNODEF$Group_Observed)
|
7cd440f0f4f3b92c11ba801ec7f495bb6d7de7ac | 0ef1a314914e88740dbe33b248a552a57c0b261d | /MBQhsi/R/Litfc.R | 145eb0abfe5cf9cb74e8fac04fa66eb887b5e196 | [] | no_license | rocrat/MBQ_Package | 845d25faed797835d916ed646496f26f78254521 | b8c4f978fce36cfd3deb5cb2604372b00bf68e15 | refs/heads/master | 2021-01-21T13:25:37.952739 | 2016-05-17T19:13:40 | 2016-05-17T19:13:40 | 53,088,771 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 742 | r | Litfc.R | #' @title Literature Forb Cover
#' @description Calculates the partial HSI given the forb cover.
#'
#' @param x Percent aerial forb cover measured as in a Daubenmire plot (Daubenmire 1959).
#' @param author The paper from which to calculate the partion HSI, c("king", "simms")
#'
#' @return Returns the relative HSI value
#'
#' @usage FC.Lit(x, author)
#'
#' @export
#' @name FC.Lit
#' @author Dominic LaRoche
#'
FC.Lit <- function(x, author){
if(length(author) > 1) stop("Please choose only one author")
if(!author %in% c("simms", "king")) stop("Please select either 'simms' or 'king'")
if(author == "king"){
s <- dnorm(x, 32.39, 2.55) * 6.391949
}
if(author == "simms"){
s <- dnorm(x, 15, 4) * 10.02651
}
return(s)
}
|
e45744bf52cd23615c28126c4879235e9ba12bdb | 758f48e8724cdbace0de71ddf54104ffb0a21aa3 | /run_analysis.R | 11f0243010fbe5039217553fd1bce9103bd6a00d | [] | no_license | zhangxuecq/wearable-computing | 5450847ce1ea0be4a89d8c6b4826c021fe0a2b5c | 81448187ae0fe9f5a1061bfd9304f575befc5ecb | refs/heads/master | 2021-01-10T01:45:44.917279 | 2015-10-25T16:25:59 | 2015-10-25T16:25:59 | 44,895,529 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,492 | r | run_analysis.R | # Load the data.
X_test <- read.table("UCI HAR Dataset/test/X_test.txt")
y_test <- read.table("UCI HAR Dataset/test/y_test.txt")
subject_test <- read.table("UCI HAR Dataset/test/subject_test.txt")
X_train <- read.table("UCI HAR Dataset/train/X_train.txt")
y_train <- read.table("UCI HAR Dataset/train/y_train.txt")
subject_train <- read.table("UCI HAR Dataset/train/subject_train.txt")
features <- read.table("UCI HAR Dataset/features.txt")
activity_labels <- read.table("UCI HAR Dataset/activity_labels.txt")
# Merges the training and the test sets to create one data set.
test <- cbind(subject_test,y_test,X_test)
train <- cbind(subject_train,y_train,X_train)
data_total <- rbind(train,test)
#Extracts only the measurements on the mean and standard deviation
#for each measurement.
index <- grep("\\bmean()\\b|\\bstd()\\b",features[,2])
data_part <- subset(data_total,select=c(1,2,index+2))
#Uses descriptive activity names to name the activities in the data set
data_part[,2] <- as.factor(activity_labels$V2[data_part[,2]])
#Appropriately labels the data set with descriptive variable names.
name <- c("subject","activity",as.character(features[index,2]))
names(data_part) <- name
#From the data set in step 4, creates a second, independent tidy data
#set with the average of each variable for each activity and each subject.
new_data <- data_part %>% group_by(subject,activity) %>% summarize_each(funs="mean")
write.table(new_data,file="tidy new data set.txt",row.names = FALSE) |
b70263d490ad35e54ee2e22e6b5f88331e8e02a4 | fe8f22495982e2d8769b806ea55fd8235c5a29aa | /man/read_logs.Rd | a0e17063b3edd0b07abfd4d60b3cf9d4a74fcb8e | [] | no_license | drsimonj/adapter | f67e1711cf57d2193b49668a7b38994e4d1e1d15 | 69a0e3330b6d39a1e337bb662e8c602c303370b9 | refs/heads/master | 2021-05-02T16:01:44.816127 | 2017-05-18T03:35:49 | 2017-05-18T03:35:49 | 72,581,326 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,134 | rd | read_logs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reading.R
\name{read_logs}
\alias{read_logs}
\alias{read_session}
\alias{read_events}
\alias{read_stream}
\alias{read_all_streams}
\title{Read user's log files}
\usage{
read_logs(user_dir)
read_session(user_dir, file_name = "session.tsv", col_names = c("var",
"info"), tz = "Australia/Sydney")
read_events(user_dir, file_name = "events.tsv", col_names = c("time",
"event", "detail"))
read_stream(user_dir, file_name, stream_dir = "streams/", is_numeric = TRUE,
is_vec3 = FALSE)
read_all_streams(user_dir, pattern = "tsv$", stream_dir = "streams/",
is_numeric = c("input_brake", "input_horizontal", "input_vertical"),
is_vec3 = c("position", "rotation", "velocity"))
}
\arguments{
\item{user_dir}{Character string defining the user's log-file directory}
\item{file_name}{Character string of the file name. Must include extension such as .tsv}
\item{col_names}{Vector of column names to be used.}
\item{tz}{a character string that specifies which time zone to parse the date with. The string
must be a time zone that is recognized by the user's OS.}
\item{stream_dir}{Character string defining the directory in which stream log
files exist.}
\item{is_numeric}{Indicate whether a stream variable is numeric and,
therefore, should be convereted to numeric. Can be a boolean value
(TRUE/FALSE) or a character vector of variable names to convert if present.}
\item{is_vec3}{Indicate whether a stream variable is a Vector3 value in Unity
and, therefore, should be convereted to a vector of 3 values. Can be a
boolean value (TRUE/FALSE) or a character vector of variable names to
convert if present.}
\item{pattern}{an optional \link{regular expression}. Only file names
which match the regular expression will be returned.}
}
\value{
read_logs with return a list with a "user" S3 class, with three
tibbles (session, events and streams). All others will return a single
\code{\link[tibble]{tibble}}.
}
\description{
These functions help to read a user's session, events, or stream log files.
read_logs() is a wrapper function that uses the default values of all other
functions to read all log files into a single list. If this fails, the log
files can be read separately using the other functions and by adjusting the
variables appropriately.
}
\details{
All files that can be read are assumed to be tab-separated values without
variable headers. Uniquely, they are assumed to:
\describe{
\item{session}{Appear in top-level of user's log file directory; contain
variable names and values}
\item{events}{Appear in top-level of user's log file directory; contain a
timestamp and an event name (tab-separated details about the event can
follow in some cases).}
\item{stream}{Appear in streams/ directory of user's log file directory;
contain a timestamp and a value.}
}
Most functions are helper functions to read in a single file.
read_all_streams, however, reads all the stream files in a directory and
merges them into a single tibble. Also, read_logs will make use of all the
functions to read all log files into a list.
}
|
734377be4451c001af29cada6d0eaa3c54f02b55 | 8a8e37a05bd1810e0c6c46bdf3e63a8ff0a79e86 | /r/SCEUtils/man/metadata_histogram.Rd | 33fa816449d2c25a706c2a8480a6ede3b6ff20a6 | [] | no_license | nathancfox/tools | 3209208c45226988273af4a9a69267a49f9bfcf1 | 3f7db30b380a8fbf569d2016795e42cb853fa15e | refs/heads/master | 2021-12-10T04:45:38.955354 | 2021-09-29T14:19:56 | 2021-09-29T14:19:56 | 240,432,103 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,158 | rd | metadata_histogram.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plots.R
\name{metadata_histogram}
\alias{metadata_histogram}
\title{Plot a histogram of a variable in rowData and colData.}
\usage{
metadata_histogram(
sce_obj,
x,
row_metadata = FALSE,
title = NULL,
x_label = NULL,
bins = NULL,
binwidth = NULL,
mean = TRUE,
median = TRUE
)
}
\arguments{
\item{sce_obj}{The SingleCellExperiment object.}
\item{x}{The name of the variable to plot.}
\item{row_metadata}{TRUE if x is in rowData, FALSE if in colData.}
\item{title}{The title of the plot.}
\item{x_label}{The x-axis label of the plot.}
\item{bins}{The number of bins to use. Cannot be set if binwidth is set.}
\item{binwidth}{The size of each bin. Cannot be set if bins is set.}
\item{mean}{If TRUE, a line will be plotted to indicate the mean of the
distribution.}
\item{median}{If TRUE, a line will be plotted to indicate the median of
the distribution.}
}
\description{
Plots a ggplot histogram of a metadata variable. Can also
annotate with the mean and/or median. Note that the mean and
median legend values will only be accurate to 0 decimal places.
}
|
69d98ef53e649d1db4f554a64057aa7db7b4dfe9 | e914f4ee7b6fab2a992abc04088ffd9cd1df85ed | /Scripts/PNM_Lichens_NoPar.R | 87048e6542d9d3ca2e8a0183d895b94b6e013ccc | [] | no_license | kunstler/MollierChap1PhD | 9abe2c9ce907fb442c12ff8b228f3eafd4ff2cec | 2a361ec49c0b448cfc9ad5469bdb6d4bfdbec671 | refs/heads/master | 2021-04-23T20:40:48.091471 | 2020-09-11T17:45:20 | 2020-09-11T17:45:20 | 249,998,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 109 | r | PNM_Lichens_NoPar.R | # Fit PNM Lichens
source("R/Functions.R")
Fun_Fit_Parc_Group_NoPar(Parc = "PNM", Groupe_Select = "Lichens")
|
18a120a68448332b1735bf4c936d9341cf856f95 | 2a48401dc9fa8307da20f92a005fc2b2f6efe531 | /man/get_status.Rd | 845c6a51c9e32b766aa17aeee1f8475f8f6e94ac | [] | no_license | vitorcapdeville/longRunningAux | 022312b6a50bc8e0182aa85bd71740300b567b6b | 0b5dcc58afe0b98e31eb242c5a3073441aa26df5 | refs/heads/master | 2021-01-01T07:42:18.775110 | 2020-02-08T17:54:17 | 2020-02-08T17:54:17 | 239,177,238 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 498 | rd | get_status.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/long_running_aux.R
\name{get_status}
\alias{get_status}
\title{Função que pega o status atual da tarefa.}
\usage{
get_status(status_file)
}
\arguments{
\item{status_file}{Um arquivo temporário onde está registrado o status da tarefa. Usualmente criado com o comando `status_file = tempfile()`.}
}
\value{
O status atual assim como registrado no arquivo.
}
\description{
Função que pega o status atual da tarefa.
}
|
b0adc7a212fa0ceb2ed5a6ee600f5761de7597fa | 439933a3fb21a29240ab4b04aebaced0569248be | /_R code for processing raw data/Make Rec effort files Spr-Sum.R | d0082c2c8f87ed883e0fb3dc3f00e94bed2eed09 | [] | no_license | nwfsc-cb/spring-chinook-distribution | e47b5e39f5ce2ab8f20413085bc13249ef3bec37 | 5bff26b6fe5102a16a9c3f2c13d659b7e831e03e | refs/heads/master | 2023-08-08T03:35:36.302066 | 2023-08-01T16:35:04 | 2023-08-01T16:35:04 | 128,123,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,225 | r | Make Rec effort files Spr-Sum.R | ### SCRIPT FOR CREATING FILES THAT CAN BE READ IN BY STAN
# Rec Effort data first
#print(base.dir)
ak.eff <- read.csv("./Processed Data/Effort Data/effort.data.ak.2022-09.csv") # THIS IS ONLY COMMERCIAL TROLL (OMIT)
ca.or.wa.eff <- read.csv("./Processed Data/Effort Data/effort.data.REC.ca.or.wa.csv")
puso.eff <- read.csv("./Processed Data/Effort Data/effort.data.REC.puso-to-2020-FIN.csv")
puso.retention <- read.csv("./Processed Data/Effort Data/WA PUSO Chinook retention.csv")
sgeo.eff <- read.csv("./Processed Data/Effort Data/effort.data.REC.sgeo.csv")
johnstone.eff <- read.csv("./Processed Data/Effort Data/effort.data.REC.johnstone.csv")
wcvi.eff <- read.csv("./Processed Data/Effort Data/effort.data.REC.wcvi.csv")
bc.eff <- read.csv("./Processed Data/Effort Data/effort.data.REC.bc.2019.csv")
can.irec.eff <- read.csv("./Processed Data/Effort Data/iREC chinook effort 05-2019.csv")
can.irec.mapping <- read.csv("./Processed Data/Effort Data/can.irec.areas.mapping.csv")
colnames(ca.or.wa.eff)[3:14] <- colnames(ak.eff)[3:14]
locations <- read.csv("./Processed Data/Locations-map to coastal areas 1-2019.csv")
##### These are the month groupings to start:
# Need to make separate matrices for power and hand trolling
ALL.MONTH <- c("month.01","month.02","month.03","month.04","month.05","month.06","month.07","month.08","month.09","month.10","month.11","month.12")
### At present we lack rec effort for 1978-1995 for both BC (except SGEO) and Alaska.
### Therefore, we will use three files for rec effort:
### One for the south in which we will use the recreational effort data.
### One for SGEO where we have effort data (post 1982) but it units of boat trips, not angler trips.
### One for the north where we will not.
##############################################################################################################
#######################################################################################################################################
#######################################################################################################################################
#######################################################################################################################################
#if(MONTH.STRUCTURE == "FOUR"){
# Seasonal Blocks
##############################################################
# AK Effort
# ak.eff$area.code <- ak.eff$SEAK.region
# ak.eff.by.area <- aggregate(ak.eff[,ALL.MONTH],by=list(year=ak.eff$year,area.code=ak.eff$area.code),sum)
#
# ak.eff.by.area$month.winter <- rowSums(ak.eff.by.area[,WINTER.MONTH])
# ak.eff.by.area$month.spring <- rowSums(ak.eff.by.area[,SPRING.MONTH])
# ak.eff.by.area$month.summer <- rowSums(ak.eff.by.area[,SUMMER.MONTH])
# ak.eff.by.area$month.fall <- rowSums(ak.eff.by.area[,FALL.MONTH])
#
# ak.eff.rec <- ak.eff.by.area[,c("year","area.code",MONTH)]
# US Coast
if(loc_18=="TWO_OR" | loc_18=="_two_OR_PUSO_AK"){
ca.or.wa.eff$area.code <- locations$area.code.two.OR[match(ca.or.wa.eff$port,locations$stat.area.port)]
}else if(loc_18=="NCA_SOR_PUSO"){
ca.or.wa.eff$area.code <- locations$area.code.NCA_SOR_PUSO[match(ca.or.wa.eff$port,locations$stat.area.port)]
}else{
ca.or.wa.eff$area.code <- locations$area.code[match(ca.or.wa.eff$port,locations$stat.area.port)]
}
### DIVIDE WESTPORT EFFORT EQUALLY BETWEEN WAC and COL
ca.or.wa.eff[ca.or.wa.eff$port=="Westport",][,grep("month",colnames(ca.or.wa.eff))] <- ca.or.wa.eff[ca.or.wa.eff$port=="Westport",][,grep("month",colnames(ca.or.wa.eff))] /2
temp <- ca.or.wa.eff[ca.or.wa.eff$port=="Westport",]
temp$area.code <- "COL"
ca.or.wa.eff <- rbind(ca.or.wa.eff,temp)
###
ca.or.wa.eff.by.area <- aggregate(ca.or.wa.eff[,ALL.MONTH],by=list(year=ca.or.wa.eff$year,area.code=ca.or.wa.eff$area.code),sum)
if(MONTH.STRUCTURE=="FOUR"){
ca.or.wa.eff.by.area$month.winter.2 <- rowSums(ca.or.wa.eff.by.area[,WINTER.MONTH[1:3]])
ca.or.wa.eff.by.area$month.winter.1 <- rowSums(ca.or.wa.eff.by.area[,WINTER.MONTH[4:5]])
ca.or.wa.eff.by.area$month.spring <- rowSums(ca.or.wa.eff.by.area[,SPRING.MONTH])
ca.or.wa.eff.by.area$month.summer <- rowSums(ca.or.wa.eff.by.area[,SUMMER.MONTH])
ca.or.wa.eff.by.area$month.fall <- rowSums(ca.or.wa.eff.by.area[,FALL.MONTH])
}
if(MONTH.STRUCTURE=="SPRING"){
ca.or.wa.eff.by.area$month.winter.2 <- rowSums(ca.or.wa.eff.by.area[,WINTER.MONTH[1:2]])
ca.or.wa.eff.by.area$month.winter.1 <- rowSums(ca.or.wa.eff.by.area[,WINTER.MONTH[3:4]])
ca.or.wa.eff.by.area$month.spring <- rowSums(ca.or.wa.eff.by.area[,SPRING.MONTH])
ca.or.wa.eff.by.area$month.summer <- rowSums(ca.or.wa.eff.by.area[,SUMMER.MONTH])
ca.or.wa.eff.by.area$month.fall <- rowSums(ca.or.wa.eff.by.area[,FALL.MONTH])
}
if(MONTH.STRUCTURE=="FRAM"){
ca.or.wa.eff.by.area$month.winter.2 <- (ca.or.wa.eff.by.area[,WINTER.MONTH[1]])
ca.or.wa.eff.by.area$month.winter.1 <- rowSums(ca.or.wa.eff.by.area[,WINTER.MONTH[2:4]])
ca.or.wa.eff.by.area$month.spring <- rowSums(ca.or.wa.eff.by.area[,SPRING.MONTH])
ca.or.wa.eff.by.area$month.summer <- rowSums(ca.or.wa.eff.by.area[,SUMMER.MONTH])
ca.or.wa.eff.by.area$month.fall <- rowSums(ca.or.wa.eff.by.area[,FALL.MONTH])
}
ca.or.wa.eff.by.area$year.wint.2 <- ca.or.wa.eff.by.area$year-1
temp <- ca.or.wa.eff.by.area[,c("year.wint.2", "area.code","month.winter.2") ]
ca.or.wa.eff.by.area <- ca.or.wa.eff.by.area %>% dplyr::select(-year.wint.2,-month.winter.2)
ca.or.wa.eff.by.area <- merge(ca.or.wa.eff.by.area,temp,by.x=c("year","area.code"),by.y=c("year.wint.2" ,"area.code")) # added all=T to avoid cutting off a year of data and maintaining consistency with other scripts
ca.or.wa.eff.by.area$month.winter <- ca.or.wa.eff.by.area$month.winter.2 + ca.or.wa.eff.by.area$month.winter.1
ca.or.wa.eff.rec <- ca.or.wa.eff.by.area[,c("year","area.code",MONTH)]
#################################################################################
# Puget Sound
#################################################################################
#Adjust later years for chinook non-retention.
#### make mapping between month number and number of days.
M <- 1:12
D <- c(31,28,31,30,31,30,31,31,30,31,30,31)
REF <- data.frame(M,D)
#Come up with a mapping between month and fraction of month that Chinook retention was possible for PUSO
puso.ret.wide <- expand.grid(month=1:12,year=unique(puso.retention$Year))
A <-matrix(0,nrow(puso.ret.wide),length(grep("Area",colnames(puso.eff))))
colnames(A) <- colnames(puso.eff)[grep("Area",colnames(puso.eff))]
puso.ret.wide <- data.frame(cbind(puso.ret.wide,A))
ALL <- NULL
for(i in 1:nrow(puso.retention)){
area <- puso.retention$Area[i]
COL <- grep(area,colnames(puso.ret.wide))
M <- puso.retention$month.open[i] : puso.retention$month.close[i]
M.frac <- NULL
if(length(M) == 1 ){
M.frac[1] <- (puso.retention$day.close[i] - puso.retention$day.open[i] + 1) / REF$D[REF$M==M[1]]
}
if(length(M) > 1 ){
M.frac[1] <- (REF$D[REF$M==M[1]] - puso.retention$day.open[i] + 1) / REF$D[REF$M==M[1]]
if(length(M)>2){M.frac[2:(length(M)-1)] <- 1}
M.frac[length(M)] <- (puso.retention$day.close[i]) / REF$D[REF$M==M[length(M)]]
}
M.frac[M.frac > 1] <- 1
B <- puso.ret.wide %>% filter(year == puso.retention$Year[i],month >= M[1], month <= M[length(M)])
B[,COL] <- M.frac
ALL <- rbind(ALL,B)
}
ALL <- as.data.frame(ALL)
puso.ret.wide <- ALL %>% group_by(month,year) %>% summarise(Area.5=sum(Area.5),Area.6=sum(Area.6),Area.7=sum(Area.7),Area.8=sum(Area.8),
Area.8.1=sum(Area.8.1),Area.8.2=sum(Area.8.2),Area.9=sum(Area.9),
Area.10=sum(Area.10),Area.11=sum(Area.11),Area.12=sum(Area.12),Area.13=sum(Area.13)) %>%
as.data.frame()
puso.ret.wide[ ,3:ncol(puso.ret.wide)][puso.ret.wide[ ,3:ncol(puso.ret.wide)] >1 ] <- 1
#################
## Deal with the effort data.
#################
puso.eff[,4:14] <- puso.eff[,4:14] * puso.eff$adjust
#3 Combine times of chinook retention
puso.eff <- puso.eff %>% group_by(Year,Month) %>%
summarise(Area.5=sum(Area.5),Area.6=sum(Area.6),Area.7=sum(Area.7),Area.8=sum(Area.8),
Area.8.1=sum(Area.8.1),Area.8.2=sum(Area.8.2),Area.9=sum(Area.9),
Area.10=sum(Area.10),Area.11=sum(Area.11),Area.12=sum(Area.12),Area.13=sum(Area.13)) %>%
as.data.frame()
puso.ret.wide <- merge(puso.eff[,c("Year","Month")],puso.ret.wide,by.x=c("Year","Month"),by.y=c("year","month"),all=T)
puso.ret.wide[is.na(puso.ret.wide)==T] <- 1
puso.eff <- merge(puso.ret.wide[,c("Year","Month")],puso.eff,by=c("Year","Month"),all=T)
puso.eff[,3:ncol(puso.eff)] <- puso.eff[,3:ncol(puso.eff)] * puso.ret.wide[,3:ncol(puso.eff)]
#### DROP AREA 5 from the EFFORT STATISTICS IF APPROPRIATE
# puso.eff.area5 <- puso.eff %>% dplyr::select(Year,Month,Area.5)
# puso.eff <- puso.eff %>% dplyr::select(-Area.5)
#
# puso.eff.area5$total.effort <- puso.eff.area5$Area.5
puso.eff<- puso.eff %>% mutate(total.effort.out = Area.5+Area.6+Area.7,
total.effort.in=Area.8+Area.8.1+Area.8.2+Area.9+Area.10+Area.11+Area.12+Area.13,
total.effort = total.effort.in + total.effort.out)
# temp <- aggregate(puso.eff$total.effort,by=list(year=puso.eff$Year,month=puso.eff$Month),sum)
# temp <- temp[order(temp$year,temp$month),]
puso.eff.wide <- data.frame(port="All Puget Sound",year=YEARS.RECOVER)
puso.eff.wide <- cbind(puso.eff.wide,matrix(0,length(YEARS.RECOVER),12))
# This section is for the original 17 area model
puso.eff.wide <- pivot_wider(puso.eff %>% dplyr::select(Year,Month,total.effort),
id_cols = "Year",names_from = "Month",values_from = "total.effort")
puso.eff.wide <- data.frame(port="All Puget Sound",puso.eff.wide)
colnames(puso.eff.wide)[3:14] <- ALL.MONTH
puso.eff.wide$effort.type <- "angler.trip"
puso.eff.wide$Notes <- NA
puso.eff.wide$area.code <- "PUSO"
if(loc_18 == "TRUE" | loc_18 == "TWO_OR" | loc_18 =="NCA_SOR_PUSO" | loc_18 =="_two_OR_PUSO_AK"){ # This section is for the 18 area model
puso.eff.in.wide <- pivot_wider(puso.eff %>% dplyr::select(Year,Month,total.effort.in),
id_cols = "Year",names_from = "Month",values_from = "total.effort.in")
puso.eff.in.wide <- data.frame(port="All Puget Sound",puso.eff.in.wide)
colnames(puso.eff.in.wide)[3:14] <- ALL.MONTH
puso.eff.in.wide$area.code <- "PUSO"
puso.eff.out.wide <- pivot_wider(puso.eff %>% dplyr::select(Year,Month,total.effort.out),
id_cols = "Year",names_from = "Month",values_from = "total.effort.out")
puso.eff.out.wide <- data.frame(port="All Puget Sound",puso.eff.out.wide)
colnames(puso.eff.out.wide)[3:14] <- ALL.MONTH
puso.eff.out.wide$area.code <- "PUSO_out"
puso.eff.wide <- rbind(puso.eff.in.wide, puso.eff.out.wide)
}
# This section is shared for all models
if(MONTH.STRUCTURE=="FOUR"){
puso.eff.wide$month.winter.2 <- rowSums(puso.eff.wide[,WINTER.MONTH[1:3]])
puso.eff.wide$month.winter.1 <- rowSums(puso.eff.wide[,WINTER.MONTH[4:5]])
puso.eff.wide$month.spring <- rowSums(puso.eff.wide[,SPRING.MONTH])
puso.eff.wide$month.summer <- rowSums(puso.eff.wide[,SUMMER.MONTH])
puso.eff.wide$month.fall <- rowSums(puso.eff.wide[,FALL.MONTH])
}
if(MONTH.STRUCTURE=="SPRING"){
puso.eff.wide$month.winter.2 <- rowSums(puso.eff.wide[,WINTER.MONTH[1:2]])
puso.eff.wide$month.winter.1 <- rowSums(puso.eff.wide[,WINTER.MONTH[3:4]])
puso.eff.wide$month.spring <- rowSums(puso.eff.wide[,SPRING.MONTH])
puso.eff.wide$month.summer <- rowSums(puso.eff.wide[,SUMMER.MONTH])
puso.eff.wide$month.fall <- rowSums(puso.eff.wide[,FALL.MONTH])
}
if(MONTH.STRUCTURE=="FRAM"){
puso.eff.wide$month.winter.2 <- (puso.eff.wide[,WINTER.MONTH[1]])
puso.eff.wide$month.winter.1 <- rowSums(puso.eff.wide[,WINTER.MONTH[2:4]])
puso.eff.wide$month.spring <- rowSums(puso.eff.wide[,SPRING.MONTH])
puso.eff.wide$month.summer <- rowSums(puso.eff.wide[,SUMMER.MONTH])
puso.eff.wide$month.fall <- rowSums(puso.eff.wide[,FALL.MONTH])
}
puso.eff.wide$year.wint.2 <- puso.eff.wide$Year-1
temp <- puso.eff.wide[,c("year.wint.2", "area.code","month.winter.2") ]
puso.eff.wide <- puso.eff.wide %>% dplyr::select(-year.wint.2,-month.winter.2)
puso.eff.wide <- merge(puso.eff.wide,temp,by.x=c("Year","area.code"),by.y=c("year.wint.2" ,"area.code"),all=T)
puso.eff.wide$month.winter <- puso.eff.wide$month.winter.2 + puso.eff.wide$month.winter.1
puso.eff.rec <- puso.eff.wide[,c("Year","area.code",MONTH)]
puso.eff.rec <- puso.eff.rec %>% dplyr::rename(year=Year)
##3 puso.eff.area5.wide
#
# puso.eff.area5.wide <- data.frame(port="WAC",year=YEARS.RECOVER)
# puso.eff.area5.wide <- cbind(puso.eff.area5.wide,matrix(0,length(YEARS.RECOVER),12))
#
# puso.eff.area5.wide <- dcast(puso.eff.area5[,c("Year","Month","total.effort")],Year~Month)
# puso.eff.area5.wide <- data.frame(port="All Puget Sound",puso.eff.area5.wide)
# colnames(puso.eff.area5.wide)[3:14] <- ALL.MONTH
#
# puso.eff.area5.wide$effort.type <- "angler.trip"
# puso.eff.area5.wide$Notes <- NA
# puso.eff.area5.wide$area.code <- "WAC"
#
# puso.eff.area5.wide$month.winter.2 <- rowSums(puso.eff.area5.wide[,WINTER.MONTH[4:5]])
# puso.eff.area5.wide$month.winter.1 <- rowSums(puso.eff.area5.wide[,WINTER.MONTH[1:3]])
# puso.eff.area5.wide$month.spring <- rowSums(puso.eff.area5.wide[,SPRING.MONTH])
# puso.eff.area5.wide$month.summer <- rowSums(puso.eff.area5.wide[,SUMMER.MONTH])
# puso.eff.area5.wide$month.fall <- rowSums(puso.eff.area5.wide[,FALL.MONTH])
#
# puso.eff.area5.wide$year.wint.2 <- puso.eff.area5.wide$Year+1
#
# temp <- puso.eff.area5.wide[,c("year.wint.2", "area.code","month.winter.2") ]
# puso.eff.area5.wide <- puso.eff.area5.wide %>% dplyr::select(-year.wint.2,-month.winter.2)
# puso.eff.area5.wide <- merge(puso.eff.area5.wide,temp,by.x=c("Year","area.code"),by.y=c("year.wint.2" ,"area.code"),all=T)
# puso.eff.area5.wide$month.winter <- puso.eff.area5.wide$month.winter.2 + puso.eff.area5.wide$month.winter.1
#
# puso.eff.area5.rec <- puso.eff.area5.wide[,c("Year","area.code",MONTH)]
# puso.eff.area5.rec <- puso.eff.area5.rec %>% dplyr::rename(year=Year)
#
# puso.eff.by.area <- aggregate(puso.eff.wide[,ALL.MONTH],
# by=list(year=puso.eff.wide$year,area.code=puso.eff.wide$area.code),sum)
#update WAC effort with puso.eff.area5 effort
# temp.WAC <- ca.or.wa.eff.rec %>% filter(area.code == "WAC")%>% filter(year %in% YEARS.RECOVER) %>% arrange(year)
# temp.puso.eff.area5 <- puso.eff.area5.rec %>% filter(year %in% YEARS.RECOVER) %>% arrange(year)
#
# if(nrow(temp.WAC)==nrow(temp.puso.eff.area5)){
# temp.WAC.rec <- cbind(temp.WAC[,1:2],temp.WAC[,3:ncol(temp.WAC)] + temp.puso.eff.area5[,3:ncol(temp.puso.eff.area5)]) %>% as.data.frame()
# }else{
# print(rep("STOP",3))
# }
#
# ca.or.wa.eff.rec <- ca.or.wa.eff.rec %>% filter(area.code != "WAC")
# ca.or.wa.eff.rec <- rbind(ca.or.wa.eff.rec,temp.WAC.rec) %>% arrange(year) %>% as.data.frame()
#################################################################################
### SGEO - NOTE THE JOHNSTONE DATA IS EXCLUDED FROM THIS BECAUSE WE DON"T HAVE EARLY DATA FROM JOHNSTONE
#################################################################################
bc.trim <- bc.eff %>% filter(DISPOSITION == "Effort")
john.areas <- c("PFMA 11","PFMA 111","PFMA 12")
sgeo.areas <- c("PFMA 13","PFMA 14","PFMA 15","PFMA 16","PFMA 17",
"PFMA 18","PFMA 19","PFMA 20","PFMA 28","PFMA 29")
swvi.areas <- c("PFMA 21","PFMA 22","PFMA 23","PFMA 24","PFMA 121","PFMA 123","PFMA 124")
nwvi.areas <- c("PFMA 25","PFMA 26","PFMA 27","PFMA 125","PFMA 126","PFMA 127")
bc.trim <- bc.trim %>% mutate(area.code = "", area.code=ifelse(PFMA %in% john.areas,"CBC",area.code)) %>%
mutate(area.code=ifelse(PFMA %in% sgeo.areas,"SGEO",area.code)) %>%
mutate(area.code=ifelse(PFMA %in% swvi.areas,"SWVI",area.code)) %>%
mutate(area.code=ifelse(PFMA %in% nwvi.areas,"NWVI",area.code))
bc.trim <- bc.trim %>% group_by(year=YEAR,month.numb,area.code) %>% dplyr::summarise(total.effort=sum(Estimate))
if(MONTH.STRUCTURE=="FOUR"){
bc.trim <- bc.trim %>% mutate(season="", season = ifelse(month.numb<=3, "month.winter.2",""),
season = ifelse(month.numb>=11 & month.numb<=12, "month.winter.1",season),
season = ifelse(month.numb>=4 & month.numb<=5, "month.spring",season),
season = ifelse(month.numb>=6 & month.numb<=7, "month.summer",season),
season = ifelse(month.numb>=8 & month.numb<=10, "month.fall",season))
}
if(MONTH.STRUCTURE=="SPRING"){
bc.trim <- bc.trim %>% mutate(season="", season = ifelse(month.numb<=2, "month.winter.2",""),
season = ifelse(month.numb>=11 & month.numb<=12, "month.winter.1",season),
season = ifelse(month.numb>=3 & month.numb<=5, "month.spring",season),
season = ifelse(month.numb>=6 & month.numb<=7, "month.summer",season),
season = ifelse(month.numb>=8 & month.numb<=10, "month.fall",season))
}
if(MONTH.STRUCTURE=="FRAM"){
bc.trim <- bc.trim %>% mutate(season="",season = ifelse(month.numb<=1, "month.winter.2",""),
season = ifelse(month.numb>=10 & month.numb<=12, "month.winter.1",season),
season = ifelse(month.numb>=4 & month.numb<=5, "month.spring",season),
season = ifelse(month.numb>=6 & month.numb<=7, "month.summer",season),
season = ifelse(month.numb>=8 & month.numb<=10, "month.fall",season))
}
bc.trim <- bc.trim %>% mutate(year.mod =year,year.mod=ifelse(season=="month.winter.2",year.mod-1,year.mod),
season=ifelse(season=="month.winter.1","month.winter",season),
season=ifelse(season=="month.winter.2","month.winter",season))
bc.mod <- bc.trim %>% group_by(year,season,area.code) %>% dplyr::summarise(total.eff = sum(total.effort))
bc.eff.rec <- full_join(expand.grid(area.code=c("CBC","SGEO","NWVI","SWVI"),year=YEARS.RECOVER),
pivot_wider(bc.mod,id_cols = c("year","area.code"),
names_from = "season",
values_from = "total.eff"))
bc.eff.rec <- bc.eff.rec %>% dplyr::select(year,area.code,month.winter,month.spring,month.summer,month.fall)
bc.eff.rec[is.na(bc.eff.rec)==T] <- 0
### iREC DATA
### Process the iREC data from Canada. This is a different data type and form than the other recreational data.
### it needs additional processing.
# extract only effort information.
can.irec.mod <- can.irec.eff %>% filter(ITEM_GROUP=="EFFORT") %>% dplyr::select(YEAR,MONTH,AREA,ITEM,ESTIMATE)
# Combine adult and juvenile effort
can.irec.mod <- can.irec.mod %>% group_by(YEAR,MONTH,AREA) %>% summarize(effort = sum(ESTIMATE))
can.irec.mod <- left_join(can.irec.mod,can.irec.mapping) %>% filter(!REGION=="RIVER") %>%
group_by(YEAR,MONTH,REGION) %>% summarize(tot.effort = sum(effort))
if(MONTH.STRUCTURE=="FOUR"){
can.irec.mod<- can.irec.mod %>% mutate(season = ifelse(MONTH<=3, "month.winter.2",""),
season = ifelse(MONTH>=11 & MONTH<=12, "month.winter.1",season),
season = ifelse(MONTH>=4 & MONTH<=5, "month.spring",season),
season = ifelse(MONTH>=6 & MONTH<=7, "month.summer",season),
season = ifelse(MONTH>=8 & MONTH<=10, "month.fall",season))
}
if(MONTH.STRUCTURE=="SPRING"){
can.irec.mod<- can.irec.mod %>% mutate(season = ifelse(MONTH<=2, "month.winter.2",""),
season = ifelse(MONTH>=11 & MONTH<=12, "month.winter.1",season),
season = ifelse(MONTH>=3 & MONTH<=5, "month.spring",season),
season = ifelse(MONTH>=6 & MONTH<=7, "month.summer",season),
season = ifelse(MONTH>=8 & MONTH<=10, "month.fall",season))
}
if(MONTH.STRUCTURE=="FRAM"){
can.irec.mod<- can.irec.mod %>% mutate(season = ifelse(MONTH<=1, "month.winter.2",""),
season = ifelse(MONTH>=10 & MONTH<=12, "month.winter.1",season),
season = ifelse(MONTH>=4 & MONTH<=5, "month.spring",season),
season = ifelse(MONTH>=6 & MONTH<=7, "month.summer",season),
season = ifelse(MONTH>=8 & MONTH<=10, "month.fall",season))
}
can.irec.mod <- can.irec.mod %>% mutate(year.mod =YEAR,year.mod=ifelse(season=="month.winter.2",year.mod-1,year.mod),
season=ifelse(season=="month.winter.1","month.winter",season),
season=ifelse(season=="month.winter.2","month.winter",season))
can.irec.mod <- can.irec.mod %>% rename(area.code=REGION)
can.irec.mod <- can.irec.mod %>% group_by(year.mod,area.code,season) %>% summarize(effort=sum(tot.effort))
can.irec.mod <- pivot_wider(can.irec.mod,id_cols = c("year.mod","area.code"),
names_from = "season",
values_from = "effort")
# dcast(can.irec.mod,year.mod+area.code~season,value.var="effort",sum)
can.irec.eff.fin <- left_join(data.frame(expand.grid(year.mod=YEARS.RECOVER,area.code=LOCATIONS$location.name)),can.irec.mod)
can.irec.eff.fin <- can.irec.eff.fin %>% rename(year=year.mod)
can.irec.eff.fin <- can.irec.eff.fin[,c("year","area.code",MONTH)]
can.irec.eff.fin[is.na(can.irec.eff.fin)==T] <- 0
effort.can.irec <- can.irec.eff.fin
###################################################################################
###################################################################################
# Combine the files and trim to match the Years span specified by the Master File
#effort <- rbind(ca.or.wa.eff.rec,puso.eff.rec)
effort <- rbind(ca.or.wa.eff.rec,puso.eff.rec,bc.eff.rec)
#effort <- rbind(ca.or.wa.eff.rec,puso.eff.rec,sgeo.eff.rec,ak.eff.rec)
temp<- expand.grid(year=YEARS.RECOVER,area.code=LOCATIONS$location.name)
effort <- merge(effort,data.frame(year=YEARS.RECOVER))
effort <- merge(effort,temp,all=T)
effort$area.numb <- LOCATIONS$location.number[match(effort$area.code,LOCATIONS$location.name)]
effort <- effort[order(effort$area.numb,effort$year),]
effort[is.na(effort == T)]<- 0
effort.can.irec$area.numb <- LOCATIONS$location.number[match(effort.can.irec$area.code,LOCATIONS$location.name)]
effort.rec <- effort
|
a729fc86b928c4260e19e494422c058b0046c958 | f17524c4609ca21b3bf05b17e2670031ebe2f136 | /Vegetation Change/VegChangeAnalyses_Final.R | f0001430c6d3dcc61cded098e008ab79f92c15b0 | [] | no_license | cliffbueno/Manuscripts | 98edb62d9ccd70b98c8d31f4c9d6c0d4f8c8b348 | 27a11135599bab6c630132a6af87b134d01f1a7c | refs/heads/master | 2023-04-11T05:14:39.090989 | 2023-03-22T00:46:59 | 2023-03-22T00:46:59 | 153,540,391 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,917 | r | VegChangeAnalyses_Final.R | # Analysis of remote sensing data, ground truth data, and summer climate data, Niwot Ridge Colorado
# By Cliff Bueno de Mesquita Fall 2015 - Spring 2018
# Original remote sensing by Luke Tillmann 2014
# Ground truthing by Cliff Bueno de Mesquita, Connor Bernard, Katherine Rosemond
# Now published in Arctic, Antarctic, and Alpine Research
################################### Setup ##################################################
library(leaps)
library(bestglm)
library(AICcmodavg)
library(corrplot)
library(randomForest)
library(miscTools)
library(ggplot2)
library(MASS)
library(spData)
library(sp)
library(spgwr)
library(boot)
library(modEvA)
library(VSURF)
library(maptools)
library(robustbase)
library(Rcpp)
library(spdep)
library(Matrix)
library(GWmodel)
library(TTR)
library(quantmod)
library(tseries)
library(fracdiff)
library(timeDate)
library(forecast)
library(ggplot2)
library(nlme)
source("~/Desktop/Functions/logisticPseudoR2s.R")
setwd("~/Desktop/CU/2Research/VegChange")
vc <- read.csv("VegChangeAll.csv")
# What variables are too correlated with elevation? None!
plot(vc$Elevation, vc$Aspect)
plot(vc$Elevation, vc$Slope)
plot(vc$Elevation, vc$Mean)
plot(vc$Elevation, vc$CV)
plot(vc$Elevation, vc$Trend)
plot(vc$Elevation, vc$Solar)
ggplot(vc, aes(Elevation, Mean)) +
geom_smooth(method = loess)
# Make continuous cover variable
cover <- read.csv("Elev_Cover.csv")
tcover <- cover[1:5,]
treemodel <- lm(tcover$Tree~poly(tcover$Elevation,3,raw=TRUE))
summary(treemodel)
scover <- cover[1:7,]
shrubmodel <- lm(scover$Shrub~poly(scover$Elevation,4,raw=TRUE))
summary(shrubmodel)
tundramodel <- lm(cover$Tundra~poly(cover$Elevation,4,raw=TRUE))
summary(tundramodel)
third_order <- function(newdist, model) {
coefs <- coef(model)
res <- coefs[1] + (coefs[2] * newdist) + (coefs[3] * newdist^2) +
(coefs[4] * newdist^3)
return(res)
}
fourth_order <- function(newdist, model) {
coefs <- coef(model)
res <- coefs[1] + (coefs[2] * newdist) + (coefs[3] * newdist^2) +
(coefs[4] * newdist^3) + (coefs[5] * newdist^4)
return(res)
}
vc$Tree_Cover <- third_order(vc$Elevation, treemodel)
for (i in 1:1532) {
if (vc$Tree_Cover[i] < 0) {
vc$Tree_Cover[i] <- 0
}
if (vc$Elevation[i] > 3472) {
vc$Tree_Cover[i] <- 0
}
}
vc$Shrub_Cover <- fourth_order(vc$Elevation, shrubmodel)
for (i in 1:1532) {
if (vc$Shrub_Cover[i] < 0) {
vc$Shrub_Cover[i] <- 0
}
if (vc$Elevation[i] > 3685) {
vc$Shrub_Cover[i] <- 0
}
}
vc$Tundra_Cover <- fourth_order(vc$Elevation, tundramodel)
for (i in 1:1532) {
if (vc$Tundra_Cover[i] < 0) {
vc$Tundra_Cover[i] <- 0
}
}
plot(vc$Elevation, vc$Tree_Cover)
plot(vc$Elevation, vc$Shrub_Cover)
plot(vc$Elevation, vc$Tundra_Cover)
# Supplementary Figure A1
ggplot(vc, aes(x = Elevation, y = Tree_Cover)) +
geom_line(aes(x=Elevation,y=Tree_Cover,colour="darkgreen")) +
geom_line(aes(x=Elevation,y=Shrub_Cover,colour="lightgreen")) +
geom_line(aes(x=Elevation,y=Tundra_Cover,colour="orange")) +
ylim(0,100) +
xlab("Elevation (m)") +
ylab("Percent Cover") +
scale_colour_manual(name = "Veg. Type",
values = c("darkgreen","lightgreen","orange"),
labels = c("Tree","Shrub","Tundra")) +
theme_bw() +
theme(axis.text.x = element_text(size = 14),
axis.title.x = element_text(size = 16, face = "bold"),
axis.text.y = element_text(size = 14),
axis.title.y = element_text(size = 16, face = "bold"))
# covers <- as.data.frame(cbind(vc$X, vc$Y, vc$Tree_Cover, vc$Shrub_Cover, vc$Tundra_Cover))
# write.csv(covers, file = "Covers.csv")
# Subsets for tundra, shrub, open forest, absent in 1972
ta <- subset(vc, Cover_1972 != "T")
sa <- subset(vc, Cover_1972 != "S")
oa <- subset(vc, Cover_1972 != "O")
loa <- subset(oa, Elevation < 3600)
lsa <- subset(sa, Elevation < 3760)
hta <- subset(ta, Elevation > 3550)
############################ Elevational Range #############################################
# Test for change in max and min, and in 95th percentile for each
# All Forest
f72 <- subset(vc, Cover_1972 == "O" | Cover_1972 == "C")
f08 <- subset(vc, Cover_2008 == "O" | Cover_2008 == "C")
min(f72$Elevation)
max(f72$Elevation)
quantile(f72$Elevation, c(0.05, 0.95))
min(f08$Elevation)
max(f08$Elevation)
quantile(f08$Elevation, c(0.05, 0.95))
# Closed Canopy Forest
cf72 <- subset(vc, Cover_1972 == "C")
cf08 <- subset(vc, Cover_2008 == "C")
min(cf72$Elevation)
max(cf72$Elevation)
quantile(cf72$Elevation, c(0.05, 0.95))
min(cf08$Elevation)
max(cf08$Elevation)
quantile(cf08$Elevation, c(0.05, 0.95))
# Open Forest
of72 <- subset(vc, Cover_1972 == "O")
of08 <- subset(vc, Cover_2008 == "O")
min(of72$Elevation)
max(of72$Elevation)
quantile(of72$Elevation, c(0.05, 0.95))
min(of08$Elevation)
max(of08$Elevation)
quantile(of08$Elevation, c(0.05, 0.95))
# Shrub
s72 <- subset(vc, Cover_1972 == "S")
s08 <- subset(vc, Cover_2008 == "S")
min(s72$Elevation)
max(s72$Elevation)
quantile(s72$Elevation, c(0.05, 0.95))
min(s08$Elevation)
max(s08$Elevation)
quantile(s08$Elevation, c(0.05, 0.95))
# Tundra
t72 <- subset(vc, Cover_1972 == "T")
t08 <- subset(vc, Cover_2008 == "T")
min(t72$Elevation)
max(t72$Elevation)
quantile(t72$Elevation, c(0.05, 0.95))
min(t08$Elevation)
max(t08$Elevation)
quantile(t08$Elevation, c(0.05, 0.95))
################################### Best GLMs #############################################
# To test for best combo of fine-scale predictor variables of change
# First look at correlations
env <- vc[,c(3:8,27:29)]
M <- cor(env)
corrplot(M, method = "number", type = "lower")
# Tree below 3600m
X <- as.data.frame(scale(loa[,c(3:8,27)]))
y <- loa$Oexpand
Xy <- as.data.frame(cbind(X,y))
bestLOE <- bestglm(Xy, IC = "AIC", family = binomial)
bestLOE # Cover, Elevation, Solar
bestLOE$BestModels
bestLOE <- glm(Oexpand ~ Tree_Cover + Elevation + Solar, family = binomial, data = loa)
summary(bestLOE)
logisticPseudoR2s(bestLOE)
Dsquared(bestLOE, adjust = TRUE)
bestLOECV<-cv.glm(data=loa,glmfit=bestLOE,K=10)
bestLOECV$delta
bestLOEAUC<-AUC(model=bestLOE)
bestLOEAUC$AUC
# Null AIC 318.88, AIC 278.27
min(loa$Tree_Cover)
max(loa$Tree_Cover)
min(loa$Elevation)
max(loa$Elevation)
min(loa$Solar)
max(loa$Solar)
# Shrub below 3760m
lsa <- subset(sa, Elevation < 3760)
X <- as.data.frame(scale(lsa[,c(3:8,28)]))
y <- lsa$Sexpand
Xy <- as.data.frame(cbind(X,y))
bestLSE <- bestglm(Xy, IC = "AIC", family = binomial)
bestLSE # Cover, Elevation, Solar, Trend
bestLSE$BestModels
bestLSE <- glm(Sexpand ~ Shrub_Cover + Elevation + Solar + Trend, family = binomial, data = lsa)
summary(bestLSE)
logisticPseudoR2s(bestLSE)
Dsquared(bestLSE, adjust = TRUE)
bestLSECV<-cv.glm(data=lsa,glmfit=bestLSE,K=10)
bestLSECV$delta
bestLSEAUC<-AUC(model=bestLSE)
bestLSEAUC$AUC
# Null AIC 352.63, AIC 321.65
min(lsa$Shrub_Cover)
max(lsa$Shrub_Cover)
min(lsa$Elevation)
max(lsa$Elevation)
min(lsa$Solar)
max(lsa$Solar)
min(lsa$Trend)
max(lsa$Trend)
# Tundra above 3500m
X <- as.data.frame(scale(hta[,c(3:8,29)]))
y <- hta$Texpand
Xy <- as.data.frame(cbind(X,y))
bestHTE <- bestglm(Xy, IC = "AIC", family = binomial)
bestHTE # CV, Solar, Slope
bestHTE$BestModels
bestHTE <- glm(Texpand ~ CV + Solar + Slope, family = binomial, data = hta)
summary(bestHTE)
logisticPseudoR2s(bestHTE)
Dsquared(bestHTE, adjust = TRUE)
bestHTECV<-cv.glm(data=hta,glmfit=bestHTE,K=10)
bestHTECV$delta
bestHTEAUC<-AUC(model=bestHTE)
bestHTEAUC$AUC
# Null AIC 148.26, AIC 138.42
min(hta$CV)
max(hta$CV)
min(hta$Solar)
max(hta$Solar)
min(hta$Slope)
max(hta$Slope)
plot(vc$Mean, vc$CV) # Less snow is more variable
cor <- cor.test(vc$Mean, vc$CV, method = "pearson")
cor
############################### Random Forests ############################################
of.rf <- randomForest(as.factor(Oexpand) ~ Tree_Cover + Elevation + Solar + Slope + Mean + CV + Trend, data = loa, family = binomial(logit), ntree = 5000, importance = TRUE)
of.rf
importance(of.rf)
varImpPlot(of.rf)
sh.rf <- randomForest(as.factor(Sexpand) ~ Shrub_Cover + Elevation + Solar + Trend + Slope + Mean + CV, data = lsa, family = binomial(logit), ntree = 5000, importance = TRUE)
sh.rf
importance(sh.rf)
varImpPlot(sh.rf)
tu.rf <- randomForest(as.factor(Texpand) ~ CV + Solar + Slope + Tundra_Cover + Elevation + Mean + Trend, data = hta, family = binomial(logit), ntree = 5000, importance = TRUE)
tu.rf
importance(tu.rf)
varImpPlot(tu.rf)
########################## Geographically Weighted Regression ##############################
# With package GWmodel
# Run model selection manually following the gwr.model.selection protocol
# Then compare to the original bestglm model
loa <- SpatialPointsDataFrame(cbind(loa$X,loa$Y), loa)
DM <- gw.dist(dp.locat=cbind(loa$X,loa$Y))
bw.f2 <- bw.ggwr(Oexpand~Tree_Cover+Elevation+Solar,data=loa,dMat=DM,
family ="binomial")
res.binomial <- ggwr.basic(Oexpand~Tree_Cover+Elevation+Solar,bw=bw.f2,
data=loa,dMat=DM,family ="binomial")
res.binomial$GW.diagnostic
lsa <- SpatialPointsDataFrame(cbind(lsa$X,lsa$Y), lsa)
DM <- gw.dist(dp.locat=cbind(lsa$X,lsa$Y))
bw.f2 <- bw.ggwr(Sexpand~Shrub_Cover+Elevation+Solar+Trend,data=lsa,dMat=DM,
family ="binomial")
res.binomial <- ggwr.basic(Sexpand~Shrub_Cover+Elevation+Solar+Trend,bw=bw.f2,
data=lsa,dMat=DM,family ="binomial")
res.binomial$GW.diagnostic
hta <- SpatialPointsDataFrame(cbind(hta$X,hta$Y), hta)
DM <- gw.dist(dp.locat=cbind(hta$X,hta$Y))
bw.f2 <- bw.ggwr(Texpand~CV+Solar+Slope,data=hta,dMat=DM,
family ="binomial")
res.binomial <- ggwr.basic(Texpand~CV+Solar+Slope,bw=bw.f2,
data=hta,dMat=DM,family ="binomial")
res.binomial$GW.diagnostic
################################### Climate Data ###########################################
# Analysis and Figure 2
d <- read.csv("Summer.csv")
m <- lm(d$Summer_mean ~ d$Year)
summary(m)
# 1972 to 2008
da <- d[20:56,]
m1 <- lm(da$Summer_mean ~ da$Year)
summary(m1)
time_series <- ts(da[,2:length(da)], start = 1972, end = 2008)
m2 <- tslm(time_series ~ trend)
summary(m2)
# Figure 2
ggplot(da, aes(x=Year,y=Summer_mean)) +
geom_point(size = 3) +
scale_x_continuous(name = "Year", breaks = seq(1972,2008,4)) +
scale_y_continuous(name = "Mean Summer Temp. (˚C)", breaks =
seq(3,12,1),
limits = c(3,11)) +
geom_smooth(method=loess, color = "blue", fill = "blue", alpha = 0.1) +
geom_smooth(method=lm, color = "red", "fill" = "red", alpha = 0.1) +
theme_bw() +
theme(legend.position="NULL",
axis.title.x = element_text(face="bold", size = 18),
axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 14),
axis.title.y = element_text(face="bold",size=18))
############################ Bare to Tundra, Soil ##########################################
bt <- read.csv("BareTundra.csv", header = TRUE)
log <- bt[1:24,]
log$Change <- c(0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1)
BTlog<-glm(Change ~ Depth, family = binomial, data = log)
summary(BTlog) # NSD
BTlog<-glm(Change ~ pH, family = binomial, data = log)
summary(BTlog) # NSD
BTlog<-glm(Change ~ Bulk, family = binomial, data = log)
summary(BTlog) # NSD
########################### Tundra to Shrub, Soil ##########################################
ts <- read.csv("TundraShrub.csv")
log <- as.data.frame(ts[1:33,])
log$Change <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
TSlog<-glm(Change ~ Depth, family = binomial, data = log)
summary(TSlog) # NSD
TSlog<-glm(Change ~ pH, family = binomial, data = log)
summary(TSlog) # NSD
TSlog<-glm(Change ~ Bulk, family = binomial, data = log)
summary(TSlog) # NSD
############################# Tundra to Forest, Soil #######################################
log <- read.csv("TundraForest.csv")
# All Species
TOlog<-glm(Change ~ Depth, family = binomial, data = log)
summary(TOlog) # NSD
TOlog<-glm(Change ~ pH, family = binomial, data = log)
summary(TOlog) # NSD
TOlog<-glm(Change ~ Bulk, family = binomial, data = log)
summary(TOlog) # NSD
# Limber Pine
TOlog<-glm(Pine ~ Depth, family = binomial, data = log)
summary(TOlog) # NSD
TOlog<-glm(Pine ~ pH, family = binomial, data = log)
summary(TOlog) # NSD
TOlog<-glm(Pine ~ Bulk, family = binomial, data = log)
summary(TOlog) # NSD
# Englemann Spruce
TOlog<-glm(Spruce ~ Depth, family = binomial, data = log)
summary(TOlog) # NSD
TOlog<-glm(Spruce ~ pH, family = binomial, data = log)
summary(TOlog) # NSD
TOlog<-glm(Spruce ~ Bulk, family = binomial, data = log)
summary(TOlog) # Significant
# Subalpine Fir
TOlog<-glm(Fir ~ Depth, family = binomial, data = log)
summary(TOlog) # NSD
TOlog<-glm(Fir ~ pH, family = binomial, data = log)
summary(TOlog) # NSD
TOlog<-glm(Fir ~ Bulk, family = binomial, data = log)
summary(TOlog) # NSD
|
c5592e69f0bf8bfc914abae660a0f889c89c6f92 | 639186029bc52a756bf395cda3cf70bc6b5ce309 | /06-IndstrInc/01-import.R | da1f809937599e3014261415ccdce8b498dc6845 | [] | no_license | Ravin515/R-Play | 36c4e502625b6bf72175b092fe84c2b8fc5f69c3 | 02b012a5bff4eb961f108ee96d2638f4933321ad | refs/heads/master | 2022-11-29T04:08:41.128438 | 2020-08-11T07:41:40 | 2020-08-11T07:43:33 | 109,561,205 | 0 | 2 | null | 2017-11-05T07:53:36 | 2017-11-05T07:53:35 | null | UTF-8 | R | false | false | 843 | r | 01-import.R | library(data.table)
library(dplyr)
library(stringr)
# a <- list.files(pattern = "*.csv")
data <- data.table(year = list.files(pattern = "*.csv"))
data[, csv := lapply(year, function(x) {
fread(x, sep = ',', fill = T, encoding = "UTF-8", na.strings = "", integer64 = "integer64")
})]
flat.data <- data[, rbindlist(.SD[['csv']], fill = T, idcol = "year")
][, year := year + 2010]
fwrite(flat.data, "2011-2013.csv")
library(RODBC)
mydb <- odbcDriverConnect("Driver={Microsoft Access Driver (*.mdb, *.accdb)}; DBQ=D:/code/r-play/07-indstrinc/2003.mdb")
sqlTables(mydb, tableName = "qy03")
res <- sqlFetch(mydb, "qy03") %>% as.data.table()
library(DBI)
library(odbc)
cn <- dbConnect(odbc::odbc(), dsn = "2003")
cn <- dbConnect(odbc::odbc(), DBQ = "D:/code/r-play/07-indstrinc/2003.mdb")
cn <- dbConnect(drv = odbc::odbc(), dsn = "2003")
|
8e97a4594ab25ed5fb76f6d55af7f079e7935fef | 57ed22671d2c348fe35c7832fd008c3a51de039c | /man/lea_prep.Rd | 39ed67834b0b167c096019af59127f01a9b2040a | [
"MIT"
] | permissive | datalorax/leaidr | 694c4d1d6d7773454673876d3c982d0db49f80b7 | 26f4672c98cae96a6ecc96e9c705890ed7a8ecb7 | refs/heads/master | 2022-11-20T13:38:44.794448 | 2020-07-27T21:42:36 | 2020-07-27T21:42:36 | 281,791,912 | 1 | 0 | null | 2020-07-22T22:02:47 | 2020-07-22T22:02:46 | null | UTF-8 | R | false | true | 584 | rd | lea_prep.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lea_prep.R
\name{lea_prep}
\alias{lea_prep}
\title{Prep State- or Nation-Wide District Shapefile}
\usage{
lea_prep(path = NULL, fips = NULL)
}
\arguments{
\item{path}{A character vector specifying a file path, such as: path = "./test".}
\item{fips}{A character vector specifying a FIPS code for a state. A reference table is available \href{https://www.mcc.co.mercer.pa.us/dps/state_fips_code_listing.htm}{here}.}
}
\value{
A shapefile.
}
\description{
\code{lea_prep()} creates your desired shapefile.
}
|
087e5ca23ac6ae8cdb98e356adeec776530a3a54 | 33b3195f06fcde4e95841c6b6db2ceeb2deb2737 | /man/transmute.Rd | b0e4f86226822f6a3f466df0f98ce1f9448c7416 | [] | no_license | serenity-r/tidylog | 26dda5c3ef05e1e87cf2fe23702a679158517a7a | f9d569c75119bb01d52cbb517a1cb180480e906c | refs/heads/master | 2020-04-21T17:12:54.823380 | 2019-02-07T06:47:05 | 2019-02-07T06:47:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 680 | rd | transmute.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mutate.R
\name{transmute}
\alias{transmute}
\alias{transmute_all}
\alias{transmute_if}
\alias{transmute_at}
\title{Wrapper around dplyr::transmute and related functions
that prints information about the operation}
\usage{
transmute(.data, ...)
transmute_all(.data, ...)
transmute_if(.data, ...)
transmute_at(.data, ...)
}
\arguments{
\item{.data}{a tbl; see \link[dplyr:mutate]{transmute}}
\item{...}{see \link[dplyr:mutate]{transmute}}
}
\value{
see \link[dplyr:mutate]{transmute}
}
\description{
Wrapper around dplyr::transmute and related functions
that prints information about the operation
}
|
914449f641473436929fe068b880f7589da25095 | fdc6d2044b02501ea04743b01bc143c2275ac010 | /R/cleanInvalidWhen.R | 9ae9a6401a4f2758a6322006c157003c39dfd3c8 | [] | no_license | fjbaron/accelerator | ad964ae4c3d5afce2a87a6d9dc1bd6c654702bed | b0da822c6150c9255f7f7551fa799b67042f0516 | refs/heads/master | 2023-06-25T02:51:56.447351 | 2023-06-13T08:14:58 | 2023-06-13T08:14:58 | 220,418,840 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 318 | r | cleanInvalidWhen.R | #' Title
#'
#' @param when
#' @param invalidWhen
#'
#' @return
#' @export
#'
#' @examples
cleanInvalidWhen=function(when,invalidWhen){
invalidWhen=invalidWhen %>% rename(label=when)
when %>% map(~ .x %>% left_join(invalidWhen,by=c("day","label")) %>% filter(is.na(reason)) %>% select(-reason) )
}
|
20fe236cdcd461760dced09d184fe6873852d341 | 17b6dbd8acf2ce8556684754dc5f48a9373f7c96 | /man/optimize_numRegions.Rd | 88042ac147aa445e18cc549ab93edb1401609bbd | [] | no_license | leekgroup/phenopredict | f53a517c670a9670041825c79456874367d92327 | ab34f6ca3c0aeb90d2c672837175d7a13c308ca5 | refs/heads/master | 2021-09-06T23:59:31.970592 | 2018-02-13T19:24:14 | 2018-02-13T19:24:14 | 66,372,434 | 16 | 3 | null | null | null | null | UTF-8 | R | false | true | 2,442 | rd | optimize_numRegions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/optimize_numRegions.R
\name{optimize_numRegions}
\alias{optimize_numRegions}
\title{Optimize number of regions used for prediction}
\usage{
optimize_numRegions(inputdata = NULL, phenodata = NULL, phenotype = NULL,
covariates = NULL, type = NULL, numRegions_set = c(10, 20, 30, 60, 80,
100, 150, 200))
}
\arguments{
\item{inputdata}{output from filter_regions() \code{inputdata}}
\item{phenodata}{data set with phenotype information; samples in rows,
variables in columns \code{phenodata}}
\item{phenotype}{phenotype of interest \code{phenotype}}
\item{covariates}{Which covariates to include in model \code{covariates}}
\item{type}{The class of the phenotype of interest (numeric, binary, factor)
\code{type}}
\item{numRegions_set}{set of numRegions to test \code{numRegions_set}}
}
\value{
Prediction accuracies across each numRegions argument tested
}
\description{
This function takes a list of possible options for
the numRegions argument of build_predictor.
Using this set of possible numRegions and expression data
(the training data / output of filter_regions()),
this function builds a predictor for each possible
numRegions. Prediction accuracy is then calculated
across varying numbers of regions. The numRegions argument
that optimizes accuracy in the training data can then be
used in build_predictor.
}
\examples{
library('GenomicRanges')
library('dplyr')
## Make up some some region data
regions <- GRanges(seqnames = 'chr2', IRanges(
start = c(28971710:28971712, 29555081:29555083, 29754982:29754984),
end = c(29462417:29462419, 29923338:29923340, 29917714:29917716)))
## make up some expression data for 9 rows and 30 people
data(sysdata, package='phenopredict')
## includes R object 'cm'
exp= cm[1:length(regions),1:30]
## generate some phenotype information
sex = as.data.frame(rep(c("male","female"),each=15))
age = as.data.frame(sample(1:100,30))
pheno = dplyr::bind_cols(sex,age)
colnames(pheno) <- c("sex","age")
## filter regions to be used to build the predictor
inputdata <- filter_regions(expression=exp, regiondata=regions,
phenodata=pheno, phenotype="sex", covariates=NULL,
type="factor", numRegions=5)
regnum <- optimize_numRegions(inputdata=inputdata ,phenodata=pheno,
phenotype="sex", covariates=NULL,type="factor",numRegions_set=c(3,5))
}
\keyword{optimization}
\keyword{phenotype,}
\keyword{prediction,}
|
ceda7a6f790b40c8cc1c845cffd261f2bb6beaf2 | f860a2ddbebe96ad25f2347823d1ad31a5ae949e | /R/inclass/class_15.R | d8e83cd3406051d24c2cac7ebefc5e29a2d4fee6 | [
"MIT"
] | permissive | mespe/STS198 | edd0e966a329b8701048e2c8371a57b0a261f2fa | 4dd8184e67689ff9d0af3dab1813973e46f59df3 | refs/heads/master | 2021-01-22T18:51:06.426391 | 2017-09-15T23:10:34 | 2017-09-15T23:10:34 | 85,125,705 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,280 | r | class_15.R | # Class 15
# STS 198
# 22 May 2017
library(ggplot2)
################################################################################
# Cleaning data
load(url("https://github.com/mespe/STS198/blob/master/data/auto_posts.rda?raw=true"))
# load("../../data/auto_posts.rda")
# Starting with price column:
head(posts$price)
# The price inculdes "$", which is going to cause issues
# if we just use as.numeric()
# We can use gsub() to get rid of the "$"
# gsub stands for global subsitution
# This does not work - "$" is a special character
price_new = gsub("$", "", posts$price)
# To search/replace for special chars $^.*+?[()]\
price_new = gsub("\\$", "", posts$price)
# Have to escape these using a double "\"
gsub(pattern = "\\\ \\$", replacement = "BOB", x = "\ $")
# This introduces NAs - We need to check those out
price_new = as.numeric(price_new)
# Turns out there are some long strings in the price column
# These are not going to convert well
long_price = nchar(posts$price) > 8
table(long_price)
posts$price[long_price]
# Looks like we are OK replacing the long ones with NAs
# Here is a test:
x = c("$353", "353", "2011 MAZDA")
as.numeric(x)
# Lets look at the results
table(price_new, useNA = "ifany")
# Now save the price as the price column
posts$price = price_new
# Plotting is helpful to check out extreme values
ggplot(posts, aes(y = price,
x = condition)) +
geom_boxplot() +
ylim(c(0,100000)) +
facet_wrap(~maker)
# We can also subset to look at the values
subset(posts, posts$maker == "maserati")
subset(posts, posts$price > 1e6)
summary(posts$price)
################################################################################
# Using lapply/sapply to go over multiple columns at once
# This would be helpful if there were more than one columns with "$"
tmp = sapply(posts[,c("price", "maker")], function(x) gsub("\\$","",x))
################################################################################
# Subsetting with the [] in 2 dimensions
# data[rows,cols]
# First row
posts[1,]
# First column
posts[,1]
# Value in first row, first column
posts[1,1]
# Can subset by name
posts["posted3625",]
posts[,"price"]
# cat() converts the \n to a newline
# Making the body easier to read
cat(posts$body[1])
|
446692c38bfc012379694967e8f232cb48fac568 | fbe57536cc2d84e69a5bf799c88fcb784e853558 | /man/unitconversion.pint.to.fluid.ounce.Rd | 9fbbdf5160d0493d028baa89a27aef340b19c692 | [
"MIT"
] | permissive | burrm/lolcat | 78edf19886fffc02e922b061ce346fdf0ee2c80f | abd3915791d7e63f3827ccb10b1b0895aafd1e38 | refs/heads/master | 2023-04-02T11:27:58.636616 | 2023-03-24T02:33:34 | 2023-03-24T02:33:34 | 49,685,593 | 5 | 2 | null | 2016-10-21T05:14:49 | 2016-01-15T00:56:55 | R | UTF-8 | R | false | true | 562 | rd | unitconversion.pint.to.fluid.ounce.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/unitconversion.pint.to.fluid.ounce.R
\name{unitconversion.pint.to.fluid.ounce}
\alias{unitconversion.pint.to.fluid.ounce}
\alias{unitconversion.pt.to.fl.oz}
\title{Unit Conversion - Liquid Volume - Pint to Fluid Ounce}
\usage{
unitconversion.pint.to.fluid.ounce(x = 1)
unitconversion.pt.to.fl.oz(x = 1)
}
\arguments{
\item{x}{Vector - Values in units of pints}
}
\value{
x, but converted to fluid ounces
}
\description{
Performs a conversion of volumes from pints to fluid ounces.
}
|
3ff0efc62797b46f17b33dd1b6df559ecf9df672 | 7fa82a4eef53ed2c3260fbdff44d26e545f92e80 | /netplot_whole_trn.R | 12cb78e360f7d077336a754ad7b07d4bbaf1d203 | [] | no_license | SocialBiologyGroupWesternU/Gene-Context | 54a6c165b22ffb385de09e8c3fc31cf417af251a | 0da3ed9f3f264b9534871a3800ddcc0f29a951b2 | refs/heads/master | 2022-12-15T02:07:26.963913 | 2020-09-18T18:59:27 | 2020-09-18T18:59:27 | 275,677,745 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,354 | r | netplot_whole_trn.R | library("igraph")
library("tidyverse")
library("RColorBrewer")
# Constants ============================================================
read_from <- "read/updatedTRN.txt" # Edgelist file
write_to <- "write/figure/netplot/whole_trn_kk_layout.pdf" # Figure file
# Helper functions ============================================================
alpha <-
function(edge){
incident_vertices <- ends(trn, edge)
vertex1_deg <- trn %>% degree(v=incident_vertices[1,1])
vertex2_deg <- trn %>% degree(v=incident_vertices[1,2])
ifelse(vertex1_deg >= vertex2_deg, vertex1_deg, vertex2_deg) %>%
magrittr::multiply_by(255/max_degree) %>%
trunc() %>%
as.hexmode() %>%
format(width=2, upper.case = TRUE) %>%
as.character()
}
colour <-
function(edge){
for (i in 1:length(clusters)){
if (edge %in% as_ids(E(clusters[[i]])))
return(colours[i])
}
return(NULL)
}
#Load and organize the data ============================================================
edgelist <-
scan(read_from, skip = 1, what = "character") %>%
matrix(ncol=2,byrow=TRUE)
trn <-
edgelist %>%
graph_from_edgelist(directed = FALSE)
trn_clustered <-
trn %>%
cluster_fast_greedy(weights=NULL)
clusters <-
trn_clustered %>%
communities() %>%
map(function(cluster) induced_subgraph(trn, cluster))
colours <- brewer.pal(length(clusters),"Set3")
max_degree <-
trn %>%
degree() %>%
max()
#Add visual attributes to trn then plot ============================================================
trn_with_attr <-
trn %>%
set_vertex_attr("label", value="") %>%
set_vertex_attr("size", value=
trn %>%
degree(normalized=TRUE) %>%
magrittr::multiply_by(30)
) %>%
set_vertex_attr("color", value=
trn_clustered %>%
membership() %>%
as.integer() %>%
map_chr(function(cluster_num) colours[cluster_num])
) %>%
set_vertex_attr("frame.color", value=NA) %>%
set_edge_attr("color", value=
trn %>%
E() %>%
as_ids() %>%
map_chr(function(edge){
edge_colour <- colour(edge)
if(!is.null(edge_colour))
paste(edge_colour,alpha(edge), sep="")
else
NA
}
)
) %>%
set_edge_attr("width", value=.5) %>%
set_graph_attr("layout", layout_with_kk)
pdf(file=write_to)
trn_with_attr %>% plot() %>% print()
dev.off()
|
6dc1eb5615079f5871e1a96549380489eaca45a4 | 4a9ce5ff6971a0f3b340af1ddbbf970ad921aa88 | /Modeling.R | 55576702f42aa6e835aef292050d91270c832a56 | [] | no_license | MarcSchneble/OnStreetParking | 0d7e8af3f82787677b62bf1f2023e56c41b03614 | 07ed62a5a8b3de06504389259d56b8812641f6bb | refs/heads/master | 2023-07-04T23:41:15.113434 | 2021-08-02T18:37:52 | 2021-08-02T18:37:52 | 392,055,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,015 | r | Modeling.R | # set current path to this file
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# clear the workspace and restart session
rm(list = ls())
# sets local system language to English
Sys.setlocale("LC_ALL","English")
source("Functions.R")
library(dplyr)
library(lubridate)
library(ggplot2)
library(survival)
library(grDevices)
library(pROC)
library(pracma)
library(readr)
library(spatstat)
library(readxl)
library(scales)
library(frailtypack)
library(mgcv)
library(PRROC)
# read parking data ----
G <- readRDS("Data/network.rds")
intens.G <- density.lpp(unmark(G), sigma = 50)
data <- read_rds("Data/data_2019_clean.rds") %>%
filter(StreetMarker %in% levels(G$data$marks),
same.day == 1, irregular == 0, DurationMinutes2 > 0,
h.start >= 8, h.start < 20,
d.start >= 91, d.start <= 191) %>%
mutate(SideOfStreetCode = factor(SideOfStreetCode, levels = unique(SideOfStreetCode)),
StreetMarker = factor(StreetMarker, levels = levels(G$data$marks)),
State = State - 1)
G$data <- G$data[G$data$marks %in% data$StreetMarker]
G$data$marks <- factor(G$data$marks, levels = unique(G$data$marks))
data$StreetMarker <- factor(data$StreetMarker, levels = levels(G$data$marks))
# distance matrix
N <- length(levels(data$StreetMarker))
distance <- matrix(NA, N, N)
for (i in 1:N) {
G.i <- G
G.i$data <- G$data[i, ]
fundist <- distfun(G.i)
distance[i, ] <- fundist(G)
}
#source("occupancy.R")
data <- read_rds("Data/data_frac.rds")
data$frac0[which(is.na(data$frac0))] <- 0
data$frac1[which(is.na(data$frac1))] <- 0
# model Lonsdale east ----
data.lonsdale.east <- filter(data, StreetName == "LONSDALE STREET",
BetweenStreet1 == "RUSSELL STREET" | BetweenStreet1 == "EXHIBITION STREET",
BetweenStreet2 == "EXHIBITION STREET" | BetweenStreet2 == "SPRING STREET") %>%
mutate(SideOfStreetCode = factor(SideOfStreetCode, levels = unique(SideOfStreetCode)))
sm <- smooth.construct.ps.smooth.spec(s(m.start), data = data.lonsdale.east, knots = NULL)
X <- sm$X
X <- sweep(X, 2, colMeans(X))[, -1]
Z <- as.data.frame(X)
colnames(Z) <- paste0("m", 1:ncol(X))
data.lonsdale.east <- bind_cols(data.lonsdale.east, Z)
ind.m <- match(unique(data.lonsdale.east$m.start), data.lonsdale.east$m.start)
x <- data.lonsdale.east$m.start[ind.m]
model.weibull.0 <- survreg(Surv(pmin(DurationMinutes2, 60), 1*(DurationMinutes2 <= 60)) ~ weekday + frailty(StreetMarker) +
SideOfStreetCode + frac1 +
m1 + m2 + m3 + m4 + m5 + m6 + m7 + m8 + m9,
dist = "weibull", data = data.lonsdale.east %>% filter(State == 0), score = TRUE)
par.m <- as.numeric(tail(model.weibull.0$coefficients, ncol(X)))
x <- data.lonsdale.east$m.start[ind.m]
y <- as.vector(X[ind.m, ]%*%par.m)
limits <- smoothConfidence(par.m,
model.weibull.0$var[11:19, 11:19],
X[ind.m, ])
df <- tibble(x = x, y = -y/model.weibull.0$scale, y.lower = -limits$lower/model.weibull.0$scale,
y.upper = -limits$upper/model.weibull.0$scale)
g0 <- ggplot(df) +
geom_line(aes(x = x, y = y), col = "red") +
geom_ribbon(aes(x = x, ymin = y.lower, ymax = y.upper), alpha = 0.5) +
theme_bw() +
scale_x_continuous(breaks = seq(360, 1200, 120), labels = c("6am", "8am", "10am", "12pm", "2pm", "4pm", "6pm", "8pm")) +
labs(x = expression(paste(hour[t])), y = expression(paste(g[4*","*0](hour[t]))), parse = TRUE) +
scale_y_continuous(limits = c(-0.8, 1))
pdf(file = "Plots/hour0.pdf", width = 4.5, height = 3)
print(g0)
dev.off()
model.weibull.1 <- survreg(Surv(pmin(DurationMinutes2, 60), 1*(DurationMinutes2 <= 60)) ~ weekday + frailty(StreetMarker) +
SideOfStreetCode + frac0 +
m1 + m2 + m3 + m4 + m5 + m6 + m7 + m8 + m9,
dist = "weibull", data = data.lonsdale.east %>% filter(State == 1), score = TRUE)
par.m <- as.numeric(tail(model.weibull.1$coefficients, ncol(X)))
y <- as.vector(X[ind.m, ]%*%par.m)
limits <- smoothConfidence(par.m,
model.weibull.1$var[11:19, 11:19],
X[ind.m, ])
df <- tibble(x = x, y = -y/model.weibull.1$scale, y.lower = -limits$lower/model.weibull.1$scale,
y.upper = -limits$upper/model.weibull.1$scale)
g1 <- ggplot(df) +
geom_line(aes(x = x, y = y), col = "red") +
geom_ribbon(aes(x = x, ymin = y.lower, ymax = y.upper), alpha = 0.5) +
theme_bw() +
scale_x_continuous(breaks = seq(360, 1200, 120), labels = c("6am", "8am", "10am", "12pm", "2pm", "4pm", "6pm", "8pm")) +
labs(x = expression(paste(hour[t])), y = expression(paste(g[4*","*1](hour[t]))), parse = TRUE) +
scale_y_continuous(limits = c(-0.8, 1))
pdf(file = "Plots/hour1.pdf", width = 4.5, height = 3)
print(g1)
dev.off()
# prediction ----
R <- 100
dur <- 10
time <- as.POSIXct("2019-06-01 10:00:00", tz = "Australia/Melbourne")
observed <- prediction.det <- prediction.exp <- prediction.weibull <- prediction.weibull.wostar <- NULL
for (r in 1:R) {
time.r <- time + days(sample(0:29, 1)) + seconds(runif(1, 0, 60*120))
# simulate data point and compute distance function
P <- rlpp(1, intens.G)
fundist <- distfun(P)
dist <- fundist(G)
# data for distance
ind.distance <- which(dist <= 300)
markers.distance <- levels(data$StreetMarker)[ind.distance]
ind.fit <- which(dist <= 250)
if (length(ind.fit) > 10) {
print(r)
markers.fit <- levels(data$StreetMarker)[ind.fit]
pre <- get_occupancy2(time.r, data, ind.distance, duration = TRUE)
post <- get_occupancy2(time.r + minutes(dur), data, ind.fit, duration = FALSE)
# fit models
data.r <- data %>% filter(StreetMarker %in% markers.fit,
difftime(time.r, DepartureTime, units = "days") > 0,
difftime(time.r, DepartureTime, units = "days") < 30) %>%
mutate(SideOfStreetCode = factor(SideOfStreetCode, levels = unique(SideOfStreetCode)),
StreetMarker = factor(StreetMarker, levels = unique(StreetMarker)))
data0 <- data.r %>% filter(State == 0)
data1 <- data.r %>% filter(State == 1)
markers <- intersect(unique(data0$StreetMarker), unique(data1$StreetMarker))
data0 <- data0 %>% filter(StreetMarker %in% markers) %>%
mutate(SideOfStreetCode = factor(SideOfStreetCode, levels = unique(SideOfStreetCode)),
StreetMarker = factor(StreetMarker, levels = unique(StreetMarker)))
data1 <- data1 %>% filter(StreetMarker %in% markers) %>%
mutate(SideOfStreetCode = factor(SideOfStreetCode, levels = unique(SideOfStreetCode)),
StreetMarker = factor(StreetMarker, levels = unique(StreetMarker)))
fmla0 <- Surv(pmin(DurationMinutes2, 60), 1*(DurationMinutes2 <= 60)) ~ weekday + frailty(StreetMarker) +
factor(h.start) + frac1
if (length(unique(data0$SideOfStreetCode)) > 1) {
fmla0 <- update(fmla0, ~ .+ SideOfStreetCode)
}
fmla1 <- Surv(pmin(DurationMinutes2, 60), 1*(DurationMinutes2 <= 60)) ~ weekday + frailty(StreetMarker) +
factor(h.start) + frac1
if (length(unique(data1$SideOfStreetCode)) > 1) {
fmla1 <- update(fmla1, ~ .+ SideOfStreetCode)
}
model.exp.0 <- survreg(fmla0, dist = "exponential", data = data0, score = TRUE)
model.exp.1 <- survreg(fmla1, dist = "exponential", data = data1, score = TRUE)
model.weibull.0 <- survreg(fmla0, dist = "weibull", data = data0, score = TRUE)
model.weibull.1 <- survreg(fmla1, dist = "weibull", data = data1, score = TRUE)
# get parameters from the model
par.exp.0 <- get_par2(time.r, model.exp.0, data0, pre, 0, distance)
par.exp.1 <- get_par2(time.r, model.exp.1, data1, pre, 1, distance)
par.weibull.0 <- get_par2(time.r, model.weibull.0, data0, pre, 0, distance)
par.weibull.1 <- get_par2(time.r, model.weibull.1, data1, pre, 1, distance)
# predict occupancy
occupancy.pre <- filter(pre, marker %in% markers)$occupancy
occupancy.post <- filter(post, marker %in% markers)$occupancy
d0 <- filter(pre, marker %in% markers)$duration
ind.prediction <- which(is.element(occupancy.pre, c(0, 1)) & is.element(occupancy.post, c(0, 1)))
prediction.exp <- c(prediction.exp, get_prediction_exp(occupancy.pre, par.exp.0, par.exp.1, dur)[ind.prediction])
prediction.weibull <- c(prediction.weibull, get_prediction_weibull(occupancy.pre, d0, par.weibull.0, par.weibull.1, dur)[ind.prediction])
d0 <- rep(0, length(d0))
prediction.weibull.wostar <- c(prediction.weibull.wostar, get_prediction_weibull(occupancy.pre, d0, par.weibull.0, par.weibull.1, dur)[ind.prediction])
prediction.det <- c(prediction.det, 1 - occupancy.pre[ind.prediction])
observed <- c(observed, occupancy.post[ind.prediction])
}
}
roc.exp <- pROC::roc(observed, prediction.exp, levels = c(1, 0))
roc.weibull <- pROC::roc(observed, prediction.weibull, levels = c(1, 0))
roc.weibull.wostar <- pROC::roc(observed, prediction.weibull.wostar, levels = c(1, 0))
roc.check <- pROC::roc(observed, prediction.det, levels = c(1, 0))
df.exp <- tibble(x = roc.exp$specificities, y = roc.exp$sensitivities, kind = "exp")
df.weibull <- tibble(x = roc.weibull$specificities, y = roc.weibull$sensitivities, kind = "weibull")
df.weibull.wostar <- tibble(x = roc.weibull.wostar$specificities,
y = roc.weibull.wostar$sensitivities, kind = "weibullwostar")
df.check <- tibble(x = roc.check$specificities, y = roc.check$sensitivities, kind = "check")
df.random <- tibble(x = c(0, 1), y = c(1, 0), kind = "random")
df <- bind_rows(df.weibull, df.weibull.wostar, df.exp) %>%
mutate(kind = factor(kind, levels = c("weibull", "weibullwostar", "exp")))
g <- ggplot() +
geom_ribbon(data = df %>% filter(kind == "weibull"), aes(x = 1-x, ymin = 0, ymax = y),
alpha = 0.2) +
geom_line(data = df, aes(x = 1-x, y = y, color = kind, linetype = kind)) +
scale_color_hue(name = "Predictor",
labels = c(paste0("Semi-Markov\n(AUC = ", round(roc.weibull$auc, 3), ")"),
paste0("Markov\n(AUC = ", round(roc.exp$auc, 3), ")"),
paste0("Random\n(AUC = 0.5)"))) +
labs(x = "Specifciity", y = "Senisitivity", linetype = "Predictor") +
theme_bw() +
theme(legend.position = "bottom", legend.key.width = unit(0.8, "cm")) +
scale_x_continuous(breaks = seq(0, 1, 0.2), labels = seq(1, 0, -0.2)) +
scale_y_continuous(breaks = seq(0, 1, 0.2)) +
scale_linetype_manual(labels = c(paste0("Semi-Markov\n(AUC = ", round(roc.weibull$auc, 3), ")"),
paste0("Markov\n(AUC = ", round(roc.exp$auc, 3), ")"),
paste0("Random\n(AUC = 0.5)")),
values = c(1, 3, 5))
pdf(file = "Plots/roc_30min_afternoon.pdf", width = 5.5, height = 5.5)
print(g)
dev.off()
# modeling with different prediction horizons ----
dur <- seq(5, 60, 5)
AUC <- matrix(0, length(dur), 3)
R <- 100
daytime <- 10
for (d in 1:length(dur)) {
time <- as.POSIXct("2019-06-01", tz = "Australia/Melbourne") + hours(daytime)
observed <- prediction.det <- prediction.exp <- prediction.weibull <- prediction.weibull.wostar <- NULL
for (r in 1:R) {
time.r <- time + days(sample(0:29, 1)) + seconds(runif(1, 0, 60*120))
# simulate data point and compute distance function
P <- rlpp(1, intens.G)
fundist <- distfun(P)
dist <- fundist(G)
# data for distance
ind.distance <- which(dist <= 300)
markers.distance <- levels(data$StreetMarker)[ind.distance]
ind.fit <- which(dist <= 250)
if (length(ind.fit) > 10) {
print(r)
markers.fit <- levels(data$StreetMarker)[ind.fit]
pre <- get_occupancy2(time.r, data, ind.distance, duration = TRUE)
post <- get_occupancy2(time.r + minutes(dur[d]), data, ind.fit, duration = FALSE)
# fit models
data.r <- data %>% filter(StreetMarker %in% markers.fit,
difftime(time.r, DepartureTime, units = "days") > 0,
difftime(time.r, DepartureTime, units = "days") < 30) %>%
mutate(SideOfStreetCode = factor(SideOfStreetCode, levels = unique(SideOfStreetCode)),
StreetMarker = factor(StreetMarker, levels = unique(StreetMarker)))
data0 <- data.r %>% filter(State == 0)
data1 <- data.r %>% filter(State == 1)
markers <- intersect(unique(data0$StreetMarker), unique(data1$StreetMarker))
data0 <- data0 %>% filter(StreetMarker %in% markers) %>%
mutate(SideOfStreetCode = factor(SideOfStreetCode, levels = unique(SideOfStreetCode)),
StreetMarker = factor(StreetMarker, levels = unique(StreetMarker)))
data1 <- data1 %>% filter(StreetMarker %in% markers) %>%
mutate(SideOfStreetCode = factor(SideOfStreetCode, levels = unique(SideOfStreetCode)),
StreetMarker = factor(StreetMarker, levels = unique(StreetMarker)))
fmla0 <- Surv(pmin(DurationMinutes2, 60), 1*(DurationMinutes2 <= 60)) ~ weekday + frailty(StreetMarker) +
factor(h.start) + frac1
if (length(unique(data0$SideOfStreetCode)) > 1) {
fmla0 <- update(fmla0, ~ .+ SideOfStreetCode)
}
fmla1 <- Surv(pmin(DurationMinutes2, 60), 1*(DurationMinutes2 <= 60)) ~ weekday + frailty(StreetMarker) +
factor(h.start) + frac1
if (length(unique(data1$SideOfStreetCode)) > 1) {
fmla1 <- update(fmla1, ~ .+ SideOfStreetCode)
}
model.exp.0 <- survreg(fmla0, dist = "exponential", data = data0, score = TRUE)
model.exp.1 <- survreg(fmla1, dist = "exponential", data = data1, score = TRUE)
model.weibull.0 <- survreg(fmla0, dist = "weibull", data = data0, score = TRUE)
model.weibull.1 <- survreg(fmla1, dist = "weibull", data = data1, score = TRUE)
# get parameters from the model
par.exp.0 <- get_par2(time.r, model.exp.0, data0, pre, 0, distance)
par.exp.1 <- get_par2(time.r, model.exp.1, data1, pre, 1, distance)
par.weibull.0 <- get_par2(time.r, model.weibull.0, data0, pre, 0, distance)
par.weibull.1 <- get_par2(time.r, model.weibull.1, data1, pre, 1, distance)
# predict occupancy
occupancy.pre <- filter(pre, marker %in% markers)$occupancy
occupancy.post <- filter(post, marker %in% markers)$occupancy
d0 <- filter(pre, marker %in% markers)$duration
ind.prediction <- which(is.element(occupancy.pre, c(0, 1)) & is.element(occupancy.post, c(0, 1)))
prediction.exp <- c(prediction.exp,
get_prediction_exp(occupancy.pre, par.exp.0, par.exp.1, dur[d])[ind.prediction])
prediction.weibull <- c(prediction.weibull,
get_prediction_weibull(occupancy.pre, d0, par.weibull.0, par.weibull.1, dur[d])[ind.prediction])
prediction.weibull.wostar <- c(prediction.weibull.wostar,
get_prediction_weibull(occupancy.pre, d0, par.weibull.0, par.weibull.1, dur[d], star = FALSE)[ind.prediction])
prediction.det <- c(prediction.det, 1 - occupancy.pre[ind.prediction])
observed <- c(observed, occupancy.post[ind.prediction])
}
}
roc.exp <- pROC::roc(observed, prediction.exp, levels = c(1, 0))
roc.weibull <- pROC::roc(observed, prediction.weibull, levels = c(1, 0))
roc.weibull.wostar <- pROC::roc(observed, prediction.weibull.wostar, levels = c(1, 0))
roc.check <- pROC::roc(observed, prediction.det, levels = c(1, 0))
#AUC[d, ] <- c(roc.weibull$auc, roc.weibull.wostar$auc, roc.exp$auc)
#saveRDS(AUC, file = "AUC.rds")
df.exp <- tibble(x = roc.exp$specificities, y = roc.exp$sensitivities, kind = "exp")
df.weibull <- tibble(x = roc.weibull$specificities, y = roc.weibull$sensitivities, kind = "weibull")
df.weibull.wostar <- tibble(x = roc.weibull.wostar$specificities,
y = roc.weibull.wostar$sensitivities, kind = "weibullwostar")
df.check <- tibble(x = roc.check$specificities, y = roc.check$sensitivities, kind = "check")
df.random <- tibble(x = c(0, 1), y = c(1, 0), kind = "random")
df <- bind_rows(df.weibull, df.weibull.wostar, df.exp) %>%
mutate(kind = factor(kind, levels = c("weibull", "weibullwostar", "exp")))
g <- ggplot() +
geom_ribbon(data = df %>% filter(kind == "weibull"), aes(x = 1-x, ymin = 0, ymax = y),
alpha = 0.2) +
geom_line(data = df, aes(x = 1-x, y = y, color = kind, linetype = kind)) +
scale_color_hue(name = "Predictor",
labels = c(paste0("Semi-Markov\n(state space S*,\nAUC = ", round(roc.weibull$auc, 3), ")"),
paste0("Semi-Markov\n(state space S,\nAUC = ", round(roc.weibull.wostar$auc, 3), ")"),
paste0("Markov\n(state space S,\nAUC = ", round(roc.exp$auc, 3), ")"))) +
labs(x = "Specifciity", y = "Senisitivity", linetype = "Predictor") +
theme_bw() +
theme(legend.position = "bottom", legend.key.width = unit(0.8, "cm")) +
scale_x_continuous(breaks = seq(0, 1, 0.2), labels = seq(1, 0, -0.2)) +
scale_y_continuous(breaks = seq(0, 1, 0.2)) +
scale_linetype_manual(labels = c(paste0("Semi-Markov\n(state space S*,\nAUC = ", round(roc.weibull$auc, 3), ")"),
paste0("Semi-Markov\n(state space S,\nAUC = ", round(roc.weibull.wostar$auc, 3), ")"),
paste0("Markov\n(state space S,\nAUC = ", round(roc.exp$auc, 3), ")")),
values = c(1, 3, 5)) +
geom_line(data = tibble(x = c(0, 1), y = c(0, 1)), aes(x = x, y = y))
pdf(file = paste0("Plots/roc_", dur[d], "min_daytime", daytime, ".pdf"), width = 5.5, height = 5.5)
print(g)
dev.off()
}
AUC <- readRDS("AUC.rds")
df <- tibble(duration = rep(dur, 3),
AUC = as.vector(AUC),
predictor = rep(c("weibull", "weibullwostar", "exp"), each = length(dur))) %>%
mutate(predictor = factor(predictor, levels = c("weibull", "weibullwostar", "exp")))
g <- ggplot(df, aes(x = duration, y = AUC, color = predictor)) +
geom_point() +
geom_line(aes(linetype = predictor)) +
theme_bw() +
scale_y_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.2)) +
scale_x_continuous(breaks = seq(0, 60, 10)) +
labs(x = "Prediction horizon (in minutes)", color = "Predictor", linetype = "Predictor") +
scale_linetype_manual(labels = c(paste0("Semi-Markov (state space S*)"),
paste0("Semi-Markov (state space S)"),
paste0("Markov (State space S)")),
values = c(1, 3, 5)) +
scale_color_hue(labels = c(paste0("Semi-Markov (state space S*)"),
paste0("Semi-Markov (state space S)"),
paste0("Markov (State space S)"))) +
geom_hline(yintercept = 0.5)
pdf(file = paste0("Plots/AUC.pdf"), width = 8, height = 4)
print(g)
dev.off()
|
7d8163f4e1e555f607fb905f3053dbf81b32ead1 | 5c542ee6a12a4637236ee876da7eb6c42667b211 | /analysis-functions.R | e80b02771688d695483ab67368946687dd79f481 | [] | no_license | jeffeaton/epp-spectrum | cecacd42c0f926d681bf3f3d9d9e6e21dd28e9a3 | b525b52e05ddbd914ec6d9c7095a52a512419929 | refs/heads/master | 2021-01-10T21:13:09.377733 | 2015-05-27T09:20:12 | 2015-05-27T09:20:12 | 19,249,007 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,372 | r | analysis-functions.R | ############################
#### Spline functions ####
############################
proj.steps <- seq(1, 43.5, 0.1) # 1970:2012
t0 <- 6
dt <- 0.1
numSplineSteps <- sum(proj.steps >= t0)
numSplines <- 7
library(splines)
fnBSpline <- function(u, q = numSplines, m = 2){
x <- seq(1,numSplineSteps)
k <- seq(min(x),max(x),length=q-m) # knots
dk <- k[2]-k[1]
k <- c(k[1]-dk*((m+1):1),k,k[q-m]+dk*(1:(m+1)))
X <- splineDesign(k,x,ord=m+2) # model matrix
b <- u # Now the coefficients
b[1] <- u[1]
## b[2] <- u[2]
b[2] <- b[1] + u[2]
for(i in 3:length(b)){
b[i] <- 2*b[i-1] - b[i-2] + u[i]
}
## Predicted r
r <- c(rep(0, sum(proj.steps < t0)), X%*%b)
return(r)
}
##############################
#### Analysis functions ####
##############################
fert.idx <- 4:10
a15to49.idx <- 4:10
a15to24.idx <- 4:5
a15plus.idx <- 4:AG
m.idx <- 1
f.idx <- 2
fnRVec <- function(theta){return(fnBSpline(theta[-(1:2)]))}
fnIota <- function(theta){return(exp(theta[1]))}
fnMod <- function(theta){return(fnSpectrum(fnIota(theta), fnRVec(theta)))}
fnASFRadjPrev <- function(mod){
prev.by.age <- rowSums(mod[,2, fert.idx, -1,],,2)/rowSums(mod[,2, fert.idx,,],,2)
births.by.age <- t(asfr[,1:dim(mod)[1]]) * rowSums(mod[,2, fert.idx,,],,2)
return(rowSums(prev.by.age * births.by.age) / rowSums(births.by.age))
}
fnANCprev <- function(mod){
hivn.by.age <- rowSums(mod[,2, fert.idx, 1,],,2)
hivp.by.age <- rowSums(mod[,2, fert.idx, -1,],,2)
births.by.age <- t(asfr[,1:dim(mod)[1]]) * (hivn.by.age + hivp.by.age)
frac.hivp.moth <- 1.0 - hivn.by.age/(sweep(hivp.by.age, 2, fert.rat, "*")+hivn.by.age)
return(rowSums(births.by.age * frac.hivp.moth) / rowSums(births.by.age))
}
prev <- function(mod, age.idx=a15to49.idx, sex.idx=c(m.idx, f.idx)) return(rowSums(mod[,sex.idx, age.idx,-1,])/rowSums(mod[,sex.idx, age.idx,,]))
ageprev <- function(mod, age.idx = 1:dim(mod)[3], agegr.idx = 1:length(age.idx)){
hivn <- apply(mod[,,age.idx,1,1], 1:2, tapply, agegr.idx, sum)
tot <- apply(rowSums(mod[,,age.idx,,], d=3), 1:2, tapply, agegr.idx, sum)
return(aperm(1 - hivn/tot, c(2, 3, 1)))
}
## fn15to49inc <- function(mod)
## inc.rate.15to49 <- rVec[ts == proj.steps] * ((sum(X[,age15to49.idx,-1,1]) + relinfect.ART*sum(X[,age15to49.idx,-1,-1]))/sum(X[,age15to49.idx,,]) + (ts == t0)*iota)
|
e694b04bdd0a30a480ef2a172009aa2ea69d8749 | cd1d77f9de36cddcd912324123559692425b3625 | /Lecture-11/figure/fig-11-02.R | 64d75e634c4209dd4df0fe83c5c7f7453a7ee8a3 | [] | no_license | jirou7800/Statistics_YNU2020_exercise | 5b798b5bb81d0abf14dddc55fb7f51306ddf3f7b | fdc7e365a884d9986d4bf8f39c03630add3ced14 | refs/heads/master | 2022-11-25T17:17:39.210419 | 2020-08-05T03:01:03 | 2020-08-05T03:01:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 939 | r | fig-11-02.R | library(dplyr)
library(ggplot2)
circle <- data.frame(t = seq(from= 0, to = 2*pi, length.out = 100)) %>%
mutate(x = cos(t),
y = sin(t)) %>%
dplyr::select(-t) %>%
mutate(type = factor("circle"))
quadra <- data.frame(x = seq(from = -1, to = 1, length.out = 100)) %>%
mutate(y = x^2 -0.5) %>%
mutate(type = factor("quadratic"))
sin <- data.frame(x = seq(from = -1.5*pi, to = 2.5*pi, length.out = 100)) %>%
mutate(y = sin(x)) %>%
mutate(type = factor("sin"))
bind_rows(circle, quadra, sin) %>%
ggplot() +
aes(x = x, y = y) +
geom_point() +
facet_wrap(~ type, ncol = 3, scale = "free") +
xlab("") +
ylab("") +
theme(axis.text.y = element_blank()) +
theme(axis.text.x = element_blank())
#library(purrr)
#library(broom)
#bind_rows(circle, quadra, sin) %>%
# nest(data = -type) %>%
# mutate(cor = map(data, ~ cor.test(.x$x, .x$y)),
# tidied = map(cor, tidy)
# ) %>%
# unnest(tidied) |
56794b3195582f7e404eab476c3b31e9ecca7360 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/heemod/examples/combine_probs.Rd.R | 1d610ba3a5fb353afe58cc195ceb2639461fa052 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 181 | r | combine_probs.Rd.R | library(heemod)
### Name: combine_probs
### Title: Combine Probabilities
### Aliases: combine_probs
### ** Examples
(p1 <- runif(5))
(p2 <- runif(5))
combine_probs(p1, p2)
|
e0bcf9ac26e5ddad2b473eb7649cc16ae9217e0f | b2d32cb57604a26e31f0c4947ee866f59a7aa8ba | /R/searcher_efficiency_figure_functions.R | 6d89117ff414f572829c853b62b9e82a418220e9 | [
"LicenseRef-scancode-warranty-disclaimer",
"CC0-1.0",
"LicenseRef-scancode-public-domain"
] | permissive | atredennick/GenEst | fe1f95ca844b2bf9cf0f3358e810439b0a964410 | 38b73a290c074872c0651926bd6de283072aa8e6 | refs/heads/master | 2020-06-10T20:24:52.460911 | 2019-06-25T16:52:44 | 2019-06-25T16:52:44 | 193,736,013 | 0 | 0 | null | 2019-06-25T15:35:03 | 2019-06-25T15:35:03 | null | UTF-8 | R | false | false | 26,174 | r | searcher_efficiency_figure_functions.R | #' @title Plot results of a single pk model
#'
#' @description Plot a single \code{\link{pkm}} model
#'
#' @param x model of class pkm
#'
#' @param col color to use
#'
#' @param ... arguments to be passed to sub functions
#'
#' @return a plot
#'
#' @examples
#' data(wind_RP)
#' mod <- pkm(formula_p = p ~ Season, formula_k = k ~ 1, data = wind_RP$SE)
#' plot(mod)
#'
#' @export
#'
plot.pkm <- function(x, col = "black", ...){
model <- x
if (anyNA(model$varbeta) || sum(diag(model$varbeta) < 0) > 0){
stop("Variance in pkm not well-defined. Cannot plot.")
}
name_p <- format(model$formula_p)
name_k <- model$formula_k
if (!is.null(model$pOnly) && model$pOnly){
stop("k missing from pk model. Cannot plot.")
}
if (class(name_k) == "numeric"){
name_k <- paste("k fixed at ", name_k, sep = "")
} else if (class(name_k) == "character"){
name_k <- "k not estimated"
}else {
name_k <- format(model$formula_k)
}
modelName <- paste(name_p, "; ", name_k, sep = "")
par(mar = c(0, 0, 0, 0))
par(fig = c(0, 1, 0.95, 1))
plot(1, 1, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n', xlab = "",
ylab = "", ylim = c(0, 1), xlim = c(0, 1)
)
points(c(0.01, 0.06), c(0.25, 0.25), type = 'l', lwd = 2, col = col)
text(x = 0.07, y = 0.3, "= Median", adj = 0, cex = 0.9)
points(c(0.2, 0.25), c(0.25, 0.25), type = 'l', lwd = 2, lty = 3, col = col)
text(x = 0.26, y = 0.3, "= Confidence Bounds", adj = 0, cex = 0.9)
labelsText <- paste(model$predictors, collapse = ".")
text_label <- paste("Labels: ", labelsText, sep = "")
text_model <- paste("Model: ", modelName, sep = "")
text(x = 0.58, y = 0.3, text_label, adj = 0, cex = 0.75)
text(x = 0.58, y = 0.7, text_model, adj = 0, cex = 0.75)
par(mar = c(2,4,2,1))
par(fig = c(0, 0.5, 0.725, 0.975), new = TRUE)
pkmParamPlot(model = model, pk = "p", col = col)
par(fig = c(0.5, 1, 0.725, 0.975), new = TRUE)
pkmParamPlot(model = model, pk = "k", col = col)
par(fig = c(0, 1, 0, 0.75), new = TRUE)
par(mar = c(1, 1, 1, 1))
plot(1,1, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n', xlab = "",
ylab = ""
)
mtext(side = 1, "Search", line = -0.25, cex = 1.5)
mtext(side = 2, "Searcher Efficiency", line = -0.25, cex = 1.5)
ncell <- model$ncell
cellNames <- model$cells[ , "CellNames"]
nmatrix_col <- min(3, ncell)
nmatrix_row <- ceiling(ncell / nmatrix_col)
figxspace <- 0.95 / nmatrix_col
figyspace <- 0.65 / nmatrix_row
x1 <- rep(figxspace * ((1:nmatrix_col) - 1), nmatrix_row) + 0.05
x2 <- rep(figxspace * ((1:nmatrix_col)), nmatrix_row) + 0.05
y1 <- rep(figyspace * ((nmatrix_row:1) - 1), each = nmatrix_col) + 0.04
y2 <- rep(figyspace * ((nmatrix_row:1)), each = nmatrix_col) + 0.04
bottomCells <- seq(ncell - (nmatrix_col - 1), ncell, 1)
leftCells <- which(1:ncell %% nmatrix_col == 1)
if (length(leftCells) == 0){
leftCells <- 1
}
for (celli in 1:ncell){
par(mar = c(2.5, 2, 0, 0))
par(fig = c(x1[celli], x2[celli], y1[celli], y2[celli]), new = T)
specificCell <- cellNames[celli]
axis_x <- FALSE
axis_y <- FALSE
if (celli %in% bottomCells){
axis_x <- TRUE
}
if (celli %in% leftCells){
axis_y <- TRUE
}
pkmSECellPlot(model = model, specificCell = specificCell, col = col,
axis_y = axis_y, axis_x = axis_x)
}
}
#' Plot parameter box plots for each cell for either p or k
#'
#' @param model model of class pkm
#'
#' @param pk character of "p" or "k" to delineate between parameter graphed
#'
#' @param col color to use
#'
#' @return a parameter plot panel
#'
#' @export
#'
pkmParamPlot <- function(model, pk = "p", col){
ncell <- model$ncell
cellNames <- model$cells[ , "CellNames"]
predictors <- model$predictors
CL <- model$CL
probs <- c(0, (1 - CL) / 2, 0.25, 0.5, 0.75, 1 - (1 - CL) / 2, 1)
pks <- rpk(n = 1000, model = model)
pks_full <- rpk(n = 1000, model = model)
if (pk == "p"){
maxy <- 1
} else if (pk == "k"){
maxy <- 1
for (celli in 1:ncell){
maxcell <- max(pks[[celli]][ , "k"]) * 1.01
maxy <- max(maxy, maxcell)
}
}
maxy[is.na(maxy)] <- 1
plot(1, type = "n", xlab = "", ylab = "", bty = "L", xaxt = 'n', yaxt = 'n',
ylim = c(0, maxy), xlim = c(0.5, ncell + 0.5)
)
for (celli in 1:ncell){
x <- celli
y <- quantile(pks[[celli]][ , pk], probs, na.rm = TRUE)
med <- c(-0.1, 0.1)
tb <- c(-0.07, 0.07)
rect(x - 0.1, y[3], x + 0.1, y[5], lwd = 2, border = col)
points(x + med, rep(y[4], 2), type = 'l', lwd = 2, col = col)
points(x + tb, rep(y[2], 2), type = 'l', lwd = 2, col = col)
points(x + tb, rep(y[6], 2), type = 'l', lwd = 2, col = col)
points(rep(x, 3), y[1:3], type = 'l', lwd = 2, col = col)
points(rep(x, 3), y[5:7], type = 'l', lwd = 2, col = col)
}
axis(1, at = 1:ncell, cellNames, las = 1, cex.axis = 0.75)
axis(2, at = seq(0, 1, 0.5), las = 1, cex.axis = 0.75)
axis(2, at = seq(0, 1, 0.1), labels = FALSE, tck = -0.05)
mtext(side = 2, pk, line = 2.75, cex = 1.1)
}
#' Plot cell-specific decay curve for searcher efficiency
#'
#' @param model model of class pkm
#'
#' @param specificCell name of the specific cell to plot
#'
#' @param col color to use
#'
#' @param axis_x logical of whether or not to plot the x axis
#'
#' @param axis_y logical of whether or not to plot the y axis
#'
#' @return a cell plot panel
#'
#' @export
#'
pkmSECellPlot <- function(model, specificCell, col, axis_y = TRUE,
axis_x = TRUE){
CL <- model$CL
cellwise <- model$cell_pk
cellNames <- model$cells[ , "CellNames"]
whichCarcs <- which(model$carcCell == specificCell)
observations <- as.matrix(model$observations[whichCarcs, ],
nrow = length(whichCarcs), ncol = ncol(model$observations)
)
nobs <- ncol(observations)
ncarc <- nrow(observations)
carcFound <- apply(observations, 2, sum, na.rm = TRUE)
carcUnavail <- apply(apply(observations, 2, is.na), 2, sum)
carcAvail <- ncarc - carcUnavail
whichSpecificCell <- which(cellNames == specificCell)
p <- cellwise[whichSpecificCell, "p_median"]
k <- cellwise[whichSpecificCell, "k_median"]
pks <- rpk(n = 1000, model = model)
ps <- pks[[whichSpecificCell]][ , "p"]
ks <- pks[[whichSpecificCell]][ , "k"]
searchTab <- matrix(1:nobs, nrow = length(ps), ncol = nobs, byrow = TRUE)
ktab <- ks^(searchTab - 1)
SE <- ps * ktab
y <- apply(SE, 2, median)
y_l <- apply(SE, 2, quantile, probs = (1 - CL) / 2)
y_u <- apply(SE, 2, quantile, probs = 1 - (1 - CL) / 2)
x_pts <- 1:nobs
y_pts <- carcFound / carcAvail
plot(x_pts, y_pts, ylim = c(0, 1), xlim = c(0.5, nobs + 0.5), xlab = "",
ylab = "", xaxt = "n", yaxt = "n", bty = "L", col = rgb(0.02, 0.02, 0.02),
lwd = 2, pch = 1, cex = 1.5
)
points(x_pts, y, type = 'l', lwd = 3, col = col)
points(x_pts, y_l, type = 'l', lwd = 2, lty = 3, col = col)
points(x_pts, y_u, type = 'l', lwd = 2, lty = 3, col = col)
for (obi in 1:nobs){
x1 <- x_pts[obi] - 0.25
y1 <- y_pts[obi] + 0.06
x2 <- x_pts[obi] + 0.35
y2 <- y_pts[obi] + 0.15
rect(x1, y1, x2, y2, border = NA, col = "white")
}
obsLabels <- paste(carcFound, carcAvail, sep = "/")
text(x_pts + 0.05, y_pts + 0.1, obsLabels, cex = 0.65)
axis(1, at = x_pts, las = 1, cex.axis = 0.75, labels = axis_x)
axis(2, at = seq(0, 1, 0.2), las = 1, cex.axis = 0.75, labels = axis_y)
text(0.5, 0.95, specificCell, adj = 0, cex = 0.75, font = 2)
}
#' @title Plot results of a set of SE models
#'
#' @description Produce a set of figures for a set of SE models, as fit by
#' \code{\link{pkmSet}}
#'
#' @param x pk model set of class pkmSet
#'
#' @param specificModel the name(s) or index number(s) of specific model(s)
#' to restrict the plot
#'
#' @param app logical indicating if the plot is for the app
#'
#' @param cols named vector of colors to use for the specific and reference
#' models
#'
#' @param ... to be sent to subfunctions
#'
#' @return a set of plots
#'
#' @examples
#' data(wind_RP)
#' mod <- pkmSet(formula_p = p ~ Season, formula_k = k ~ Season,
#' data = wind_RP$SE
#' )
#' plot(mod)
#'
#' @export
#'
plot.pkmSet <- function(x, specificModel = NULL, app = FALSE, cols = SEcols(),
...){
modelSet <- x
specMods <- checkSpecificModelSE(modelSet, specificModel)
modelSet <- tidyModelSetSE(modelSet)
nmod <- length(specMods)
for (modi in 1:nmod){
if (modi == 2){
devAskNewPage(TRUE)
}
if (!is.null(modelSet[[modi]]$pOnly) && modelSet[[modi]]$pOnly){
plot(0, 0, type = 'n', axes = F, xlab = '', ylab = '')
text(0, .5, "k missing from pk model. Cannot plot.", cex = 2, col = 2)
} else {
plotSEFigure(modelSet, specMods[modi], app, cols)
}
}
devAskNewPage(FALSE)
}
#' @title Plot results of a single SE model in a set
#'
#' @description Produce a figures for a specific SE model, as fit by
#' \code{\link{pkmSet}}
#'
#' @param modelSet pk model set of class \code{pkmSet}
#'
#' @param specificModel the name of the specific model for the plot
#'
#' @param app logical indicating if the plot is for the app
#'
#' @param cols named vector of colors to use for the specific and reference
#' models
#'
#' @return a plot
#'
#' @export
#'
plotSEFigure <- function(modelSet, specificModel, app, cols){
plotSEHeader(modelSet, specificModel, app, cols)
plotSEBoxPlots(modelSet, specificModel, cols)
plotSEBoxTemplate(modelSet, specificModel, cols)
plotSECells(modelSet, specificModel, cols)
}
#' @title The SE plot header
#'
#' @description Produce the header for an SE plot
#'
#' @param modelSet pk model set of class pkmSet
#'
#' @param specificModel the name of the specific model for the plot
#'
#' @param app logical indicating if the plot is for the app
#'
#' @param cols named vector of colors to use for the specific and reference
#' models
#'
#' @return a plot
#'
#' @export
#'
plotSEHeader <- function(modelSet, specificModel, app = FALSE,
cols = SEcols()){
par(mar = c(0, 0, 0, 0))
par(fig = c(0, 1, 0.935, 1))
plot(1, 1, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n', xlab = "",
ylab = "", ylim = c(0, 1), xlim = c(0, 1)
)
LL <- sapply(modelSet, "[[", "loglik")
referenceMod <- names(modelSet)[which(LL == max(LL))]
if (app){
specificModel <- gsub("~ 1", "~ constant", specificModel)
referenceMod <- gsub("~ 1", "~ constant", referenceMod)
}
rect(0.01, 0.725, 0.06, 0.925, lwd = 2, col = cols["spec"], border = NA)
text_model <- paste("= Selected Model: ", specificModel, sep = "")
text(x = 0.07, y = 0.85, text_model, adj = 0, cex = 0.9)
rect(0.01, 0.325, 0.06, 0.525, lwd = 2, col = cols["ref"], border = NA)
text_model <- paste("= Reference Model: ", referenceMod, sep = "")
text(x = 0.07, y = 0.45, text_model, adj = 0, cex = 0.9)
labelsText <- paste(modelSetPredictors(modelSet), collapse = ".")
labelsText[labelsText == ""] <- "all"
text_label <- paste("Labels: ", labelsText, sep = "")
text(x = 0.9, y = 0.8, text_label, adj = 1, cex = 0.75)
}
#' @title p and k box plots for an SE model set
#'
#' @description Plot parameter box plots for each cell within a model for
#' both p and k with comparison to the cellwise model
#'
#' @param modelSet modelSet of class pkmSet
#'
#' @param specificModel name of the specific submodel to plot
#'
#' @param cols named vector of colors to use for the specific and reference
#' models
#'
#' @return a set of parameter plot panels
#'
#' @export
#'
plotSEBoxPlots <- function(modelSet, specificModel, cols){
par(mar = c(0,0,0,0))
par(fig = c(0, 0.45, 0.7, 0.965), new = TRUE)
pkmSetSpecParamPlot(modelSet, specificModel, "p", cols)
par(fig = c(0.45, 0.9, 0.7, 0.965), new = TRUE)
if (!grepl("k not estimated", specificModel)){
pkmSetSpecParamPlot(modelSet, specificModel, "k", cols)
}
}
#' @title p or k box plots for an SE model set
#'
#' @description Plot parameter box plots for each cell within a model for
#' either p or k with comparison to the cellwise model
#'
#' @param modelSet modelSet of class pkmSet
#'
#' @param specificModel name of the specific submodel to plot
#'
#' @param pk character of "p" or "k" to delineate between parameter graphed
#'
#' @param cols named vector of colors to use for the specific and reference
#' models
#'
#' @return a specific parameter plot panel
#'
#' @export
#'
pkmSetSpecParamPlot <- function(modelSet, specificModel, pk = "p", cols){
model_spec <- modelSet[[specificModel]]
model_ref <- refMod(modelSet)
CL <- model_ref$CL
probs <- c(0, (1 - CL) / 2, 0.25, 0.5, 0.75, 1 - (1 - CL) / 2, 1)
observations_spec <- model_spec$observations
observations_ref <- model_ref$observations
ncell_spec <- model_spec$ncell
ncell_ref <- model_ref$ncell
cellNames_ref <- model_ref$cells[ , "CellNames"]
predictors_spec <- model_spec$predictors
predictors_ref <- model_ref$predictors
if (any(grepl("k not estimated", specificModel))){
return(1)
}
pks_spec <- rpk(n = 1000, model = model_spec)
pks_ref <- rpk(n = 1000, model = model_ref)
# kIncluded <- !any(grepl("k not estimated", specificModel))
# if (kIncluded){
# pks_spec <- rpk(n = 1000, model = model_spec)
# pks_ref <- rpk(n = 1000, model = model_ref)
# } else{
# pks_spec <- rpk(n = 1000, model = model_spec)
# pks_ref <- rpk(n = 1000, model = model_ref)
# }
cellMatch_spec <- matchCells(model_spec, modelSet)
cellMatch_ref <- matchCells(model_ref, modelSet)
cells_set <- modelSetCells(modelSet)
cellNames_set <- cells_set$CellNames
ncell_set <- nrow(cells_set)
if (pk == "p"){
maxy <- 1
} else if (pk == "k"){
maxy <- 1
for (celli in 1:ncell_set){
maxcell_spec <- max(pks_spec[[cellMatch_spec[celli]]][ , "k"]) * 1.01
maxcell_ref <- max(pks_ref[[cellMatch_ref[celli]]][ , "k"]) * 1.01
maxy <- max(c(maxy, maxcell_spec, maxcell_ref))
}
}
maxy[is.na(maxy)] <- 1
par(mar = c(4,3,2,1))
plot(1, type = "n", xlab = "", ylab = "", bty = "L", xaxt = 'n', yaxt = 'n',
ylim = c(0, maxy), xlim = c(0.5, ncell_set + 0.5)
)
for (celli in 1:ncell_set){
cMi_s <- cellMatch_spec[celli]
cMi_f <- cellMatch_ref[celli]
x_s <- celli - 0.2
y_s <- quantile(pks_spec[[cMi_s]][ , pk], probs, na.rm = TRUE)
x_f <- celli + 0.2
y_f <- quantile(pks_ref[[cMi_f]][ , pk], probs, na.rm = TRUE)
med <- c(-0.1, 0.1)
tb <- c(-0.07, 0.07)
rect(x_s - 0.1, y_s[3], x_s + 0.1, y_s[5], lwd = 1, border = cols["spec"])
points(x_s + med, rep(y_s[4], 2), type = 'l', lwd = 1, col = cols["spec"])
points(x_s + tb, rep(y_s[2], 2), type = 'l', lwd = 1, col = cols["spec"])
points(x_s + tb, rep(y_s[6], 2), type = 'l', lwd = 1, col = cols["spec"])
points(rep(x_s, 3), y_s[1:3], type = 'l', lwd = 1, col = cols["spec"])
points(rep(x_s, 3), y_s[5:7], type = 'l', lwd = 1, col = cols["spec"])
rect(x_f - 0.1, y_f[3], x_f + 0.1, y_f[5], lwd = 1, border = cols["ref"])
points(x_f + med, rep(y_f[4], 2), type = 'l', lwd = 1, col = cols["ref"])
points(x_f + tb, rep(y_f[2], 2), type = 'l', lwd = 1, col = cols["ref"])
points(x_f + tb, rep(y_f[6], 2), type = 'l', lwd = 1, col = cols["ref"])
points(rep(x_f, 3), y_f[1:3], type = 'l', lwd = 1, col = cols["ref"])
points(rep(x_f, 3), y_f[5:7], type = 'l', lwd = 1, col = cols["ref"])
}
axis(1, at = 1:ncell_set, labels = FALSE, tck = -0.05)
ang <- 0
offy <- -0.25
offx <- NULL
if (ncell_set > 3){
ang <- 35
offx <- 1
}
xcex <- 0.75
if (ncell_set > 6){
xcex <- 0.5
offy <- -0.125
}
text(1:ncell_set, offy, srt = ang, adj = offx, labels = cellNames_set,
xpd = TRUE, cex = xcex
)
axis(2, at = seq(0, 1, 0.5), las = 1, cex.axis = 0.7)
axis(2, at = seq(0, 1, 0.1), labels = FALSE, tck = -0.015)
mtext(side = 2, pk, line = 2.2, cex = 1.125)
}
#' @title template box plot
#'
#' @description Plot template box plot
#'
#' @param modelSet modelSet of class pkmSet
#'
#' @param specificModel name of the specific submodel to plot
#'
#' @param cols named vector of colors to use for the specific and reference
#' models
#'
#' @return a template box plot
#'
#' @export
#'
plotSEBoxTemplate <- function(modelSet, specificModel, cols){
model_spec <- modelSet[[specificModel]]
col_spec <- cols["spec"]
par(mar = c(0,0,0,0))
par(fig = c(0.92, 1, 0.8, 0.95), new = TRUE)
plot(1,1, type = "n", bty = "n", xaxt = "n", yaxt = "n", xlab = "",
ylab = "", ylim = c(0, 1), xlim = c(0, 1)
)
x_s <- 0.1
CL_split <- (1 - model_spec$CL) / 2
probs_y <- c(0, CL_split, 0.25, 0.5, 0.75, 1 - CL_split, 1)
set.seed(12)
y_s <- quantile(rnorm(1000, 0.5, 0.15), probs = probs_y)
med <- c(-0.1, 0.1)
tb <- c(-0.07, 0.07)
rect(x_s - 0.1, y_s[3], x_s + 0.1, y_s[5], lwd = 1, border = col_spec)
points(x_s + med, rep(y_s[4], 2), type = 'l', lwd = 1, col = col_spec)
points(x_s + tb, rep(y_s[2], 2), type = 'l', lwd = 1, col = col_spec)
points(x_s + tb, rep(y_s[6], 2), type = 'l', lwd = 1, col = col_spec)
points(rep(x_s, 3), y_s[1:3], type = 'l', lwd = 1, col = col_spec)
points(rep(x_s, 3), y_s[5:7], type = 'l', lwd = 1, col = col_spec)
num_CL <- c(CL_split, 1 - CL_split) * 100
text_CL <- paste(num_CL, "%", sep = "")
text_ex <- c("min", text_CL[1], "25%", "50%", "75%", text_CL[2], "max")
text(x_s + 0.2, y_s, text_ex, cex = 0.6, adj = 0)
}
#' @title Plot the cellwise results of a single model in a set of SE models
#'
#' @description Produce a set of cellwise figures for a specific SE model, as
#' fit by \code{\link{pkmSet}}
#'
#' @param modelSet pk model set of class pkmSet
#'
#' @param specificModel the name of the specific model for the plot
#'
#' @param cols named vector of colors to use for the specific and reference
#' models
#'
#' @return a plot
#'
#' @export
#'
plotSECells <- function(modelSet, specificModel, cols){
model_ref <- refMod(modelSet)
par(fig = c(0, 1, 0, 0.65), new = TRUE, mar = c(1, 1, 1, 1))
plot(1,1, type = "n", bty = "n", xaxt = "n", yaxt = "n", xlab = "",
ylab = ""
)
mtext(side = 1, "Search", line = -0.25, cex = 1.5)
mtext(side = 2, "Searcher Efficiency", line = -0.25, cex = 1.5)
cells_set <- modelSetCells(modelSet)
ncell <- nrow(cells_set)
cellNames <- cells_set[ , "CellNames"]
nmatrix_col <- min(3, ncell)
nmatrix_row <- ceiling(ncell / nmatrix_col)
figxspace <- 0.95 / nmatrix_col
figyspace <- 0.65 / nmatrix_row
x1 <- rep(figxspace * ((1:nmatrix_col) - 1), nmatrix_row) + 0.035
x2 <- rep(figxspace * ((1:nmatrix_col)), nmatrix_row) + 0.035
y1 <- rep(figyspace * ((nmatrix_row:1) - 1), each = nmatrix_col) + 0.03
y2 <- rep(figyspace * ((nmatrix_row:1)), each = nmatrix_col) + 0.03
bottomCells <- seq(ncell - (nmatrix_col - 1), ncell, 1)
leftCells <- which(1:ncell %% nmatrix_col == 1)
if (length(leftCells) == 0){
leftCells <- 1
}
for (celli in 1:ncell){
par(mar = c(2.5, 2, 0, 0))
par(fig = c(x1[celli], x2[celli], y1[celli], y2[celli]), new = TRUE)
specificCell <- cellNames[celli]
axis_x <- FALSE
axis_y <- FALSE
if (celli %in% bottomCells){
axis_x <- TRUE
}
if (celli %in% leftCells){
axis_y <- TRUE
}
axes <- c("x" = axis_x, "y" = axis_y)
pkmSetSpecSECellPlot(modelSet, specificModel, specificCell, cols, axes)
}
}
#' Plot cell-specific decay curve for searcher efficiency for a specific model
#' with comparison to the cellwise model
#'
#' @param modelSet modelSet of class pkmSet
#'
#' @param specificModel name of the specific submodel to plot
#'
#' @param specificCell name of the specific cell to plot
#'
#' @param cols named vector of colors to use for the specific and reference
#' models
#'
#' @param axes named vector of logical values indicating whether or not to
#' plot the x axis and the y axis
#'
#' @return a specific cell plot panel
#'
#' @export
#'
pkmSetSpecSECellPlot <- function(modelSet, specificModel, specificCell,
cols, axes){
model_spec <- modelSet[[specificModel]]
model_ref <- refMod(modelSet)
cellwise_spec <- model_spec$cell_pk
cellwise_ref <- model_ref$cell_pk
cellNames_spec <- model_spec$cells[ , "CellNames"]
cellNames_ref <- model_ref$cells[ , "CellNames"]
cells_set <- modelSetCells(modelSet)
preds_set <- modelSetPredictors(modelSet)
carcCells <- apply(data.frame(model_spec$data0[ , preds_set]),
1, paste, collapse = "."
)
whichCarcs <- which(carcCells == specificCell)
if (specificCell == "all"){
whichCarcs <- 1:length(carcCells)
}
observations <- as.matrix(model_spec$observations[whichCarcs, ],
nrow = length(whichCarcs),
ncol = ncol(model_spec$observations)
)
nobs <- ncol(observations)
ncarc <- nrow(observations)
carcFound <- apply(observations, 2, sum, na.rm = TRUE)
carcUnavail <- apply(apply(observations, 2, is.na), 2, sum)
carcAvail <- ncarc - carcUnavail
if (any(grepl("k not estimated", specificModel))){
return(1)
}
pks_spec <- rpk(n = 1000, model = model_spec)
pks_ref <- rpk(n = 1000, model = model_ref)
# kIncluded <- !any(grepl("k not estimated", specificModel))
# if (kIncluded){
# pks_spec <- rpk(n = 1000, model = model_spec)
# pks_ref <- rpk(n = 1000, model = model_ref)
# } else{
# pks_spec <- rpk(n = 1000, model = model_spec, kFill = 1)
# pks_ref <- rpk(n = 1000, model = model_ref, kFill = 1)
# }
cellMatch_spec <- matchCells(model_spec, modelSet)
cellMatch_ref <- matchCells(model_ref, modelSet)
cells_set <- modelSetCells(modelSet)
cellNames_set <- cells_set$CellNames
whichSpecificCell_spec <- cellMatch_spec[cellNames_set == specificCell]
whichSpecificCell_ref <- cellMatch_ref[cellNames_set == specificCell]
ps_spec <- pks_spec[[whichSpecificCell_spec]][ , "p"]
ks_spec <- pks_spec[[whichSpecificCell_spec]][ , "k"]
ps_ref <- pks_ref[[whichSpecificCell_ref]][ , "p"]
ks_ref <- pks_ref[[whichSpecificCell_ref]][ , "k"]
searchTab <- matrix(1:nobs, nrow = 1000, ncol = nobs, byrow = TRUE)
ktab_spec <- ks_spec^(searchTab - 1)
ktab_ref <- ks_ref^(searchTab - 1)
SE_spec <- ps_spec * ktab_spec
SE_ref <- ps_ref * ktab_ref
y_spec <- apply(SE_spec, 2, median)
y_ref <- apply(SE_ref, 2, median)
x_pts <- 1:nobs
y_pts <- carcFound / carcAvail
plot(x_pts, y_pts, ylim = c(0, 1.1), xlim = c(0.5, nobs + 0.5), xlab = "",
ylab = "", xaxt = "n", yaxt = "n", bty = "L", col = rgb(0.02, 0.02, 0.02),
lwd = 2, pch = 1, cex = 1.5
)
points(x_pts, y_ref, type = 'l', lwd = 3, col = cols["ref"])
points(x_pts, y_spec, type = 'l', lwd = 3, col = cols["spec"])
for (obi in 1:nobs){
x1 <- x_pts[obi] - 0.25
y1 <- y_pts[obi] + 0.035
x2 <- x_pts[obi] + 0.35
y2 <- y_pts[obi] + 0.11
rect(x1, y1, x2, y2, border = NA, col = "white")
}
obsLabels <- paste(carcFound, carcAvail, sep = "/")
text(x_pts + 0.05, y_pts + 0.075, obsLabels, cex = 0.65)
axis(1, at = x_pts, las = 1, cex.axis = 0.75, labels = axes["x"])
axis(2, at = seq(0, 1, 0.2), las = 1, cex.axis = 0.75, labels = axes["y"])
text(0.5, 1.1, specificCell, adj = 0, cex = 0.75, font = 2, xpd = TRUE)
}
#' @title Produce a named vectory with standard SE plot colors
#'
#' @description Produce a named vectory with standard SE plot colors
#'
#' @export
#'
SEcols <- function(){
c(spec = "black", ref = "grey")
}
#' @title Error check a specific model selection for an SE plot
#'
#' @description Make sure it's available and good, update the name for usage
#'
#' @param modelSet pk model set of class pkmSet
#'
#' @param specificModel the name of the specific model for the plot
#'
#' @return updated name of the model to use
#'
#' @export
#'
checkSpecificModelSE <- function(modelSet, specificModel){
if (!is.null(specificModel) && anyNA(specificModel)){
stop(
"specificModel must be NULL or a vector of model names or positions.",
"\nNAs not allowed."
)
}
if (length(specificModel) > 0){
if (is.numeric(specificModel)){
if (anyNA(specificModel)){
warning("specificModel cannot be NA. NA models removed.")
specificModel <- specificModel[!is.na(specificModel)]
if (length(specificModel) == 0){
stop("No valid specificModel")
}
}
if (any(specificModel > length(modelSet))){
stop(paste0("there are only ", length(modelSet), " model choices."))
}
specificModel <- names(modelSet)[specificModel]
}
if (any(specificModel %in% names(modelSet)) == FALSE){
stop("Selected model not in set. To see options use names(modelSet).")
}
modNames <- specificModel
for (modi in modNames){
if (pkmFail(modelSet[[modi]])){
stop("specificModel ", modi, " is not a well-fit pk model")
}
}
} else{
specificModel <- names(pkmSetFailRemove(modelSet))
}
return(specificModel)
}
#' @title Tidy an SE model set
#'
#' @description Remove bad fit models
#'
#' @param modelSet pk model set of class pkmSet
#'
#' @return a trimmed model set
#'
#' @export
#'
tidyModelSetSE <- function(modelSet){
modelSet <- pkmSetFailRemove(modelSet)
modelSet <- modelSet[order(sapply(modelSet, "[[", "AICc"))]
class(modelSet) <- c("pkmSet", "list")
return(modelSet)
}
|
a2f79ef755aae6a40a235d5fd9cd811bd7712d21 | b3547e9c11de7bea45121be5d475282de7ca0915 | /BayesSUR/R/plotGraph.R | 799bbe7df3815c1b52f3c873c11b499253680ecb | [] | permissive | ocbe-uio/BayesSUR | e0fbfb48777059d8ad094707406a7c56d322b268 | a0806225eba05a835cb1d8042e6749d8a2c9ba00 | refs/heads/master | 2021-12-10T07:03:55.945054 | 2021-11-30T08:40:14 | 2021-11-30T08:40:14 | 220,016,529 | 0 | 0 | MIT | 2020-10-21T12:09:17 | 2019-11-06T14:32:02 | C++ | UTF-8 | R | false | false | 3,803 | r | plotGraph.R | #' @title plot the estimated graph for multiple response variables
#' @description
#' Plot the estimated graph for multiple response variables from a \code{BayesSUR} class object.
#' @importFrom igraph E plot.igraph graph_from_adjacency_matrix
#' @importFrom graphics par
#' @name plotGraph
#' @param x either an object of class \code{BayesSUR} (default) or a symmetric numeric matrix representing an adjacency matrix for a given graph structure.
#' If x is an adjacency matrix, argument \code{main="Given graph of responses"} by default.
#' @param Pmax a value for thresholding the learning structure matrix of multiple response variables. Default is 0.5
#' @param main an overall title for the plot
#' @param edge.width edge width. Default is 2
#' @param edge.weight draw weighted edges after thresholding at 0.5. The default value \code{FALSE} is not to draw weighted edges
#' @param vertex.label character vector used to label the nodes
#' @param vertex.label.color label color. Default is \code{"black"}
#' @param vertex.size node size. Default is 30
#' @param vertex.color node color. Default is \code{"dodgerblue"}
#' @param vertex.frame.color node color. Default is \code{"NA"}
#' @param ... other arguments
#'
#' @examples
#' data("exampleEQTL", package = "BayesSUR")
#' hyperpar <- list( a_w = 2 , b_w = 5 )
#'
#' set.seed(9173)
#' fit <- BayesSUR(Y = exampleEQTL[["blockList"]][[1]],
#' X = exampleEQTL[["blockList"]][[2]],
#' data = exampleEQTL[["data"]], outFilePath = tempdir(),
#' nIter = 100, burnin = 50, nChains = 2, gammaPrior = "hotspot",
#' hyperpar = hyperpar, tmpFolder = "tmp/" )
#'
#' ## check output
#' # show the graph relationship between responses
#' plotGraph(fit, estimator = "Gy")
#'
#' @export
plotGraph <- function(x, Pmax=0.5, main = "Estimated graph of responses", edge.width=2, edge.weight=FALSE, vertex.label=NULL, vertex.label.color="black", vertex.size=30, vertex.color="dodgerblue", vertex.frame.color=NA, ...){
if (!inherits(x, "BayesSUR")){
if( is.matrix(x) & is.numeric(x) ){
if( !((dim(x)[1]==dim(x)[2]) & (sum(dim(x))>2)) )
stop("Use only with a \"BayesSUR\" object or numeric square matrix")
Gy_hat <- x
if(!is.null(vertex.label))
rownames(Gy_hat) <- colnames(Gy_hat) <- vertex.label
if( main=="Estimated graph of responses" )
main <- "Given graph of responses"
}else{
stop("Use only with a \"BayesSUR\" object or numeric square matrix")
}
}else{
x$output[-1] <- paste(x$output$outFilePath,x$output[-1],sep="")
covariancePrior <- x$input$covariancePrior
if(covariancePrior == "HIW"){
Gy_hat <- as.matrix( read.table(x$output$Gy) )
}else{
stop("Gy is only estimated with hyper-inverse Wishart prior for the covariance matrix of responses!")
}
if(!is.null(vertex.label)){
rownames(Gy_hat) <- colnames(Gy_hat) <- vertex.label
}else{
rownames(Gy_hat) <- colnames(Gy_hat) <- names(read.table(x$output$Y,header=T))
}
}
if( Pmax<0 | Pmax>1 )
stop("Please specify correct argument 'Pmax' in [0,1]!")
if(edge.weight){
Gy_thresh <- Gy_hat
Gy_thresh[Gy_hat<=Pmax] <- 0
}else{
Gy_thresh <- as.matrix( Gy_hat > Pmax )
}
net <- graph_from_adjacency_matrix( Gy_thresh, weighted=T, mode="undirected", diag=F)
if( edge.weight ){
plot.igraph(net, main = main, edge.width=E(net)$weight*2, vertex.label=vertex.label, vertex.color=vertex.color, vertex.frame.color=vertex.frame.color, ...)
}else{
plot.igraph(net, main = main, edge.width=edge.width, vertex.label=vertex.label, vertex.color=vertex.color, vertex.frame.color=vertex.frame.color, vertex.label.color=vertex.label.color, vertex.size=vertex.size, ...)
}
}
|
0d14d05fb94a70281a1925b122cdd04e698904dd | d47833e60e3b9760619cf9c348d97b188f342db3 | /MobileNetworkDataSimulationTemplate/code/src/inference/man/computeInitialPopulation.Rd | ebda7dc090cddd1bf08e2c3cc7353918109c01c0 | [] | no_license | Lorencrack3/TFG-Lorenzo | 1a8ef9dedee45edda19ec93146e9f7701d261fbc | e781b139e59a338d78bdaf4d5b73605de222cd1c | refs/heads/main | 2023-06-04T00:23:16.141485 | 2021-06-29T21:31:25 | 2021-06-29T21:31:25 | 364,060,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,409 | rd | computeInitialPopulation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/computeInitialPopulation.R
\name{computeInitialPopulation}
\alias{computeInitialPopulation}
\title{Computes the distribution of the population count at initial time instant.}
\usage{
computeInitialPopulation(
nnet,
params,
popDistr,
rndVal = FALSE,
ciprob = NULL,
method = "ETI"
)
}
\arguments{
\item{nnet}{The random values generated with \code{aggregation} package for the number of individuals detected by the
network.}
\item{params}{The parameters of the distribution. It should be a data.table object with the following columns:
\code{region, omega1, omega2, pnrRate, regionArea_km2, N0, dedupPntRate, alpha, beta, theta, zeta, Q}.}
\item{popDistr}{The distribution to be used for population count. This parameter could have one of the following
values: \code{NegBin} (negative binomial distribution), \code{BetaNegBin} (beta negative binomial distribution) or
\code{STNegBin} (state process negative binomial distribution).}
\item{rndVal}{If FALSE the result return by this function will be a list with a single element, a data.table object
with the following columns: \code{region, Mean, Mode, Median, SD, Min, Max, Q1, Q3, IQR, CV, CI_LOW, CI_HIGH}. If
TRUE the list will have a second element which is a data.table object containing the random values generated for
each region.}
\item{ciprob}{Value of probability of the CI (between 0 and 1) to be estimated. If NULL the default value is 0.89.}
\item{method}{The method to compute credible intervals. It could have 2 values, 'ETI' or 'HDI'. The default value is
'ETI.}
}
\value{
A list object with one or two elements. If rndVal is FALSE the list will have a single element with
descriptive statistics for the population count, which is a data.table object with the following columns:
\code{region, Mean, Mode, Median, Min, Max, Q1, Q3, IQR, SD, CV, CI_LOW, CI_HIGH}. If rndVal is TRUE the list will
have a second element which is a data.table object containing the random values generated for each region. The name
of the two list elements giving the descriptive statistics and random values for time t are 'stats' and
'rnd_values'.
}
\description{
Computes the distribution of the population count at initial time instant using one of the three
distributions: Negative Binomial, Beta Negative Binomial or State Process Negative Binomial.
}
|
457639b37ccfab993a16b42be00cf159c6426700 | f9ea199b6dd4611ede80ef2e7311e401291db602 | /plot_example.R | ebc7715693fb64b824d0ec581bceca2ec39169e7 | [] | no_license | owenqiz/miscellaneous | 1e47bae86268e718710a4df6200f3982e41eb93b | 3dc3470bb1acc6401da3a89941aa14c9a545ab74 | refs/heads/master | 2023-04-13T00:09:13.275915 | 2023-04-05T06:08:10 | 2023-04-05T06:08:10 | 150,648,172 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 160 | r | plot_example.R | plotm <- function(m){
# pause between plots
par(ask = TRUE)
hist(m)
plot(density(m))
image(m)
}
plotm(matrix(rnorm(100), ncol = 10))
|
724f74ad488602bb7f7c8bd361764a7b84884eaa | a5978fdc5e4e2400e8042db708db07b01e383346 | /tests/testthat/test_format_acct.R | dc89f7f4abeee48c59a96516e6a31f384bc4f248 | [] | no_license | willbradley/acctR | eea045fe50446eed133e26d4b1d1bf20e240a13c | 82c5f97161cd4b9e1a1bf3079e56bb00303d0d2b | refs/heads/master | 2021-09-05T11:25:07.123276 | 2018-01-26T22:15:44 | 2018-01-26T22:15:44 | 119,079,203 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 377 | r | test_format_acct.R | # Test that format_acct works!
x <- c(1000, -1000, NA)
test_that("format_acct returns expected output", {
expect_error(format_acct('test'))
expect_true(any(is.na(format_acct(x))))
expect_true(identical(format_acct(x[1]), "$1,000.00"))
expect_true(identical(format_acct(x[2]), "$(1,000.00)"))
expect_true(identical(format_acct(x[1], shown.in = 'k'), "$1.00K"))
})
|
68886f05344f66a7e9e99e11456ce475444d9afb | 2dcd0a1e101abdd50e72d7f37b803c10a41cc659 | /src/final/psth.r | 9a39aa9a50719ef9ab941ec3ce6ba12d3d2c241a | [
"MIT"
] | permissive | Abraham-newbie/Life-Events | 147a9c3eb873a3ae5b640a5f2fe6b9878ff99822 | 80a6ef45833edb0e5b99530bcbdeac58a8698d39 | refs/heads/master | 2023-06-24T12:43:26.953285 | 2021-07-07T12:44:40 | 2021-07-07T12:44:40 | 383,793,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,724 | r | psth.r | psth <- function(tb, eventnames, title_str) {
xlabels = c(
"pre24" = "-24", "pre12" = "-12", "post03" = "+3",
"post06" = "+6", "post09" = "+9", "post12" = "+12",
"post24" = "+24", "post36" = "+36", "post48" = "+48"
)
timelevels= c('pre36', 'pre24', 'pre12', 'post03', 'post06', 'post09',
'post12', 'post24', 'post36', 'post48')
tb %>%
filter(code %in% eventnames) %>%
group_by(code) %>%
summarise(N_min = min(n)) %>%
mutate(
note = paste('smallest n =', N_min),
outcome = 'min'
) -> annotates
# tb %>%
# mutate(n = ifelse(n > 1000, 1000, n)) -> tb
tb %>%
filter(code %in% eventnames) %>%
mutate(
outcome = recode(outcome, mcs = 'Affective', losat = 'Cognitive'),
term = factor(term, levels = timelevels, ordered = TRUE),
estimate=scale(estimate),
code = fct_relevel(code, eventnames)) %>%
ggplot(aes(x = term, y = estimate, group = outcome, colour = outcome)) +
geom_point() +
geom_line() +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_hline(yintercept = 0, alpha = 0.25) +
geom_vline(xintercept = 2.85, alpha = 0.25) +
scale_x_discrete(labels = xlabels) +
ylim(-10,10)+
facet_wrap(~ code) +
labs(title = title_str,
subtitle = 'Coefficients (sd units) at 90 percent confidence intervals',
y = '',
x = 'months pre and post life event') +
geom_text(data = annotates, aes(x = Inf, y = -Inf, label = note),
hjust = 1.1, vjust = -.75, colour = 1, size = 3) +
theme_light()+
theme(plot.title = element_text(face="bold"))+
expand_limits(y = 900000) # or some other arbitrarily large number
} |
3d687e0994b22eddaafb01655163492d671a8670 | 95494552a25d2250351e2d53bd8306947610a91d | /pwr-crossvalidation.R | bbe39d1b68c9a6e4d762bede42dc297bd68ffee9 | [] | no_license | lizzieinvancouver/pwr | aedd7b773465b26710cb3501fe836e5b270e4e5a | 3993a7535fb64f5db1698165a7dad4652c057001 | refs/heads/master | 2022-05-02T13:03:04.286970 | 2022-04-28T17:35:34 | 2022-04-28T17:35:34 | 143,559,117 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 11,152 | r | pwr-crossvalidation.R | # Davies et al. R code for PWR cross-validation (CV)
# Code includes f(x)s and CV of Arnell data
#
# By Jim Regetz (NCEAS)
# simple function to randomly partition indices 1:n into k equal sized
# groups (folds)
fold <- function(n, k) {
samp <- sample(n)
len <- unname(table(cut(seq.int(n), k)))
end <- cumsum(len)
mapply(
function(start, end, samp) {
samp[start:end]
},
start = c(1, head(end, -1)+1),
end = cumsum(len),
MoreArgs = list(samp=sample(n)),
SIMPLIFY = FALSE, USE.NAMES = FALSE
)
}
# cross-validation of pwr
pwr.cv <- function(formula, phy4d, wfun, bwidth, method, holdout) {
# extract training set from phy4d
phy.train <- phy4d[setdiff(seq(nTips(phy4d)), holdout)]
if (missing(bwidth)) {
bwidth <- get.opt.bw(formula, phy.train, wfun=wfun, method=method)
}
# get weights vectors for *all* species, but only keep rows
# corresponding to training set
wts.train <- getWts(phy4d, bwidth, wfun)[-holdout,]
# extract training data from phy4d
dat.train <- tipData(phy.train)
# loop over each point and do a weighted least squares regression
# with weights based on distance
yhat <- sapply(holdout, function(p) {
w <- wts.train[,p]
b <- lm(formula, data=data.frame(dat.train, w), weights=w)
predict(b, tipData(phy4d)[p,])
})
return(data.frame(
y=tipData(phy4d)[holdout, as.character(formula)[[2]]],
yhat.pwr=yhat)
)
}
pwr.cv.slope <- function(formula, phy4d, wfun, bwidth, method, holdout) {
# extract training set from phy4d
phy.train <- phy4d[setdiff(seq(nTips(phy4d)), holdout)]
if (missing(bwidth)) {
bwidth <- get.opt.bw(formula, phy.train, wfun=wfun, method=method)
}
# get weights vectors for *all* species, but only keep rows
# corresponding to training set
wts.train <- getWts(phy4d, bwidth, wfun)[-holdout,]
# extract training data from phy4d
dat.train <- tipData(phy.train)
# loop over each point and do a weighted least squares regression
# with weights based on distance
yhat <- sapply(holdout, function(p) {
w <- wts.train[,p]
b <- lm(formula, data=data.frame(dat.train, w), weights=w)
b$coefficients["x"]
})
return(data.frame(
slope=tipData(phy4d)[holdout, "true_slope"],
slope.pwr=yhat)
)
}
# cross-validation of pgls
pgls.cv <- function(formula, phy4d, holdout) {
# extract training set from phy4d
p4.train <- phy4d[setdiff(seq(nTips(phy4d)), holdout)]
phy.train <- suppressWarnings(as(p4.train, "phylo"))
# extract training data from phy4d
dat.train <- tipData(p4.train)
# loop over each point and do a weighted least squares regression
# with weights based on distance
pgls <- do.call(gls, list(model=formula, data=dat.train,
correlation=corBrownian(phy=phy.train)))
return(data.frame(
y=tipData(phy4d)[holdout, as.character(formula)[[2]]],
yhat.pgls=predict(pgls, tipData(phy4d)[holdout,]))
)
}
pgls.cv.slope <- function(formula, phy4d, holdout) {
# extract training set from phy4d
p4.train <- phy4d[setdiff(seq(nTips(phy4d)), holdout)]
phy.train <- suppressWarnings(as(p4.train, "phylo"))
# extract training data from phy4d
dat.train <- tipData(p4.train)
# loop over each point and do a weighted least squares regression
# with weights based on distance
pgls <- do.call(gls, list(model=formula, data=dat.train,
correlation=corBrownian(phy=phy.train)))
return(data.frame(
slope=tipData(phy4d)[holdout, "true_slope"],
slope.pgls=pgls$coefficients[[2]])
)
}
simcv <- function(pglsfit, sim=c("slope", "var1", "var2"), vcv, mc.cores) {
sim <- match.arg(sim, c("slope", "var1", "var2"))
# make sure target columns don't already exist
tipData(arn.p4d)$simSlope <- NULL
tipData(arn.p4d)$seed.sim <- NULL
tipData(arn.p4d)$ffd.sim <- NULL
if (sim=="slope") {
# extract global intercept (assumed constant in our simulation)
simInt <- coef(pglsfit)[[1]]
# use global slope estimate as root value for simulated forward
# evolution of this "trait", and add to tree data
simSlope <- rTraitCont(as(arn.p4d, "phylo"), sigma=2,
root.value=coef(pglsfit)[[2]])
arn.p4d <- addData(arn.p4d, data.frame(simSlope))
# generate simulated FFD
arn.p4d <- addData(arn.p4d, data.frame(ffd.sim=simInt +
tipData(arn.p4d)$simSlope * tipData(arn.p4d)$seed,
seed.sim=tipData(arn.p4d)$seed))
} else if (sim=="var1") {
require(geiger)
if (missing(vcv)) {
vcv <- ic.sigma(arnp, tipData(arn.p4d)[c("FFD", "seed")])
}
vars.sim <- sim.char(arnp, vcv)[,,1]
arn.p4d <- addData(arn.p4d, setNames(data.frame(vars.sim), c("ffd.sim",
"seed.sim")))
} else if (sim=="var2") {
require(phytools)
tipData(arn.p4d)$seed.sim <- fastBM(arnp, a=coef(gls(seed ~ 1,
data=tipData(arn.p4d), correlation=corBrownian(phy=arnp))))
tipData(arn.p4d)$ffd.sim <- c(cbind(1, tipData(arn.p4d)$seed.sim) %*%
coef(pglsfit)) + fastBM(arnp)
}
# 5-fold cross validation
k <- fold(nTips(arn.p4d), 5)
if (missing(mc.cores)) {
mc.cores <- min(length(k), 16)
}
# ...pgls
arn.pgls.cve <- mclapply(seq_along(k), function(fold) {
yhat <- pgls.cv(ffd.sim ~ seed.sim, arn.p4d, holdout=k[[fold]])
sqrt(mean(do.call("-", yhat)^2))
}, mc.cores=mc.cores)
# ...pwr
arn.pwr.cve <- mclapply(seq_along(k), function(fold) {
yhat <- pwr.cv(ffd.sim ~ seed.sim, arn.p4d, wfun="martins",
method="L-BFGS-B", holdout=k[[fold]])
sqrt(mean(do.call("-", yhat)^2))
}, mc.cores=mc.cores)
return(c(
sapply(c(mean.pgls=mean, sd.pgls=sd),
function(f) f(unlist(arn.pgls.cve))),
sapply(c(mean.pwr=mean, sd.pwr=sd),
function(f) f(unlist(arn.pwr.cve)))
))
}
#
# procedural code
#
stop("end of function definitions")
set.seed(99)
k <- matrix(sample(nTips(ap4d2)), ncol=5)
library(parallel)
# estimate expected generalization error using 5-fold CV
# ...pwr
pgls.cve <- mclapply(seq(ncol(k)), function(fold) {
yhat <- pgls.cv(ffd.sc ~ seed.sc, ap4d2, holdout=k[,fold])
sqrt(mean(do.call("-", yhat)^2))
})
# ...pgls
pwr.cve <- mclapply(seq(ncol(k)), function(fold) {
yhat <- pwr.cv(ffd.sc ~ seed.sc, ap4d2, wfun="martins",
method="L-BFGS-B", holdout=k[, fold])
sqrt(mean(do.call("-", yhat)^2))
})
sapply(c(mean=mean, sd=sd), function(f) f(unlist(pgls.cve)))
## mean sd
## 39.82692 2.74358
sapply(c(mean=mean, sd=sd), function(f) f(unlist(pwr.cve)))
## mean sd
## 29.19123 5.25956
# estimate expected generalization error using 5-fold CV
# ...pwr
arn.pgls.cve <- mclapply(seq(ncol(k)), function(fold) {
yhat <- pgls.cv(FFD ~ seed, arn.p4d, holdout=k[fold])
sqrt(mean(do.call("-", yhat)^2))
})
# ...pgls
arn.pwr.cve <- mclapply(seq(ncol(k)), function(fold) {
yhat <- pwr.cv(FFD ~ seed, arn.p4d, wfun="martins",
method="L-BFGS-B", holdout=k[fold])
sqrt(mean(do.call("-", yhat)^2))
})
sapply(c(mean=mean, sd=sd), function(f) f(unlist(arn.pgls.cve)))
## mean sd
## 39.82692 2.74358
sapply(c(mean=mean, sd=sd), function(f) f(unlist(arn.pwr.cve)))
## mean sd
## 29.19123 5.25956
# estimate expected generalization error using 10-fold CV
set.seed(99)
k <- fold(nTips(arn.p4d), 10)
# ...pgls
arn.pgls.cvp <- mclapply(seq_along(k), function(fold) {
yhat <- pgls.cv(FFD ~ seed, arn.p4d, holdout=k[[fold]])
yhat
}, mc.cores=min(length(k), 16))
arn.pgls.cve <- lapply(arn.pgls.cvp, function(y) {
sqrt(mean(do.call("-", y)^2))
})
sapply(c(mean=mean, sd=sd), function(f) f(unlist(arn.pgls.cve)))
## mean sd
## 20.698476 5.332476
mean(with(do.call(rbind, arn.pgls.cvp), (y-yhat.pgls)^2))
## [1] 454.0954
# ...pwr
arn.pwr.cvp <- mclapply(seq_along(k), function(fold) {
yhat <- pwr.cv(FFD ~ seed, arn.p4d, wfun="martins",
method="L-BFGS-B", holdout=k[[fold]])
yhat
}, mc.cores=min(length(k), 16))
arn.pwr.cve <- lapply(arn.pwr.cvp, function(y) {
sqrt(mean(do.call("-", y)^2))
})
sapply(c(mean=mean, sd=sd), function(f) f(unlist(arn.pwr.cve)))
## mean sd
## 18.491499 3.691288
mean(with(do.call(rbind, arn.pwr.cvp), (y-yhat.pwr)^2))
## [1] 353.5059
# estimate expected generalization error using 5-fold CV
set.seed(99)
k <- fold(nTips(arn.p4d), 5)
# ...pgls
arn.pgls.cve <- mclapply(seq_along(k), function(fold) {
yhat <- pgls.cv(FFD ~ seed, arn.p4d, holdout=k[[fold]])
sqrt(mean(do.call("-", yhat)^2))
}, mc.cores=min(length(k), 16))
# ...pwr
arn.pwr.cve <- mclapply(seq_along(k), function(fold) {
yhat <- pwr.cv(FFD ~ seed, arn.p4d, wfun="martins",
method="L-BFGS-B", holdout=k[[fold]])
sqrt(mean(do.call("-", yhat)^2))
}, mc.cores=min(length(k), 16))
sapply(c(mean=mean, sd=sd), function(f) f(unlist(arn.pgls.cve)))
## mean sd
## 21.097889 2.928884
# ... in-sample prediction error
sqrt(mean(resid(pgls.arn.brownian)^2))
## [1] 21.32004
sapply(c(mean=mean, sd=sd), function(f) f(unlist(arn.pwr.cve)))
## mean sd
## 18.453470 2.916869
# ... in-sample prediction error
sqrt(mean((mapply(function(coef, seed) coef["(Intercept)",
"Estimate"] + coef["seed", "Estimate"] * seed, pwr.arn.martins,
tipData(arn.p4d)$seed) - tipData(arn.p4d)$FFD)^2))
## [1] 8.879647
cve.sim <- mclapply(1:100, function(i) simcv(pgls.arn.brownian, mc.cores=1),
mc.cores=25)
system.time(cve.sim <- mclapply(1:100, function(i) simcv(pgls.arn.brownian, mc.cores=1),
mc.cores=25)
)
## user system elapsed
## 14191.143 10.849 643.536
system.time(cve.sim <- mclapply(1:100, function(i) simcv(pgls.arn.brownian, mc.cores=1),
mc.cores=25)
)
#
# simulate data using fastBM
#
library(phytools)
set.seed(99)
tipData(arn.p4d)$x <- fastBM(arnp, a=coef(gls(seed ~ 1, data=tipData(arn.p4d),
correlation=corBrownian(phy=arnp))))
tipData(arn.p4d)$y <- c(cbind(1, tipData(arn.p4d)$x) %*% coef(gls(FFD ~ seed,
data=tipData(arn.p4d), correlation=corBrownian(phy=arnp)))) + fastBM(arnp)
# ...pgls
arnBM.pgls.cve <- mclapply(seq_along(k), function(fold) {
yhat <- pgls.cv(y ~ x, arn.p4d, holdout=k[[fold]])
sqrt(mean(do.call("-", yhat)^2))
}, mc.cores=min(length(k), 16))
# ...pwr
arnBM.pwr.cve <- mclapply(seq_along(k), function(fold) {
yhat <- pwr.cv(y ~ x, arn.p4d, wfun="martins",
method="L-BFGS-B", holdout=k[[fold]])
sqrt(mean(do.call("-", yhat)^2))
}, mc.cores=min(length(k), 16))
arnBM.pgls.yhat <- do.call(rbind, mclapply(1:length(k), function(fold)
pgls.cv(y ~ x, arn.p4d, holdout=k[[fold]]), mc.cores=5))
arnBM.pwr.yhat <- do.call(rbind, mclapply(1:length(k), function(fold)
pwr.cv(y ~ x, arn.p4d, wfun="martins", method="L-BFGS-B",
holdout=k[[fold]]), mc.cores=5))
|
5570927927e7356701b139fb9653175790d13ccd | 3845f0cfcf3d2cb7abf8b358daf95f2ea5f8529c | /plot1.R | 323eb65333e8ebadf13b69a6c8d4b3301db945e4 | [] | no_license | wiseguy3257/ExData_Plotting1 | 869a1316f88763516972af5cae32b558637654ac | 521d888830f1b5912ee6199866cf3780c7d40777 | refs/heads/master | 2021-01-18T17:40:31.486906 | 2014-12-07T23:48:08 | 2014-12-07T23:48:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 429 | r | plot1.R | setwd("C:/Users/Chris/Desktop/R/exdata-data-household_power_consumption")
d = read.table("household_power_consumption.txt", sep=";", header=T,
na.strings="?", stringsAsFactors=FALSE)
str(d)
dat <- d[which(d$Date=='1/2/2007' | d$Date=='2/2/2007'),]
str(dat)
hist(dat$Global_active_power, col="red", xlab="Global Active Power (kilowatts)",
main="Global Active Power")
dev.copy(png, file="plot1.png")
dev.off()
|
d4a62e248001ab7577f6401b34abc01428731da2 | e5fc120f866933943a29c796c7c607dc2690cab3 | /analysis/ipm/ltre/sensitivity_mean_ipm.R | 0f9cc452ccd2728f9e32c548c211c5e780d0e3e6 | [] | no_license | AldoCompagnoni/lupine | e07054e7e382590d5fa022a23e024dfec80c80b2 | afc41a2b66c785957db25583f25431bb519dc7ec | refs/heads/master | 2021-06-23T04:50:30.617943 | 2021-06-11T13:00:59 | 2021-06-11T13:00:59 | 185,047,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,892 | r | sensitivity_mean_ipm.R | # Calculate sensitivities of the mean model
rm(list=ls())
source("analysis/format_data/format_functions.R")
options(stringsAsFactors = F)
library(dplyr)
library(tidyr)
library(ggplot2)
library(readxl)
library(testthat)
# data
lupine_df <- read.csv( "data/lupine_all.csv")
fruit_rac <- read_xlsx('data/fruits_per_raceme.xlsx')
seed_x_fr <- read_xlsx('data/seedsperfruit.xlsx')
pred_g <- read_xlsx('data/post predation_lupinus tidestromii.xlsx')
sl_size <- read.csv('results/ml_mod_sel/size_sl/seedl_size.csv')
clim <- read.csv("data/prism_point_reyes_87_18.csv")
enso <- read.csv("data/enso_data.csv")
germ <- read_xlsx('data/seedbaskets.xlsx') %>%
select(g0:g2) %>%
colMeans
germ_adj <- read.csv('results/ml_mod_sel/germ/germ_adj.csv')
# format climate data ----------------------------------------
years <- c(2005:2018)
m_obs <- 5
m_back <- 36
# calculate yearly anomalies
year_anom <- function(x, var ){
# set names of climate variables
clim_names <- paste0( var,c('_t0','_tm1','_t0_tm1','_t0_tm2') )
mutate(x,
avgt0 = x %>% select(V1:V12) %>% rowSums,
avgtm1 = x %>% select(V13:V24) %>% rowSums,
avgt0_tm1 = x %>% select(V1:V24) %>% rowSums,
avgt0_tm2 = x %>% select(V1:V36) %>% rowSums ) %>%
select(year, avgt0, avgtm1, avgt0_tm1, avgt0_tm2) %>%
setNames( c('year',clim_names) )
}
# format climate - need to select climate predictor first
ppt_mat <- subset(clim, clim_var == "ppt") %>%
prism_clim_form("precip", years, m_back, m_obs) %>%
year_anom('ppt')
tmp_mat <- subset(clim, clim_var == 'tmean') %>%
prism_clim_form('tmean', years, m_back, m_obs) %>%
year_anom('tmp')
enso_mat <- subset(enso, clim_var == 'oni' ) %>%
month_clim_form('oni', years, m_back, m_obs) %>%
year_anom('oni')
# put together all climate
clim_mat <- Reduce( function(...) full_join(...),
list(ppt_mat,tmp_mat,enso_mat) )
# format climate data ----------------------------------------
years <- c(2005:2018)
m_obs <- 5
m_back <- 36
# calculate yearly anomalies
year_anom <- function(x, var){
# set names of climate variables
clim_names <- paste0( var,c('_t0','_tm1','_t0_tm1','_t0_tm2') )
mutate(x,
avgt0 = x %>% select(V1:V12) %>% rowSums,
avgtm1 = x %>% select(V13:V24) %>% rowSums,
avgt0_tm1 = x %>% select(V1:V24) %>% rowSums,
avgt0_tm2 = x %>% select(V1:V36) %>% rowSums ) %>%
select(year, avgt0, avgtm1, avgt0_tm1, avgt0_tm2) %>%
setNames( c('year',clim_names) )
}
# format climate - need to select climate predictor first
ppt_mat <- subset(clim, clim_var == "ppt") %>%
prism_clim_form("precip", years, m_back, m_obs) %>%
year_anom('ppt')
tmp_mat <- subset(clim, clim_var == 'tmean') %>%
prism_clim_form('tmean', years, m_back, m_obs) %>%
year_anom('tmp')
enso_mat <- subset(enso, clim_var == 'oni' ) %>%
month_clim_form('oni', years, m_back, m_obs) %>%
year_anom('oni')
# put together all climate
clim_mat <- Reduce( function(...) full_join(...),
list(ppt_mat,tmp_mat,enso_mat) )
# vital rates format --------------------------------------------------------------
# first, format site/year combinations
site_df <- select(lupine_df, year, location) %>%
unique %>%
# create 'Site' column to merge with consumption dat
mutate( Site = gsub(' \\([0-9]\\)','',location) ) %>%
subset( year > 2004 ) %>%
arrange( location, year ) %>%
complete(location,year)
surv <- subset(lupine_df, !is.na(surv_t1) ) %>%
subset( area_t0 != 0) %>%
mutate( log_area_t0 = log(area_t0),
year = year ) %>%
mutate( log_area_t02 = log_area_t0^2,
log_area_t03 = log_area_t0^3) %>%
left_join( clim_mat )
grow <- lupine_df %>%
# remove sleedings at stage_t0
subset(!(stage_t0 %in% c("DORM", "NF")) &
!(stage_t1 %in% c("D", "NF", "DORM")) ) %>%
# remove zeroes from area_t0 and area_t1
subset( area_t0 != 0) %>%
subset( area_t1 != 0) %>%
mutate( log_area_t1 = log(area_t1),
log_area_t0 = log(area_t0),
log_area_t02 = log(area_t0)^2,
year = year ) %>%
left_join( clim_mat )
flow <- subset(lupine_df, !is.na(flow_t0) ) %>%
subset( area_t0 != 0) %>%
mutate( log_area_t0 = log(area_t0),
log_area_t02 = log(area_t0)^2,
year = year ) %>%
left_join( clim_mat )
fert <- subset(lupine_df, flow_t0 == 1 ) %>%
subset( area_t0 != 0) %>%
subset( !is.na(numrac_t0) ) %>%
# remove non-flowering individuals
subset( !(flow_t0 %in% 0) ) %>%
mutate( log_area_t0 = log(area_t0),
log_area_t02 = log(area_t0)^2,
year = year ) %>%
# remove zero fertility (becase fertility should not be 0)
# NOTE: in many cases, notab_t1 == 0, because numab_t1 == 0 also
subset( !(numrac_t0 %in% 0) ) %>%
left_join( clim_mat )
abor_df <- subset(lupine_df, !is.na(flow_t0) & flow_t0 == 1 ) %>%
subset( !is.na(numrac_t0) ) %>%
# remove non-flowering individuals
subset( !(flow_t0 %in% 0) ) %>%
# remove zero fertility (becase fertility should not be 0)
subset( !(numrac_t0 %in% 0) ) %>%
# only years indicated by Tiffany
subset( year %in% c(2010, 2011, 2013:2017) ) %>%
# calculate abortion rates
mutate( ab_r = numab_t0 / numrac_t0 ) %>%
group_by( location, year ) %>%
summarise( ab_r_m = mean(ab_r, na.rm=T) ) %>%
ungroup %>%
right_join( select(site_df,-Site) ) %>%
mutate( ab_r_m = replace(ab_r_m,
is.na(ab_r_m),
mean(ab_r_m,
na.rm=T)) )
cons_df <- read_xlsx('data/consumption.xlsx') %>%
mutate( Mean_consumption = Mean_consumption %>% as.numeric) %>%
select( Year, Site, Mean_consumption) %>%
# update contents/names of variables for merging
mutate( Site = toupper(Site) ) %>%
rename( year = Year ) %>%
# expand to all site/year combinations
right_join( site_df ) %>%
mutate( Mean_consumption = replace(Mean_consumption,
is.na(Mean_consumption),
mean(Mean_consumption,na.rm=T)
) ) %>%
# remove NA locations
subset( !is.na(location) ) %>%
# remove annoying code
select( -Site ) %>%
rename( cons = Mean_consumption ) %>%
arrange(location,year)
germ_df <- site_df %>%
select( location ) %>%
unique %>%
arrange( location ) %>%
left_join(germ_adj) %>%
# create germ_obs for AL (1)
mutate( germ_obs = replace( germ_obs,
location == 'AL (1)',
mean(germ_obs[location %in% c('ATT (8)',
'POP9 (9)')])) ) %>%
# create germ_obs for BR (6)
mutate( germ_obs = replace( germ_obs,
location == 'BR (6)',
mean(germ_obs[location %in% c('BS (7)',
'DR (3)')])) ) %>%
# post-dispersal predation
mutate( post_d_p = (germ['g0'] - germ_obs) / germ['g0'] ) %>%
# add germination rats
mutate( g0 = germ['g0'],
g1 = germ['g1'],
g2 = germ['g2'] )
# models ---------------------------------------------------------
mod_s <- glm(surv_t1 ~ log_area_t0 + log_area_t02 + log_area_t03 +
tmp_tm1, data=surv, family='binomial')
mod_g <- lm( log_area_t1 ~ log_area_t0, data=grow)
g_lim <- range(c(grow$log_area_t0, grow$log_area_t1))
mod_fl <- glm(flow_t0 ~ log_area_t0 + tmp_tm1, data=flow, family='binomial')
mod_fr <- glm(numrac_t0 ~ log_area_t0 + tmp_tm1, data=fert, family='poisson')
fr_rac <- glm(NumFruits ~ 1, data=fruit_rac, family='poisson')
seed_fr <- glm(SEEDSPERFRUIT ~ 1,
data=mutate(seed_x_fr,
# substitute 0 value with really low value (0.01)
SEEDSPERFRUIT = replace(SEEDSPERFRUIT,
SEEDSPERFRUIT == 0,
0.01) ),
family=Gamma(link = "log"))
# vital rate models
surv_p <- coef(mod_s)
grow_p <- coef(mod_g)
grow_p <- c(grow_p, summary(mod_g)$sigma)
flow_p <- coef(mod_fl)
fert_p <- coef(mod_fr)
size_sl_p <- sl_size
fr_rac_p <- coef(fr_rac) %>% exp
seed_fr_p <- coef(seed_fr) %>% exp
germ_p <- germ * (1 - 0.9)
cons_p <- subset(cons_df, location != 'AL (1)')$cons %>% mean(na.rm=T)
abor_p <- abor_df$ab_r_m %>% mean
# IPM parameters -------------------------------------------------------------
# function to extract values
extr_value <- function(x, field){ subset(x, type_coef == 'fixef' & ranef == field )$V1 }
# list of mean IPM parameters.
pars_mean <- list( # adults vital rates
surv_b0 = surv_p['(Intercept)'],
surv_b1 = surv_p['log_area_t0'],
surv_b2 = surv_p['log_area_t02'],
surv_b3 = surv_p['log_area_t03'],
surv_clim = surv_p['tmp_tm1'],
grow_b0 = grow_p['(Intercept)'],
grow_b1 = grow_p['log_area_t0'],
grow_sig = grow_p[3],
flow_b0 = flow_p['(Intercept)'],
flow_b1 = flow_p['log_area_t0'],
flow_clim = flow_p['tmp_tm1'],
fert_b0 = fert_p['(Intercept)'],
fert_b1 = fert_p['log_area_t0'],
fert_clim = fert_p['tmp_tm1'],
abort = abor_p,
clip = cons_p,
fruit_rac = fr_rac_p,
seed_fruit = seed_fr_p,
g0 = germ_p['g0'],
g1 = germ_p['g1'],
g2 = germ_p['g2'],
recr_sz = size_sl_p$mean_sl_size,
recr_sd = size_sl_p$sd_sl_size,
L = g_lim[1],
U = g_lim[2],
mat_siz_sl = 100,
mat_siz = 100 )
# test that no NAs present
expect_equal(pars_mean %>%
unlist %>%
is.na() %>%
sum, 0)
# IPM functions ------------------------------------------------------------------------------
inv_logit <- function(x){ exp(x)/(1+exp(x)) }
# Survival at size x
sx<-function(x,pars,tmp_anom){
# survival prob. of each x size class
inv_logit(pars$surv_b0 +
pars$surv_b1 * x +
pars$surv_b2 * x^2 +
pars$surv_b3 * x^3 +
pars$surv_clim * tmp_anom)
}
# update kernel functions
grow_sd <- function(x,pars){
pars$a*(exp(pars$b*x)) %>% sqrt
}
# growth (transition) from size x to size y
gxy <- function(y,x,pars){
# returns a *probability density distribution* for each x value
dnorm(y, mean = pars$grow_b0 + pars$grow_b1*x,
sd = pars$grow_sig)
}
# transition: Survival * growth
pxy<-function(y,x,pars,tmp_anom){
sx(x,pars,tmp_anom) * gxy(y,x,pars)
}
# production of seeds from x-sized mothers
fx <-function(x,pars,tmp_anom){
# total racemes prod
tot_rac <- inv_logit( pars$flow_b0 +
pars$flow_b1*x +
pars$flow_clim*tmp_anom ) *
exp( pars$fert_b0 +
pars$fert_b1*x +
pars$fert_clim*tmp_anom )
# viable racs
viab_rac <- tot_rac * (1- pars$abort) * (1-pars$clip)
# viable seeds
viab_sd <- viab_rac * pars$fruit_rac * pars$seed_fruit
return(viab_sd)
}
# Size distribution of recruits
recs <-function(y,pars){
dnorm(y, mean = pars$recr_sz, sd = pars$recr_sd )
}
fxy <- function(y,x,pars,tmp_anom){
fx(x,pars,tmp_anom) * recs(y,pars)
}
# IPM kernel/matrix ------------------------------------------------------------
kernel <- function(tmp_anom, pars){
# set up IPM domains --------------------------------------------------------
# plants
n <- pars$mat_siz
L <- pars$L
U <- pars$U
#these are the upper and lower integration limits
h <- (U-L)/n #Bin size
b <- L+c(0:n)*h #Lower boundaries of bins
y <- 0.5*(b[1:n]+b[2:(n+1)]) #Bins' midpoints
#these are the boundary points (b) and mesh points (y)
# populate kernel ------------------------------------------------------------
# seeds mini matrix
s_mat <- matrix(0,2,2)
# seeds that enter 2 yr-old seed bank
plant_s2 <- fx(y,pars,tmp_anom) * pars$g2
# seeds that enter 1 yr-old seed bank
plant_s1 <- fx(y,pars,tmp_anom) * pars$g1
# seeds that go directly to seedlings germinate right away
Fmat <- (outer(y,y, fxy, pars, tmp_anom) * pars$g0 * h)
# seeds that enter 2 yr-old seed bank
s_mat[2,1] <- 1
# recruits from the 1 yr-old seedbank
s1_rec <- h * recs(y, pars)
# recruits from the 2 yr-old seedbank
s2_rec <- h * recs(y, pars)
# survival and growth of adult plants
Tmat <- outer(y,y,pxy,pars,tmp_anom) * h
# rotate <- function(x) t(apply(x, 2, rev))
# outer(y,y, fxy, pars, h) %>% t %>% rotate %>% image
small_K <- Tmat + Fmat
# Assemble the kernel -------------------------------------------------------------
# top 2 vectors
from_plant <- rbind( rbind( plant_s2, plant_s1),
small_K )
# leftmost vectors
from_seed <- rbind( s_mat,
cbind(s1_rec, s2_rec) )
k_yx <- cbind( from_seed, from_plant )
return(k_yx)
# tests "integrating' functions ---------------------------------------------------
# s_sl
# expect_true( ((outer( rep(1,100), y_s, s_sl, pars, h_s) %>% t %>% colSums) > 0.99) %>% all )
# gxy
# expect_true( ((outer(y,y,gxy,pars)*h) %>% t %>% colSums > 0.97) %>% all)
# gxy_s: huge unintentional eviction. Why?
# expect_true( ((outer(y_s,y,gxy_s,pars)*h) %>% t %>% colSums > 0.97) %>% all)
}
ker <- kernel(0,pars_mean)
lambda <- Re(eigen(ker)$value[1])
nPar <- length(pars_mean) - 4
sPar <- numeric(nPar) # vector to hold parameter sensitivities
dp <- 0.01 # perturbation for calculating sensitivities
for(j in 1:nPar){
m.par <- pars_mean
m.par[[j]] <- m.par[[j]] - dp
IPM.down <- kernel(0.5,m.par)
lambda.down <- Re(eigen(IPM.down)$values[1])
m.par[[j]] <- m.par[[j]] + 2*dp
IPM.up <- kernel(0.5,m.par)
lambda.up <- Re(eigen(IPM.up)$values[1])
sj <- (lambda.up-lambda.down)/(2*dp)
sPar[j] <- sj
cat(j,names(pars_mean)[j],sj,"\n");
}
graphics.off(); dev.new(width=11,height=6);
par(mfrow=c(2,1),mar=c(4,2,2,1),mgp=c(2,1,0));
# graph the sensitivity
sens_df <- data.frame( parameter = names(pars_mean)[1:nPar],
sensitivity = sPar,
elasticity = sPar*abs(as.numeric(pars_mean[1:nPar]))/lambda ) %>%
gather( measure, value, sensitivity:elasticity)
ggplot(sens_df) +
geom_bar( aes( x = parameter,
y = value ),
stat = 'identity') +
theme( axis.text.x = element_text( angle = 80,
vjust = 0.5) ) +
facet_grid( measure ~ 1 ) +
ylab( 'Sensitivity/Elasticity') +
xlab( 'Parameter' ) +
ggsave( 'results/ipm/ltre/sens_elast.tiff',
width=6.3,height=6.3,compression='lzw')
# only plot elasticity
subset(sens_df, measure == 'elasticity') %>%
ggplot() +
geom_bar( aes( x = parameter,
y = value ),
stat = 'identity') +
theme( axis.text.x = element_text( angle = 80,
vjust = 0.5) ) +
ylab( 'Elasticity') +
xlab( 'Parameter' ) +
ggsave( 'results/ipm/ltre/elast.tiff',
width=6.3,height=6.3,compression='lzw')
|
c942bf00bedd981b6c57aca30631202524e9c934 | 70bd36f4dbbbdb101e853d135b34385ae7366161 | /squirrels.R | 24e29e94afdfaca98b7305f0824a6051ef0bbf76 | [
"MIT"
] | permissive | bbelcher97/mapping | 35560ad8b4067c2ff0ae3963d7ebc8ef87579920 | 3b33a3d323256c711d6d1db38c28f495d9e48828 | refs/heads/master | 2020-04-07T17:49:09.526748 | 2018-12-17T01:25:51 | 2018-12-17T01:25:51 | 158,584,918 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 163 | r | squirrels.R | library(spocc)
library(mapr)
squirrels <- occ(query='Sciuridae', from='gbif', limit=2500)
df = as.data.frame(squirrels$gbif$data$Sciuridae)
map_leaflet(squirrels) |
fb9c4b46d0cef314bb5a1418f7b21e7a6e2f66f5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Compositional/examples/diri.reg.Rd.R | 1db3b9832b54a7d0202ce73e472914abeabec4ed | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 343 | r | diri.reg.Rd.R | library(Compositional)
### Name: Dirichlet regression
### Title: Dirichlet regression
### Aliases: diri.reg diri.reg2
### Keywords: Dirichlet regression multivariate regression
### ** Examples
x <- as.vector(iris[, 4])
y <- as.matrix(iris[, 1:3])
y <- y / rowSums(y)
mod1 <- diri.reg(y, x)
mod2 <-diri.reg2(y, x)
mod3 <- comp.reg(y, x)
|
34d8b04a6586c0f7976bdf3f8e63e9ef90737cce | 1fe9c4fc4f4b3a193ee042c414bcd87c22fec4af | /Dealing_with_date-time_data/time-zones.R | d7c43ddefa9bdce635ae2d8c422cdc197bf23be6 | [
"MIT"
] | permissive | oucru-biostats/Data_management_and_basic_summaries_in_R | 949e6efd6e2dbe0c5d381aab2a6cfe087e4786a3 | 8153ee732eff1a3bc227cd5211ff30c357871e1f | refs/heads/main | 2023-03-19T04:17:51.909131 | 2021-03-03T02:47:39 | 2021-03-03T02:47:39 | 343,683,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 83 | r | time-zones.R | Time_Zones <- read.csv('time-zones.csv', stringsAsFactors = FALSE)
View(Time_Zones) |
056001e23a334d20e331a90e1e41c83407cc90cb | cbd96ff896c7c62ed6b4960e5011e14039e5e60c | /inst/tests/test-dmvn.R | 837d99c80e9ae79846babb3ecf6211c8a5101f55 | [] | no_license | mfasiolo/mvnfast | af016cca7eeed89beae71639dc895cca5e71d267 | 1f5aacbba23a6e1fd369cd963917298e5eb8b4a4 | refs/heads/master | 2023-07-06T00:59:49.298436 | 2023-06-26T15:52:42 | 2023-06-26T15:52:42 | 19,045,554 | 28 | 10 | null | 2023-06-26T15:52:44 | 2014-04-22T20:44:38 | R | UTF-8 | R | false | false | 1,377 | r | test-dmvn.R | context("dmvn() and maha()")
test_that("Checking dmvn() and maha() against dmvnorm() and mahalanobis", {
library("mvtnorm")
##########
###### d = 1, n = 1 case
##########
set.seed(4616)
N <- c(1, 100, 1, 100)
d <- c(1, 1, 10, 10)
message("Testing dmvn() and maha()")
for(ii in 1:length(N))
{
mu <- 1:d[ii]
tmp <- matrix(rnorm(d[ii]^2), d[ii], d[ii])
mcov <- tcrossprod(tmp, tmp)
myChol <- chol(mcov)
X <- rmvnorm(N[ii], mu, mcov)
##### dmvn()
bench <- dmvnorm(X, mu, mcov, log = T)
# Sequential
expect_lt(sum(abs(dmvn(X, mu, mcov, log = T) - bench)), 1e-6)
expect_lt(sum(abs(dmvn(X, mu, myChol, isChol = TRUE, log = T) - bench)), 1e-6)
# Parallel
expect_lt(sum(abs(dmvn(X, mu, mcov, ncores = 2, log = T) - bench)), 1e-6)
expect_lt(sum(abs(dmvn(X, mu, myChol, ncores = 2, isChol = TRUE, log = T) - bench)), 1e-6)
##### maha()
bench <- mahalanobis(X, mu, mcov)
# Sequential
expect_lt(sum(abs(maha(X, mu, mcov) - bench)), 1e-6)
expect_lt(sum(abs(maha(X, mu, myChol, isChol = TRUE) - bench)), 1e-6)
# Parallel
expect_lt(sum(abs(maha(X, mu, mcov, ncores = 2) - bench)), 1e-6)
expect_lt(sum(abs(maha(X, mu, myChol, ncores = 2, isChol = TRUE) - bench)), 1e-6)
message(paste("Test", ii, "passed."))
}
detach("package:mvtnorm", unload=TRUE)
}) |
4885395ce1534d0a44c29d973db1a762f6202304 | aae44f8e1422ae9d611bd27a4a7b9754f25a60ef | /ssm revision complete analyses for archiving.R | cf8fa632aecff79fcdd8f1580c67cff90a7bc5f3 | [] | no_license | adampepi/nonstationary_ecoletts | d36a9c45266c6faa2f3aa16a3cab822ab9d028f7 | 282be62639536eeede685c5163be509b190a068e | refs/heads/master | 2023-04-11T19:55:32.774473 | 2021-03-24T19:04:57 | 2021-03-24T19:04:57 | 351,192,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,307 | r | ssm revision complete analyses for archiving.R | ##Code for state space models and simulations for Pepi, Holyoak & Karban 2021, Ecology Letters
rm(list = ls())
library(rjags)
library(R2jags)
library(AICcmodavg)
library(lattice)
library(MCMCvis)
library(tidyverse)
library(tidybayes)
library(ggplot2)
library(ggstance)
library(bayestestR)
setwd("~/Documents/Research/dissertation/time series analyses/nonstationary_modelling")
cats1<-read.csv('bodega.cats2.csv')
str(cats1)
cats<-cats1
cats$Precip<-as.numeric(scale(cats$precip))
cats$cat.count<-as.integer(cats$lupine.count)
#First part
cats2<-cats1[1:19,]
cats2$Precip<-as.numeric(scale(cats2$precip))
cats2$cat.count<-as.integer(cats2$lupine.count)
str(cats2)
#Second part
cats3<-cats1[19:34,]
cats3$Precip<-as.numeric(scale(cats3$precip))
cats3$cat.count<-as.integer(cats3$lupine.count)
str(cats)
### Delayed DD Gompertz precip
#Full series
sink("tigermodelprecipgompdelayed.jags") # name of the txt file
cat("
model{
#Priors
mean.r[1] ~ dnorm(0, 0.1) # Prior for mean growth rate
mean.r[2] ~ dnorm(0, 0.1)
sigma.proc ~ dunif(0, 10) # Prior for sd of state process
sigma2.proc <- pow(sigma.proc, 2)
tau.proc <- pow(sigma.proc, -2)
beta_precip ~ dnorm(0,0.01)
beta_dd1 ~ dnorm(0,0.01)
beta_dd2 ~ dnorm(0,0.01)
beta_0 ~ dnorm(0, 0.01)
#State process
for (t in 3:T){
mean.r[t] <- beta_0 + beta_dd1 * logN.est[t-1] + beta_dd2 * logN.est[t-2] + beta_precip * precip[t-1]
}
#Error model
for(t in 1:T){
logN.est[t] ~ dnorm(mean.r[t], tau.proc)
N.est[t] <- exp(logN.est[t])
# Observation process
y[t] ~ dpois(N.est[t]*logarea[t])
}
}
", fill=T)
sink()
jags.data15<- list(y = as.integer(cats$cat.count), T = length(cats$Year),precip=cats$Precip,logarea=cats$lupine.area)
year<-cats$Year
inits15 <- function(){list(sigma.proc =0.9, beta_0= 3,beta_dd1= -0.1, beta_dd2= -0.35, beta_precip=0.03)}
parameters15<- c("mean.r", "sigma2.proc", "N.est","logN.est","beta_0","beta_dd1",
'beta_dd2',"beta_precip")
ni <- 20000
nt <- 1
nb <- 1000
nc <- 3
pgompdelayed <- jags(jags.data15, inits15, parameters15, "tigermodelprecipgompdelayed.jags", n.chains = nc,
n.thin = nt, n.iter = ni, n.burnin = nb, working.directory = getwd())
pgd<-jags.model("tigermodelprecipgompdelayed.jags",data=jags.data15, inits=inits15,n.chains = nc)
summary(pgompdelayed)
print(pgompdelayed, digits = 3)
theta.samples1 <- coda.samples(pgd, n.iter=10000, thin=10, c("beta_precip","beta_dd1","beta_dd2","beta_0",'sigma2.proc'))
par(mar=rep(2,4)); plot(theta.samples1)
#First half
sink("tigermodelprecipgompdelayed2.jags") # name of the txt file
cat("
model{
#Priors
mean.r[1] ~ dnorm(0, 0.1) # Prior for mean growth rate
mean.r[2] ~ dnorm(0, 0.1)
sigma.proc ~ dunif(0, 10) # Prior for sd of state process
sigma2.proc <- pow(sigma.proc, 2)
tau.proc <- pow(sigma.proc, -2)
beta_precip ~ dnorm(0,0.01)
beta_dd1 ~ dnorm(0,0.01)
beta_dd2 ~ dnorm(0,0.01)
beta_0 ~ dnorm(0, 0.01)
#State process
for (t in 3:T){
mean.r[t] <- beta_0 + beta_dd1 * logN.est[t-1] + beta_dd2 * logN.est[t-2] + beta_precip * precip[t-1]
}
#Error model
for(t in 1:T){
logN.est[t] ~ dnorm(mean.r[t], tau.proc)
N.est[t] <- exp(logN.est[t])
# Observation process
y[t] ~ dpois(N.est[t]*logarea[t])
}
}
", fill=T)
sink()
jags.data15<- list(y = as.integer(cats2$cat.count), T = length(cats2$Year),precip=cats2$Precip,logarea=cats2$lupine.area)
year<-cats$Year
inits15 <- function(){list(sigma.proc =0.9, beta_0= 3,beta_dd1= -0.1, beta_dd2= -0.35, beta_precip=0.03)}
parameters15<- c("mean.r", "sigma2.proc", "N.est","logN.est","beta_0","beta_dd1",
'beta_dd2',"beta_precip")
ni <- 20000
nt <- 1
nb <- 1000
nc <- 3
pgompdelayed2 <- jags(jags.data15, inits15, parameters15, "tigermodelprecipgompdelayed2.jags", n.chains = nc,
n.thin = nt, n.iter = ni, n.burnin = nb, working.directory = getwd())
pgd2<-jags.model("tigermodelprecipgompdelayed2.jags",data=jags.data15, inits=inits15,n.chains = nc)
summary(pgompdelayed)
print(pgompdelayed, digits = 3)
theta.samples2 <- coda.samples(pgd2, n.iter=10000, thin=10, c("beta_precip","beta_dd1","beta_dd2","beta_0","sigma2.proc"))
par(mar=rep(2,4)); plot(theta.samples2)
#Second half
sink("tigermodelprecipgompdelayed3.jags") # name of the txt file
cat("
model{
#Priors
mean.r[1] ~ dnorm(0, 0.1) # Prior for mean growth rate
mean.r[2] ~ dnorm(0, 0.1)
sigma.proc ~ dunif(0, 10) # Prior for sd of state process
sigma2.proc <- pow(sigma.proc, 2)
tau.proc <- pow(sigma.proc, -2)
beta_precip ~ dnorm(0,0.01)
beta_dd1 ~ dnorm(0,0.01)
beta_dd2 ~ dnorm(0,0.01)
beta_0 ~ dnorm(0, 0.01)
#State process
for (t in 3:T){
mean.r[t] <- beta_0 + beta_dd1 * logN.est[t-1] + beta_dd2 * logN.est[t-2] + beta_precip * precip[t-1]
}
#Error model
for(t in 1:T){
logN.est[t] ~ dnorm(mean.r[t], tau.proc)
N.est[t] <- exp(logN.est[t])
# Observation process
y[t] ~ dpois(N.est[t]*logarea[t])
}
}
", fill=T)
sink()
jags.data15<- list(y = as.integer(cats3$cat.count), T = length(cats3$Year),precip=cats3$Precip,logarea=cats3$lupine.area)
year<-cats$Year
inits15 <- function(){list(sigma.proc =0.9, beta_0= 3,beta_dd1= -0.1, beta_dd2= -0.35, beta_precip=0.03)}
parameters15<- c("mean.r", "sigma2.proc", "N.est","logN.est","beta_0","beta_dd1",
'beta_dd2',"beta_precip")
ni <- 20000
nt <- 1
nb <- 1000
nc <- 3
pgompdelayed3 <- jags(jags.data15, inits15, parameters15, "tigermodelprecipgompdelayed3.jags", n.chains = nc,
n.thin = nt, n.iter = ni, n.burnin = nb, working.directory = getwd())
pgd3<-jags.model("tigermodelprecipgompdelayed3.jags",data=jags.data15, inits=inits15,n.chains = nc)
summary(pgompdelayed)
print(pgompdelayed, digits = 3)
plotModelOutput(pgompdelayed,log((cats$lupine.count+1)/cats$lupine.area))
theta.samples3 <- coda.samples(pgd3, n.iter=10000, thin=10, c("beta_precip","beta_dd1","beta_dd2","beta_0",'sigma2.proc'))
par(mar=rep(2,4)); plot(theta.samples3)
coda::traceplot(theta.samples3)
png("pgomp12.png", width=600, height=400)
MCMCplot(object = pgompdelayed,
object2 = pgompdelayed2,
col='black',
col2='blue',
params = c("beta_0",'beta_dd1','beta_dd2',"beta_precip"),
labels = c('Intercept', 'Direct density-dependence', 'Delayed density-dependence', 'Precipitation'),
xlim=c(-3,3))
legend('topright',inset=.05,col = c('black',"blue","red"),lty=1,lwd=3,legend=c('Whole series',"Before threshold","After threshold"))
dev.off()
png("pgomp3.png", width=600, height=400, bg = "transparent")
MCMCplot(object = pgompdelayed3,
col='red',
params = c("beta_0",'beta_dd1','beta_dd2',"beta_precip"),
labels = c('Intercept', 'Direct density-dependence', 'Delayed density-dependence', 'Precipitation'),
xlim=c(-3,3))
dev.off()
m1<-tidy_draws(theta.samples1)
m1
spread_draws()
point_interval(m1)
III<-theta.samples1 %>%
gather_draws(beta_0,beta_dd1,beta_dd2,beta_precip)%>%
mutate(Model = "Whole series")
I<-theta.samples2 %>%
gather_draws(beta_0,beta_dd1,beta_dd2,beta_precip)%>%
mutate(Model = "Part I")
II<-theta.samples3 %>%
gather_draws(beta_0,beta_dd1,beta_dd2,beta_precip)%>%
mutate(Model = "Part II")
overall<-bind_rows(III,II,I)
overall$Model<-as.factor(overall$Model)
levels(overall$Model)
overall$Model = factor(overall$Model,levels(overall$Model)[c(2,1,3)])
levels(overall$Model)
overall%>%
ggplot(aes(y = .variable , x = .value,color=Model)) +
stat_halfeyeh(position = position_dodgev(height = .9),.width = c(.90, .95))+geom_vline(xintercept=0,lty=2)+xlab(label="Value")+
ylab(label="Parameter")+
scale_y_discrete(labels = c(expression(alpha["0"]),expression(alpha["1"]),expression(alpha["2"]),expression(beta["Precip"])))+
theme_classic()+theme(text=element_text(size=20))+guides(color = guide_legend(reverse = TRUE))+ scale_color_manual(values=c("red", "blue", "black"))
ci(overall.1$beta_dd1[overall.1$Model=="Whole series"],method="HDI",ci=0.9)
ci(overall.1$beta_dd1[overall.1$Model=="Part I"],method="HDI",ci=0.9)
ci(overall.1$beta_dd1[overall.1$Model=="Part II"],method="HDI",ci=0.9)
ci(overall.1$beta_dd2[overall.1$Model=="Whole series"],method="HDI",ci=0.9)
ci(overall.1$beta_dd2[overall.1$Model=="Part I"],method="HDI",ci=0.9)
ci(overall.1$beta_dd2[overall.1$Model=="Part II"],method="HDI",ci=0.9)
ci(overall.1$beta_precip[overall.1$Model=="Whole series"],method="HDI",ci=0.9)
ci(overall.1$beta_precip[overall.1$Model=="Part I"],method="HDI",ci=0.9)
ci(overall.1$beta_precip[overall.1$Model=="Part II"],method="HDI",ci=0.9)
III.1<-theta.samples1 %>%
spread_draws(beta_0,beta_dd1,beta_dd2,beta_precip)%>%
mutate(Model = "Whole series")
I.1<-theta.samples2 %>%
spread_draws(beta_0,beta_dd1,beta_dd2,beta_precip)%>%
mutate(Model = "Part I")
II.1<-theta.samples3 %>%
spread_draws(beta_0,beta_dd1,beta_dd2,beta_precip)%>%
mutate(Model = "Part II")
overall.1<-bind_rows(III.1,II.1,I.1)
overall.1$Model<-as.factor(overall.1$Model)
overall.1
overall.1$beta1<-overall.1$beta_dd1
overall.1$beta2<-overall.1$beta_dd2
sample(overall.1$beta_0[overall.1$Model=="Whole series"],size=1)
xes<-seq(-2,2,by=.1)
croyama<-function(x)-0.25*x^2
curve1<-croyama(xes)
curve<-data.frame(beta1=xes,beta2=curve1)
xx<-c(-2,0,2)
yy<-c(-1,1,-1)
beta1<-c(0.085,-0.619,0.45)
beta2<-c(-0.325,-0.258,-0.239)
model<-c("Whole series","Part I","Part II")
royama<-data.frame(beta1=beta1,beta2=beta2,Model=model)
triangle<-data.frame(beta1=xx,beta2=yy)
vline<-data.frame(x1=0,x2=0,y1=-1,y2=1)
labels<-data.frame(quadrant=c("I","II","III","IV","I'","II'","III'","IV'"),y=c(0.35,0.35,-.6,-.6,0.35,0.35,-1.15,-1.15),x=c(0.3,-0.3,-.7,.7,-1.5,1.5,-.7,.7))
str(overall.1)
detach("package:biwavelet", unload=TRUE)
ggplot()+geom_point(data=overall.1,mapping=aes(x=beta1,y=beta2, color=Model),size=1,alpha = 0.25)+xlim(-2,2)+ylim(-1.2,1)+theme_classic()+geom_point(data=royama,mapping=aes(x=beta1,y=beta2, fill=Model),size=4,shape=21)+xlab(expression(alpha["1"]))+ylab(expression(alpha["2"]))+geom_line(data=curve,mapping=aes(x=beta1,y=beta2))+
geom_polygon(data=triangle,mapping=aes(x=beta1,y=beta2),colour="black",fill=NA)+geom_segment(data=vline,mapping=aes(x=x1,xend=x2,y=y1,yend=y2))+
geom_text(data=labels,mapping=aes(x=x,y=y,label=quadrant),size=7)+
geom_segment(aes(x =royama$beta1[2],y = royama$beta2[2],xend = royama$beta1[3],yend = royama$beta2[3]),arrow=arrow(),data=royama)+theme(text=element_text(size=20))
##### Simlations -- using posterior
library(biwavelet)
cats1$logmean<-log(cats1$cat.mean)
cats1$logntm1<-log(cats1$ntm1)
cats1$logntm2<-log(cats1$ntm1)
cats1$precipscaled<-scale(cats1$precip)
cats2<-cats1[2:34,]
cats3<-cats1[3:34,]
oseries<-cbind(cats1$Year,cats1$logmean)
nrands<-1000
wtc.0 = wt(oseries)
##Generalised simulation function
##values drawn from posterior
simulation<-function(a0samp=overall.1$beta_0[overall.1$Model=="Whole series"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Whole series"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Whole series"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Whole series"],
reps=1000
)
{
##fixed values
log1<-function (N,Ntm1,a0,a1,a2,b1,p) a0 + a1*N+a2*Ntm1+b1*p
tf<-34 #run time
n0<-0.293 #pop init size
n1<--2.957
n2<--1.822
samples<-vector(length=reps)
for(i in 1:reps){
a0<-sample(a0samp,size=1)
a1<-sample(a1samp,size=1)
a2<-sample(a2samp,size=1)
b1<-sample(bprecipsamp,size=1)
n<-rep(NA,tf) #make vector
n[1] = n0 #put init pop
n[2] = n1
n[3]=n2
precip<-cats1$precipscaled
for(t in 3:(tf-1)){ #t-1 to match lengths
n[t+1]<-log1(N=n[t],Ntm1=n[t-1],a0=a0,a1=a1,a2=a2,b1=b1,p=precip[t])
}
sim1<-cbind(cats1$Year,n)
wtc.1 = wt(sim1)
samples[i]<-wdist(wtc.0$wave,wtc.1$wave)
}
print(samples)
}
s1<-simulation(a0samp=overall.1$beta_0[overall.1$Model=="Whole series"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Whole series"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Whole series"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Whole series"],
reps=10000)
hist(s1)
s1.1<-as.data.frame(s1)
s1.1$sim<-"Whole Series"
str(s1.1)
ggplot(s1.1,aes(s1))+geom_density()
###First Part
s2<-simulation(a0samp=overall.1$beta_0[overall.1$Model=="Part I"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Part I"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Part I"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part I"],
reps=10000)
hist(s2)
s2.1<-as.data.frame(s2)
s2.1$sim<-"Part I"
s2.1$s1<-s2.1$s2
ggplot(s2.1,aes(s2))+geom_density()
###Second Part
s3<-simulation(a0samp=overall.1$beta_0[overall.1$Model=="Part II"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Part II"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Part II"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part II"],
reps=10000)
hist(s3)
s3.1<-as.data.frame(s3)
s3.1$sim<-"Part II"
s3.1$s1<-s3.1$s3
ggplot(s3.1,aes(s3))+geom_density()
###Whole series --- precip from part II
s4<-simulation(a0samp=overall.1$beta_0[overall.1$Model=="Whole series"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Whole series"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Whole series"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part II"],
reps=10000)
hist(s4)
s4.1<-as.data.frame(s4)
s4.1$sim<-"Whole Series Precip Same"
s4.1$s1<-s4.1$s4
ggplot(s1.1,aes(s4))+geom_density()
###First Part
s5<-simulation(a0samp=overall.1$beta_0[overall.1$Model=="Part I"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Part I"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Part I"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part II"],
reps=10000)
hist(s5)
s5.1<-as.data.frame(s5)
s5.1$sim<-"Part I Precip Same"
s5.1$s1<-s5.1$s5
ggplot(s5.1,aes(s5))+geom_density()
##Part II no DD
s6<-simulation(a0samp=overall.1$beta_0[overall.1$Model=="Part II"],
a1samp=0,
a2samp=0,
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part II"],
reps=10000)
hist(s6)
s6.1<-as.data.frame(s6)
s6.1$sim<-"Part II No DD"
s6.1$s1<-s6.1$s6
ggplot(s6.1,aes(s6))+geom_density()
s1.1
str(s1.1)
str(s2.1)
str(s3.1)
str(s4.1)
str(s5.1)
str(s6.1)
values<-c(s1.1$s1,s2.1$s1,s3.1$s1,s4.1$s1,s5.1$s1,s6.1$s1)
length(values)
simulations<-c(s1.1$sim,s2.1$sim,s3.1$sim,s4.1$sim,s5.1$sim,s6.1$sim)
length(simulations)
allsims<-data.frame(values=values,simulation=simulations)
str(allsims)
allsims$simulation<-as.factor(allsims$simulation)
str(allsims)
ggplot(allsims,aes(values,lty=simulation))+geom_density()
hdi1<-ci(allsims$values[allsims$simulation=='Whole Series'], method="HDI",ci=0.95)
hdi2<-ci(allsims$values[allsims$simulation=='Whole Series Precip Same'], method="HDI",ci=0.95)
hdi3<-ci(allsims$values[allsims$simulation=='Part II No DD'], method="HDI",ci=0.95)
hdi4<-ci(allsims$values[allsims$simulation=='Part II'], method="HDI",ci=0.95)
hdi5<-ci(allsims$values[allsims$simulation=='Part I Precip Same'], method="HDI",ci=0.95)
hdi6<-ci(allsims$values[allsims$simulation=='Part I'], method="HDI",ci=0.95)
hdi1
hdi2
hdi3
hdi4
hdi5
hdi6
hdi1<-ci(allsims$values[allsims$simulation=='Whole Series'], method="HDI",ci=0.9)
hdi2<-ci(allsims$values[allsims$simulation=='Whole Series Precip Same'], method="HDI",ci=0.9)
hdi3<-ci(allsims$values[allsims$simulation=='Part II No DD'], method="HDI",ci=0.9)
hdi4<-ci(allsims$values[allsims$simulation=='Part II'], method="HDI",ci=0.9)
hdi5<-ci(allsims$values[allsims$simulation=='Part I Precip Same'], method="HDI",ci=0.9)
hdi6<-ci(allsims$values[allsims$simulation=='Part I'], method="HDI",ci=0.9)
hdi1
hdi2
hdi3
hdi4
hdi5
hdi6
str(allsims)
ggplot(data=allsims,aes(y = simulation , x = values)) +
stat_halfeyeh(position = position_dodgev(height = .9),.width = c(.90, .95))+xlab(label="Value")+
ylab(label="Scenario")+
theme_classic()+theme(text=element_text(size=20))
#####Posterior predictive checks
cats1<-read.csv('bodega.cats2.csv')
str(cats1)
cats<-cats1
cats$Precip<-as.numeric(scale(cats$precip))
cats$cat.count<-as.integer(cats$lupine.count)
cats2<-cats1[1:19,]
cats2$Precip<-as.numeric(scale(cats2$precip))
cats2$cat.count<-as.integer(cats2$lupine.count)
str(cats2)
cats3<-cats1[19:34,]
cats3$Precip<-as.numeric(scale(cats3$precip))
cats3$cat.count<-as.integer(cats3$lupine.count)
str(cats)
sink("tigermodelprecipgompdelayedppcheck.jags") # name of the txt file
cat("
model{
#Priors
mean.r[1] ~ dnorm(0, 0.1) # Prior for mean growth rate
mean.r[2] ~ dnorm(0, 0.1)
sigma.proc ~ dunif(0, 10) # Prior for sd of state process
sigma2.proc <- pow(sigma.proc, 2)
tau.proc <- pow(sigma.proc, -2)
beta_precip ~ dnorm(0,0.01)
beta_dd1 ~ dnorm(0,0.01)
beta_dd2 ~ dnorm(0,0.01)
beta_0 ~ dnorm(0, 0.01)
#State process
for (t in 3:T){
mean.r[t] <- beta_0 + beta_dd1 * logN.est[t-1] + beta_dd2 * logN.est[t-2] + beta_precip * precip[t-1]
}
#Error model
for(t in 1:T){
logN.est[t] ~ dnorm(mean.r[t], tau.proc)
N.est[t] <- exp(logN.est[t])
# Observation process
y[t] ~ dpois(N.est[t]*logarea[t])
y.new[t] ~ dpois(N.est[t]*logarea[t])
res[t]<-y[t]-N.est[t]
res.new[t]<-y.new[t]-N.est[t]
}
#Derived parameters
fit <- sum(res[])
fit.new <- sum(res.new[])
}
", fill=T)
sink()
jags.data15<- list(y = as.integer(cats$cat.count), T = length(cats$Year),precip=cats$Precip,logarea=cats$lupine.area)
year<-cats$Year
inits15 <- function(){list(sigma.proc =0.9, beta_0= 3,beta_dd1= -0.1, beta_dd2= -0.35, beta_precip=0.03)}
parameters15<- c("mean.r", "sigma2.proc", "N.est","logN.est","beta_0","beta_dd1",
'beta_dd2',"beta_precip","fit",'fit.new','y.new')
ni <- 20000
nt <- 1
nb <- 1000
nc <- 3
pgompdelayed <- jags(jags.data15, inits15, parameters15, "tigermodelprecipgompdelayedppcheck.jags", n.chains = nc,
n.thin = nt, n.iter = ni, n.burnin = nb, working.directory = getwd())
pgd<-jags.model("tigermodelprecipgompdelayed.jags",data=jags.data15, inits=inits15,n.chains = nc)
summary(pgompdelayed)
print(pgompdelayed, digits = 3)
theta.samples1 <- coda.samples(pgd, n.iter=10000, thin=10, c("beta_precip","beta_dd1","beta_dd2","beta_0"))
par(mar=rep(1,4)); plot(theta.samples1,oma=c(2,2,2,2))
library(jagsUI)
pgompdelayed <- jags(jags.data15, inits15, parameters15, "tigermodelprecipgompdelayedppcheck.jags", n.chains = nc,
n.thin = nt, n.iter = ni, n.burnin = nb)
pp.check(x=pgompdelayed, observed = 'fit', simulated = 'fit.new')
crosscorr.plot(theta.samples1)
crosscorr(theta.samples1)
#pp.check(x=pgompdelayed, observed = 'y', simulated = 'y.new')
str(pgompdelayed)
ynew<-pgompdelayed$sims.list$y.new
year<-cats$Year
y = as.integer(cats$cat.count)
y
plot(x=year,y=y+1,type='l',log='y',xlab="Year",ylab="Count")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=y+1, lwd=3)
#First half
sink("tigermodelprecipgompdelayed2ppcheck.jags") # name of the txt file
cat("
model{
#Priors
mean.r[1] ~ dnorm(0, 0.1) # Prior for mean growth rate
mean.r[2] ~ dnorm(0, 0.1)
sigma.proc ~ dunif(0, 10) # Prior for sd of state process
sigma2.proc <- pow(sigma.proc, 2)
tau.proc <- pow(sigma.proc, -2)
beta_precip ~ dnorm(0,0.01)
beta_dd1 ~ dnorm(0,0.01)
beta_dd2 ~ dnorm(0,0.01)
beta_0 ~ dnorm(0, 0.01)
#State process
for (t in 3:T){
mean.r[t] <- beta_0 + beta_dd1 * logN.est[t-1] + beta_dd2 * logN.est[t-2] + beta_precip * precip[t-1]
}
#Error model
for(t in 1:T){
logN.est[t] ~ dnorm(mean.r[t], tau.proc)
N.est[t] <- exp(logN.est[t])
# Observation process
y[t] ~ dpois(N.est[t]*logarea[t])
y.new[t] ~ dpois(N.est[t]*logarea[t])
res[t]<-y[t]-N.est[t]
res.new[t]<-y.new[t]-N.est[t]
}
#Derived parameters
fit <- sum(res[])
fit.new <- sum(res.new[])
}
", fill=T)
sink()
jags.data15<- list(y = as.integer(cats2$cat.count), T = length(cats2$Year),precip=cats2$Precip,logarea=cats2$lupine.area)
year<-cats$Year
inits15 <- function(){list(sigma.proc =0.9, beta_0= 3,beta_dd1= -0.1, beta_dd2= -0.35, beta_precip=0.03)}
parameters15<- c("mean.r", "sigma2.proc", "N.est","logN.est","beta_0","beta_dd1",
'beta_dd2',"beta_precip","fit",'fit.new','y.new')
ni <- 20000
nt <- 1
nb <- 1000
nc <- 3
pgompdelayed2 <- jags(jags.data15, inits15, parameters15, "tigermodelprecipgompdelayed2ppcheck.jags", n.chains = nc,
n.thin = nt, n.iter = ni, n.burnin = nb)
pgd2<-jags.model("tigermodelprecipgompdelayed2.jags",data=jags.data15, inits=inits15,n.chains = nc)
summary(pgompdelayed)
print(pgompdelayed, digits = 3)
theta.samples2 <- coda.samples(pgd2, n.iter=10000, thin=10, c("beta_precip","beta_dd1","beta_dd2","beta_0"))
par(mar=rep(1,4)); plot(theta.samples2)
pp.check(x=pgompdelayed2, observed = 'fit', simulated = 'fit.new')
crosscorr(theta.samples2)
ynew<-pgompdelayed2$sims.list$y.new
ynew
y = as.integer(cats2$cat.count)
y
year<-cats2$Year
plot(x=cats2$Year,y=y+1,type='l',log='y',xlab="Year",ylab="Count")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=y+1, lwd=3)
#Second half
sink("tigermodelprecipgompdelayed3ppcheck.jags") # name of the txt file
cat("
model{
#Priors
mean.r[1] ~ dnorm(0, 0.1) # Prior for mean growth rate
mean.r[2] ~ dnorm(0, 0.1)
sigma.proc ~ dunif(0, 10) # Prior for sd of state process
sigma2.proc <- pow(sigma.proc, 2)
tau.proc <- pow(sigma.proc, -2)
beta_precip ~ dnorm(0,0.01)
beta_dd1 ~ dnorm(0,0.01)
beta_dd2 ~ dnorm(0,0.01)
beta_0 ~ dnorm(0, 0.01)
#State process
for (t in 3:T){
mean.r[t] <- beta_0 + beta_dd1 * logN.est[t-1] + beta_dd2 * logN.est[t-2] + beta_precip * precip[t-1]
}
#Error model
for(t in 1:T){
logN.est[t] ~ dnorm(mean.r[t], tau.proc)
N.est[t] <- exp(logN.est[t])
# Observation process
y[t] ~ dpois(N.est[t]*logarea[t])
y.new[t] ~ dpois(N.est[t]*logarea[t])
res[t]<-y[t]-N.est[t]
res.new[t]<-y.new[t]-N.est[t]
}
#Derived parameters
fit <- sum(res[])
fit.new <- sum(res.new[])
}
", fill=T)
sink()
jags.data15<- list(y = as.integer(cats3$cat.count), T = length(cats3$Year),precip=cats3$Precip,logarea=cats3$lupine.area)
year<-cats$Year
inits15 <- function(){list(sigma.proc =0.9, beta_0= 3,beta_dd1= -0.1, beta_dd2= -0.35, beta_precip=0.03)}
parameters15<- c("mean.r", "sigma2.proc", "N.est","logN.est","beta_0","beta_dd1",
'beta_dd2',"beta_precip","fit",'fit.new','y.new')
ni <- 20000
nt <- 1
nb <- 1000
nc <- 3
pgompdelayed3 <- jags(jags.data15, inits15, parameters15, "tigermodelprecipgompdelayed3ppcheck.jags", n.chains = nc,
n.thin = nt, n.iter = ni, n.burnin = nb )
pgd3<-jags.model("tigermodelprecipgompdelayed3.jags",data=jags.data15, inits=inits15,n.chains = nc)
summary(pgompdelayed3)
print(pgompdelayed3, digits = 3)
theta.samples3<- coda.samples(pgd3, n.iter=10000, thin=10, c("beta_precip","beta_dd1","beta_dd2","beta_0"))
par(mar=rep(1,4)); plot(theta.samples3)
crosscorr(theta.samples3)
pp.check(x=pgompdelayed3, observed = 'fit', simulated = 'fit.new')
ynew<-pgompdelayed3$sims.list$y.new
ynew
y = as.integer(cats3$cat.count)
y
year<-cats3$Year
dev.off()
plot(x=year,y=y+1,type='l',log='y',xlab="Year",ylab="Count")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=ynew[sample(nrow(ynew),size=1,replace=FALSE),]+1, col="blue")
lines(x=year,y=y+1, lwd=3)
##Simulation plots
samples<-matrix(0,ncol=34-3,nrow=100)
samples
simulation2<-function(a0samp=overall.1$beta_0[overall.1$Model=="Whole series"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Whole series"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Whole series"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Whole series"],
reps=100
)
{
##fixed values
log1<-function (N,Ntm1,a0,a1,a2,b1,p) a0 + a1*N+a2*Ntm1+b1*p
tf<-34 #run time
n0<-0.293 #pop init size
n1<--2.957
n2<--1.822
samples<-matrix(0,ncol=tf,nrow=reps)
for(i in 1:reps){
a0<-sample(a0samp,size=1)
a1<-sample(a1samp,size=1)
a2<-sample(a2samp,size=1)
b1<-sample(bprecipsamp,size=1)
n<-rep(NA,tf) #make vector
n[1] = n0 #put init pop
n[2] = n1
n[3]=n2
precip<-cats1$precipscaled
for(t in 3:(tf-1)){ #t-1 to match lengths
n[t+1]<-log1(N=n[t],Ntm1=n[t-1],a0=a0,a1=a1,a2=a2,b1=b1,p=precip[t])
}
sim1<-cbind(cats1$Year,n)
samples[i,]<-n
}
print(samples)
}
##Have to have JagsUI unloaded?
par(mfrow=c(2,3))
s1<-simulation2(a0samp=overall.1$beta_0[overall.1$Model=="Whole series"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Whole series"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Whole series"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Whole series"],
reps=100)
s1.1<-exp(s1)
s1.1[1,]
y = cats$lupine.count/cats$lupine.area
year<-cats$Year
plot(x=year,y=y+1,type='l',log='y',xlab="Year",ylab="Density",ylim=c(1,20),main="Whole series")
for(i in 1:50){
lines(x=year,y=s1.1[sample(nrow(s1.1),size=1,replace=FALSE),]+1, col=rgb(0,0,1,0.1))
}
lines(x=year,y=y+1, lwd=3)
###First Part
s2<-simulation2(a0samp=overall.1$beta_0[overall.1$Model=="Part I"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Part I"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Part I"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part I"],
reps=100)
s1.1<-exp(s2)
y = cats$lupine.count/cats$lupine.area
year<-cats$Year
plot(x=year,y=y+1,type='l',log='y',xlab="Year",ylab="Density",ylim=c(1,20),main='Part I')
for(i in 1:50){
lines(x=year,y=s1.1[sample(nrow(s1.1),size=1,replace=FALSE),]+1, col=rgb(0,0,1,0.1))
}
lines(x=year,y=y+1, lwd=3)
###Second Part
s3<-simulation2(a0samp=overall.1$beta_0[overall.1$Model=="Part II"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Part II"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Part II"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part II"],
reps=100)
s1.1<-exp(s3)
y = cats$lupine.count/cats$lupine.area
year<-cats$Year
plot(x=year,y=y+1,type='l',log='y',xlab="Year",ylab="Density",ylim=c(1,20),main='Part II')
for(i in 1:50){
lines(x=year,y=s1.1[sample(nrow(s1.1),size=1,replace=FALSE),]+1, col=rgb(0,0,1,0.1))
}
lines(x=year,y=y+1, lwd=3)
###Whole series --- precip from part II
s4<-simulation2(a0samp=overall.1$beta_0[overall.1$Model=="Whole series"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Whole series"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Whole series"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part II"],
reps=100)
s1.1<-exp(s4)
y = cats$lupine.count/cats$lupine.area
year<-cats$Year
plot(x=year,y=y+1,type='l',log='y',xlab="Year",ylab="Density",ylim=c(1,20),main='Whole Series Precip Same')
for(i in 1:50){
lines(x=year,y=s1.1[sample(nrow(s1.1),size=1,replace=FALSE),]+1, col=rgb(0,0,1,0.1))
}
lines(x=year,y=y+1, lwd=3)
###First Part
s5<-simulation2(a0samp=overall.1$beta_0[overall.1$Model=="Part I"],
a1samp=overall.1$beta_dd1[overall.1$Model=="Part I"],
a2samp=overall.1$beta_dd2[overall.1$Model=="Part I"],
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part II"],
reps=100)
s1.1<-exp(s5)
y = cats$lupine.count/cats$lupine.area
year<-cats$Year
plot(x=year,y=y+1,type='l',log='y',xlab="Year",ylab="Density",ylim=c(1,20),main="Part I Precip Same")
for(i in 1:30){
lines(x=year,y=s1.1[sample(nrow(s1.1),size=1,replace=FALSE),]+1, col=rgb(0,0,1,0.1))
}
lines(x=year,y=y+1, lwd=3)
##Part II no DD
s6<-simulation2(a0samp=overall.1$beta_0[overall.1$Model=="Part II"],
a1samp=0,
a2samp=0,
bprecipsamp=overall.1$beta_precip[overall.1$Model=="Part II"],
reps=100)
s1.1<-exp(s6)
y = cats$lupine.count/cats$lupine.area
year<-cats$Year
plot(x=year,y=y+1,type='l',log='y',xlab="Year",ylab="Density",ylim=c(1,20),main='Part II No DD')
for(i in 1:30){
lines(x=year,y=s1.1[sample(nrow(s1.1),size=1,replace=FALSE),]+1, col=rgb(0,0,1,0.1))
}
lines(x=year,y=y+1, lwd=3)
|
1bf880e3230ed008084baa29d8b0acc00620cd3b | 84d9d69a930ab7a15fff5d3d05a9c11c4431ce30 | /pml.r | 331889b994dc0443fa24499544b35473dca4a20b | [] | no_license | etaoinbe/predmachlearn-project | 2de25ee018dc803bfcd4bb203cc215e9922eee46 | f9df76e133802484f7e48575182bf99aff1804be | refs/heads/master | 2020-06-01T18:16:07.020056 | 2014-06-22T16:59:55 | 2014-06-22T16:59:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,525 | r | pml.r | # What you should submit
#
# The goal of your project is to predict the manner in which they did the exercise. This is the "classe" variable
# in the training set. You may use any of the other variables to predict with. You should create a report describing
# how you built your model, how you used cross validation, what you think the expected out of sample error is, and why
# you made the choices you did. You will also use your prediction model to predict 20 different test cases.
#
# 1. Your submission should consist of a link to a Github repo with your R markdown and compiled HTML file describing
# your analysis. Please constrain the text of the writeup to < 2000 words and the number of figures to be less than 5.
# It will make it easier for the graders if you submit a repo with a gh-pages branch so the HTML page can be viewed
# online (and you always want to make it easy on graders :-).
# 2. You should also apply your machine learning algorithm to the 20 test cases available in the test data above.
# Please submit your predictions in appropriate format to the programming assignment for automated grading.
# See the programming assignment for additional details.
#
# Reproducibility
#
# Due to security concerns with the exchange of R code, your code will not be run during the evaluation by your
# classmates. Please be sure that if they download the repo, they will be able to view the compiled HTML version of your
# analysis.
# C:\data\git\predmachlearn-project
library(caret)
library(rattle)
#http://groupware.les.inf.puc-rio.br/public/papers/2013.Velloso.QAR-WLE.pdf
#
testmethod="rf"
#testmethod="rpart"
#####################
# TRAINING
#####################
#
setwd("C:\\data\\lectures\\predmachlearn\\project")
#setwd("e:\\data-e\\project")
#training <- read.csv("pml-training.csv")
#xx <- read.csv("pml-training.csv",as.is=T,stringsAsFactors=F)
pmlData <- read.csv("pml-training.csv",as.is=T,stringsAsFactors=F)
ns=names(pmlData)
for(i in 1:length(ns)) {
name=ns[i]
if( name!="classe") {
if( typeof(pmlData[,name])=="character" ) { cat("!!!",name);
pmlData[, c(name)]=as.numeric( pmlData[, c(name)] ) ;
}
#print(sprintf("types tr %s name %s ",typeof(pmlData[, c(name)]), name ) )
}}
pmlData$classe=as.factor(pmlData$classe)
inTrain = createDataPartition(pmlData$classe, p = .6)[[1]]
training = pmlData[ inTrain,]
validationset = pmlData[-inTrain,]
dim(validationset)
#training<- training[ sample(dim(training)[1], 100), ] #!!!
qplot(seq_along(training$classe),training$classe)
qplot(training$X,training$classe)
excludes="timestamp|X|user_name|new_window"
#training<-training[,colSums(is.na(training)) < nrow(training) ]
#testing<-testingsrc[,colSums(is.na(testingsrc)) < nrow(testingsrc) ]
#training1 <- subset( trainingsrc, select = -X )
NAs <- apply(training,2,function(x) {sum(is.na(x))})
training <- training[,which(NAs == 0)]
removeIndex <- grep(excludes, names(training))
training <- training[,-removeIndex]
set.seed(975)
if(testmethod=="rpart") {
modfit=train(training$classe ~ ., method="rpart", data=training )
print(modfit$finalModel)
jpeg("modfittree.jpg")
plot(modfit$finalModel,uniform=TRUE,main="tree")
text(modfit$finalModel,use.n=TRUE,all=TRUE,cex=.8)
dev.off()
jpeg("fancytree.jpeg")
fancyRpartPlot(modfit$finalModel)
dev.off()
confusionMatrix(training$classe, predict(modfit, training))
}
###
if(testmethod=="rf") {
#
training<- training[ sample(dim(training)[1], 6000), ] #!!!
#training<- training[ sample(dim(training)[1], 3000), ] #!!!
#https://class.coursera.org/predmachlearn-002/forum/thread?thread_id=249#post-1024
trctrl=trainControl(method = "cv", number = 5)
modfitrf=train(training$classe ~ ., method="rf", data=training, trControl = trctrl, prox=TRUE )
print(modfitrf$results)
confusionMatrix(training$classe, predict(modfitrf, training))
}
##########################################
# validation out of sample error
##########################################
if(testmethod=="rpart") {
valp= predict(modfit, validationset, verbose = TRUE)
confusionMatrix(validationset$classe, valp)
}
if(testmethod=="rf") {
valp = predict(modfitrf, validationset, verbose = TRUE)
confusionMatrix(validationset$classe, valp)
}
#####################
# TESTING
#####################
testing <- read.csv("pml-testing.csv",as.is=T,stringsAsFactors=F)
ns=names(testing)
for(i in 1:length(ns)) {
name=ns[i]
if( name!="classe") {
if( typeof(testing[,name])=="character" ) { cat("!!!",name);
testing[, c(name)]=as.numeric( testing[, c(name)] ) ;
}
print(sprintf("types tr %s name %s ",typeof(testing[, c(name)]), name ) )
}}
#NAs2 <- apply(testing,2,function(x) {sum(is.na(x))})
testing <- testing[,which(NAs == 0)]
#testing<-testing[,colSums(is.na(testing)) < nrow(testing) ]
removeIndex <- grep(excludes,names(testing))
#testing <- subset( testing, select = removeIndex )
testing <- testing[,-removeIndex]
table(training$classe)
plot(table(training$classe))
if(testmethod=="rpart") {
predict(modfit, testing, verbose = TRUE)
}
if(testmethod=="rf") {
prediction= predict(modfitrf, testing, verbose = TRUE)
}
pml_write_files = function(x){
n = length(x)
for(i in 1:n){
filename = paste0("problem_id_",i,".txt")
write.table(x[i],file=filename,quote=FALSE,row.names=FALSE,col.names=FALSE)
}
}
pml_write_files(prediction)
|
84363a636901ec229eeb9d8fa8486c5dc8f1a677 | 343d569ab4a4a89a762c58f4fda375ab95823f0a | /R/lr.R | 1cef03a2fc349ec9ca03fd931c7f3e183313f6a1 | [] | no_license | asancpt/sasLM | d62aa33ac3e63aff1c1a2db92a4c8615840ba06b | 8c8d4dcf5f556a44bedfa5b19d3094bbd41bc486 | refs/heads/master | 2023-05-26T06:33:44.773640 | 2021-06-15T03:50:02 | 2021-06-15T03:50:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,027 | r | lr.R | lr = function(Formula, Data, eps=1e-8)
{
if (!attr(terms(Formula, data=Data), "response")) stop("Dependent variable should be provided!")
x = ModelMatrix(Formula, Data)
y = model.frame(Formula, Data)[,1]
if (!is.numeric(y)) stop("Dependent variable should be numeric!")
nc = ncol(x$X)
XpX = crossprod(x$X)
XpY = crossprod(x$X, y)
aXpX = rbind(cbind(XpX, XpY), cbind(t(XpY), crossprod(y)))
ag2 = G2SWEEP(aXpX, Augmented = TRUE, eps = eps)
b = ag2[1:nc, (nc + 1)]
iXpX = ag2[1:nc, 1:nc]
nr = nrow(x$X)
np = attr(ag2, "rank")
DFr = nr - np
SSE = ag2[(nc + 1), (nc + 1)]
fIntercept = attr(x$terms, "intercept")
SST = as.numeric(crossprod(y - fIntercept*mean(y)))
if (DFr > 0) {
MSE = SSE/DFr
bVar = iXpX %*% XpX %*% t(iXpX) * MSE
bVar[abs(bVar) < eps] = NA_real_
bSE = sqrt(diag(bVar))
Tval = b/bSE
Pval = 2*(1 - pt(abs(Tval), DFr))
} else {
MSE = NA
bSE = NA
Tval = NA
Pval = NA
}
Parameter = cbind(b, bSE, Tval, Pval)
colnames(Parameter) = c("Estimate", "Std. Error", "t value", "Pr(>|t|)")
rownames(Parameter) = colnames(x$X)
Res = list()
Res$call = match.call()
Res$terms = x$terms
Res$residuals = as.vector(y - x$X %*% b)
coef1 = Parameter
coef1[is.na(bSE) & b == 0, "Estimate"] = NA_real_
DefOpt = options(contrasts=c("contr.SAS", "contr.SAS"))
coef1 = coef1[colnames(model.matrix(Formula, Data)), , drop=FALSE]
options(DefOpt)
Res$coefficients = coef1
Res$aliased = !is.numeric(coef1[,"Estimate"])
Res$df = c(np, DFr, nc)
Res$r.squared = 1 - SSE/SST
if (DFr > 0) {
Res$sigma = sqrt(MSE)
Res$adj.r.squared = 1 - (1 - Res$r.squared) * (nr - fIntercept)/DFr
Res$fstatistic = c(value=(SST - SSE)/(np - fIntercept)/MSE, numdf=(np - fIntercept), dendf=DFr)
} else {
Res$sigma = NaN
Res$adj.r.squared = NaN
Res$fstatistic = c(NaN, numdf=(np - fIntercept), dendf=DFr)
}
class(Res) = "summary.lm"
return(Res)
}
|
81e38bf0edcf8a3d78d32cea44ce6949ade68e4f | 7bb3f64824627ef179d5f341266a664fd0b69011 | /Business_Statistics_:_A_First_Course_by_David_M._Levine,_Kathryn_A._Szabat,_David_F._Stephan,_P._K._Vishwanathan/CH7/EX7.3/7_3.R | 2f6ee19a071780ad3593b1edc0c3679385c50da9 | [
"MIT"
] | permissive | prashantsinalkar/R_TBC_Uploads | 8bd0f71834814b1d03df07ce90b2eae3b7d357f8 | b3f3a8ecd454359a2e992161844f2fb599f8238a | refs/heads/master | 2020-08-05T23:06:09.749051 | 2019-10-04T06:54:07 | 2019-10-04T06:54:07 | 212,746,586 | 0 | 0 | MIT | 2019-10-04T06:03:49 | 2019-10-04T06:03:48 | null | UTF-8 | R | false | false | 346 | r | 7_3.R | #Effect of sample size n, on the clustering of Means is the sampling Distribution
# Z formula for sample means : z = (sample_mean - average)/(standard_dev/sqrt(sample_size))
sample_mean<- 365
avg<- 368
standard_dev<-15
sample_size<- 100
standard_error_mean<- standard_dev/sqrt(sample_size)
z<- (sample_mean - avg)/standard_error_mean
z
|
f9056a264915fa042030c754904daad5a663d6a2 | 483b19fca44376eee27583bcb9691e731d3345d2 | /tests/testthat/test-initAnimal.R | 01e5360a3814e963c304a76aa3ccd581fe727436 | [
"MIT"
] | permissive | Nature40/tRackIT | f550f6a629b369519b9e5b7d60829ca16ed885ea | 10176a15c1123b29c1c74da6ce6db34cfab15063 | refs/heads/main | 2023-04-09T13:46:45.948535 | 2022-11-10T12:32:41 | 2022-11-10T12:32:41 | 321,942,074 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 514 | r | test-initAnimal.R | test_that("Expected output", {
anml<-initAnimal(projList = test_project,projroot= "H:/projects/repositories/test_project/",saveAnml = TRUE, animalID = test_project$tags$ID[1], species = "woodpecker", sex = "m", age = "adult", weight = 36, rep.state = "breeding", freq = 150050, start = test_project$tags$start[1], end = test_project$tags$end[1] )
expect_equal(names(anml), c("meta", "path"))
expect_equal(!is.na(anml$meta$animalID),!is.na(1))
expect_equal(!is.na(anml$meta$freq),!is.na(1))
})
|
091c8dc5c7c42ae1d93d24a8dc5c0891d25a2880 | b1ef9bf42b79ef0b5ee7979f537d05d50fc24f75 | /inst/shiny-examples/BioMonTools/www/linked_files/TaxaMaps/BMT_MapTaxaObs_Example1.R | ed0e3bdfaf8caabffd182b451fd0a54b6d56f057 | [
"MIT"
] | permissive | leppott/BioMonTools | 19b2c06f8faee35d9f17060e82150d8c3e07521c | 3c69c89e0b79acc2150e8516949f6a977362213c | refs/heads/main | 2023-08-18T10:37:49.117554 | 2023-08-11T11:26:36 | 2023-08-11T11:26:36 | 157,881,777 | 12 | 6 | MIT | 2022-06-09T13:43:46 | 2018-11-16T14:55:10 | R | UTF-8 | R | false | false | 1,695 | r | BMT_MapTaxaObs_Example1.R | library(readxl)
library(BioMonTools)
#set working directory
wd <-'C:/Users/Jen.Stamp/Documents/R_code/BioMonTools_4.3.C/TaxaDistribMaps'
setwd(wd)
#this script creates maps in the order in which the taxa appear in your input file.
#I recommend sorting by taxonomy (your preference - e.g., phylum, order, family, genus) before running the script
#df_obs <- read_excel("~/BioMonTools/Maps_Plecop_genus.xlsx")
data_example <- read_excel(file.path(wd, 'MapInput_Example1.xlsx'))
df_obs <- data_example
SampID <- "SampleID"
TaxaID <- "TaxaID"
TaxaCount <- "N_Taxa"
Lat <- "Latitude"
Long <- "Longitude"
output_dir <- getwd()
output_prefix <- "maps.taxa."
output_type <- "pdf"
myDB <- "state"
myRegion <- c("iowa", "nebraska", "kansas", "missouri", "oklahoma", "minnesota")
# Iowa lat/long
x_IA <- c(-(96+38/60), -(90+8/60))
y_IA <- c((40+23/60), (43+30/60))
# Nebraska lat/long
x_NE <- c(-(104+3/60), -(95+19/60))
y_NE <- c((40), (43))
# Kansas lat/long
x_KS <- c(-(102+3/60), -(94+35/60))
y_KS <- c((37), (40))
# Missouri lat/long
x_MO <- c(-(95+46/60), -(89+6/60))
y_MO <- c((36), (40+37/60))
# Oklahoma lat/long
x_OK <- c(-(103), -(94+26/60))
y_OK <- c((33+37/60), (37))
# Minnesota lat/long
x_MN <- c(-(89+29/60), -(97+14/60))
y_MN <- c((43+30/60), (46))
myXlim <- c(min(x_IA, x_NE, x_KS, x_MO, x_OK, x_MN), max(x_IA, x_NE, x_KS, x_MO, x_OK, x_MN))
myYlim <- c(min(y_IA, y_NE, y_KS, y_MO, y_OK, y_MN), max(y_IA, y_NE, y_KS, y_MO, y_OK, y_MN))
df_obs <- as.data.frame(df_obs)
# Run function with extra arguments for map
MapTaxaObs(df_obs, SampID, TaxaID, TaxaCount, Lat, Long
, database=myDB, regions=myRegion, xlim=myXlim, ylim=myYlim
, map_grp = "Source")
|
008ae68588abff0192e6b7dd54a36431ed13f4df | e294404235f97acb3b0da5f85148b5433577500c | /Scripts/(3)Injection_donnees.R | 9afa241b2c982a903d7e51d5fea8e9e155638d1b | [] | no_license | montravailBIO500/Travail_Spikee | 77c4be040a8f05f2553dee83a07c416060aa83fc | 46ed3beecdd8a6dff09e3b9da4776436755c9187 | refs/heads/main | 2023-04-10T19:13:26.558711 | 2021-04-26T03:12:55 | 2021-04-26T03:12:55 | 357,302,943 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,657 | r | (3)Injection_donnees.R | ### INJECTION DES DONNEES ###
# INSTALLER LE PACKAGE SQLITE
install.packages('RSQLite')
library(RSQLite)
# OUVRIR LA CONNECTION
con <- dbConnect(SQLite(), dbname="projetspikee.db")
# CREER LES TABLES SQL
# Creer la table noeuds
noeuds_sql <- '
CREATE TABLE noeuds (
nom_prenom VARCHAR(50),
annee_debut DATE,
session_debut CHAR(1),
programme VARCHAR(50),
coop BOLEAN,
bio500 BOLEAN,
bio5002 BOLEAN,
PRIMARY KEY (nom_prenom)
);'
dbSendQuery(con, noeuds_sql)
# Creer la table collaborations
collaborations_sql <- '
CREATE TABLE collaborations (
etudiant1 VARCHAR(50),
etudiant2 VARCHAR(50),
sigle CHAR(6),
session CHAR(3),
PRIMARY KEY (etudiant1, etudiant2, sigle, session),
FOREIGN KEY (etudiant1) REFERENCES noeuds(nom_prenom),
FOREIGN KEY (etudiant2) REFERENCES noeuds(nom_prenom),
FOREIGN KEY (sigle) REFERENCES cours(sigle)
);'
dbSendQuery(con, collaborations_sql)
# Creer la table cours
cours_sql <- '
CREATE TABLE cours (
sigle CHAR(6) NOT NULL,
credits INTEGER NOT NULL,
obligatoire BOLEAN,
laboratoire BOLEAN,
distance BOLEAN,
groupes BOLEAN,
libre BOLEAN,
PRIMARY KEY (sigle, distance)
);'
dbSendQuery(con, cours_sql)
# VERIFIER LA PRESENCE DES 3 TABLES DANS LE SERVEUR SQL
dbListTables(con)
# INJECTION DES DONNEES DANS LES TABLES SQL
dbWriteTable(con, append = TRUE, name = "noeuds", value = db_noeuds2.2, row.names = FALSE)
dbWriteTable(con, append = TRUE, name = "collaborations", value = db_collaborations1.0, row.names = FALSE)
dbWriteTable(con, append = TRUE, name = "cours", value = db_cours2.0, row.names = FALSE)
|
55e8dce5d2b4cd215545ce398308af125f032598 | abdae0f889e4dc5aa848c850a213a609a698ec4b | /file1.R | d0b1a15a20ba4b6a3092d4d0e98c689b19ccd804 | [] | no_license | Tusharbiswas/analytics1 | 265f9bdcef312af9af4031591536d804fcf912f8 | 56e1cb09732772ef91c1a5a37f085f587032fe05 | refs/heads/master | 2020-03-26T20:07:04.823175 | 2018-08-20T17:39:41 | 2018-08-20T17:39:41 | 145,305,792 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,895 | r | file1.R | # Data structures
#vectors----
v1=1:100 #create vector from 1 to 100
v2=c(1,4,5,10)
class(v1)
class(v2)
v3=c('a','santa','banta')
v3 #print the vector
v3=c(TRUE,FALSE,T,F,T)
class(v4)
#summary on vectors
mean(v1)
median(v1)
sd(v1)
var(v1)
hist(v1)
hist(women$height)
v2[v2>=5]
x=rnorm(60,mean=60,sd=10)
x
plot(x)
hist(x)
plot(density(x))
abline(v=60)
#rectangles and density together
hist(x,freq=F)
lines(density(x))
hist(x,breaks=10,col=1:10)
lines(density(x))
length(x)
sd(x)
?sample
x1=LETTERS[5:20]
x1
set.seed(1234)
y1=sample(x1)
y1
set.seed(6)
(y2=sample(x1,size=5))
(gender=sample(c('M','F'),size=1000000,replace=TRUE,prob=c(.3,.7)))
(t1=table(gender))
prop.table(t1)
pie(t1)
barplot(t1,col=1:2,horiz=T)
#matrix----
(m1=matrix(1:24,nrow=4))
(m2=matrix(1:24,nrow=4,byrow=T))
(m3=matrix(1:24,ncol=4,byrow=T))
(x=trunc(runif(60,60,100)))
plot(density(x))
(m4=matrix(x,ncol=6))
colSums(m4)
rowSums(m4)
rowMeans(m4)
colMeans(m4)
m4[m4>67]
m4[m4>67&m4<86]
m4[8:10,c(1,3,5)]
rowSums(m4[8:10,c(1,3,5)])
#round, trunc, ceiling, floor
?runif
#array----
#data.frame
#rollno, name, gender, course, marks1, marks2
(rollno=1:60)
(name=paste('student1',1:60,sep = '-'))
name[1:20]
name[c(15,20,37)]
name[-c(1:10)]
rev(name)
name[60:1]
(gender=sample(c('MALE','FEMALE'),size=60, replace=T, prob=c(.3,.7)))
(course=sample(c('BBA','MBA','FPM'),size=60,replace=T,prob=c(.2,.2,.6)))
(marks1=ceiling(rnorm(60,mean=65,sd=7)))
(marks2=ceiling(rnorm(60,mean=65,sd=11)))
(grades=sample(c('A','B','C'),size=60, replace=T))
students=data.frame(rollno,name, gender,course,marks1,marks2,grades, stringsAsFactors=F)
class(students)
summary(students)
students[,c('name')]
students[students$gender=='MALE',c('rollno','gender','marks1')]
students[students$gender=='MALE'& students$grades=='c',c('rollno','gender','marks1')]
students$gender
t1=table(students$gender)
barplot(table(students$course))
student1. student2
students[students$marks>55|students$marks<75,c('name','marks1')]
text(1:3,table(students$course)+5,table(students$course))
str(students)
nrow(students)
names(students)
dim(students)
head(students)
tail(students)
head(students,n=7)
students[10:15,-c(3)]
#avg marks scored by each gender in marks1
#gender, marks1
aggregate(students$marks1,by=list(students$gender),FUN=mean)
aggregate(students$marks2,by=list(students$gender),FUN=max) #max marks scored by each student in course 2
aggregate(students$marks2,by=list(students$course, students$gender),FUN=mean)
#dplyr
library(dplyr)
students%>%group_by(gender)%>%summarise(mean(marks1))
students%>%group_by(course,gender)%>%summarise(mean(marks1))
students%>%group_by(gender)%>%summarise(mean(marks1),min(marks2),max(marks2))
students%>%group_by(course)%>%summarise(mean(marks1))
students%>%group_by(gender)%>%summarise(mean(marks2))
students%>%group_by(course,gender)%>%summarise(meanmarks1=mean(marks1),min(marks2),max(marks2))%>%arrange(desc(meanmarks1))
students%>%arrange(desc(marks1))%>% filter(gender=='MALE')%>%top_n(5)
?sample_frac
sample_frac(students,replace=T,0.1)
?sample_n
sample_n(students, 5, replace = TRUE)
students%>%sample_frac(.1)
students%>%sample_n(10)
students%>%sample_frac(.1)%>%arrange(course)%>%select(name,gender)
students%>%arrange(course,marks1,grades)%>%select(course,gender,marks1)%>%filter(course=='BBA',grades=='B')
#factor
names(students)
students$gender=factor(students$gender)
summary(students$gender)
summary(students$course)
students$course=factor(students$course,ordered=T)
summary(students$course)
students$course=factor(students$course,ordered=T,levels=c('FPM','MBA','BBA'))
summary(students$course)
students$grades
students$grades=factor(students$grades,ordered=T,levels=c('C','B','A'))
summary(students$grades)
students$grades
students
write.csv(students,'./data/iimtrichy.csv')
students2=read.csv('./data/iimtrichy.csv')
students3=read.csv(file.choose())
install.packages('gsheet')
|
c0cc00670060e963a7ab09fb32cb5eebe48e2f38 | 705255987191f8df33b8c2a007374f8492634d03 | /man/update-DataDA-method.Rd | cdd85e96195eb731c45df82965892becc6bed78e | [] | no_license | Roche/crmPack | be9fcd9d223194f8f0e211616c8b986c79245062 | 3d897fcbfa5c3bb8381da4e94eb5e4fbd7f573a4 | refs/heads/main | 2023-09-05T09:59:03.781661 | 2023-08-30T09:47:20 | 2023-08-30T09:47:20 | 140,841,087 | 24 | 9 | null | 2023-09-14T16:04:51 | 2018-07-13T11:51:52 | HTML | UTF-8 | R | false | true | 2,325 | rd | update-DataDA-method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Data-methods.R
\name{update,DataDA-method}
\alias{update,DataDA-method}
\alias{update-DataDA}
\title{Updating \code{DataDA} Objects}
\usage{
\S4method{update}{DataDA}(object, u, t0, trialtime, y, ..., check = TRUE)
}
\arguments{
\item{object}{(\code{DataDA})\cr object you want to update.}
\item{u}{(\code{numeric})\cr the new DLT free survival times for all patients,
i.e. for existing patients in the \code{object} as well as for new patients.}
\item{t0}{(\code{numeric})\cr the time that each patient starts DLT observation
window. This parameter covers all patients, i.e. existing patients in the
\code{object} as well as for new patients.}
\item{trialtime}{(\code{number})\cr current time in the trial, i.e. a followup
time.}
\item{y}{(\code{numeric})\cr the new DLTs for all patients, i.e. for existing
patients in the \code{object} as well as for new patients.}
\item{...}{further arguments passed to \code{Data} update method \code{\link{update-Data}}.
These are used when there are new patients to be added to the cohort.}
\item{check}{(\code{flag})\cr whether the validation of the updated object
should be conducted. See help for \code{\link{update-Data}} for more details
on the use case of this parameter.}
}
\value{
The new, updated \code{\link{DataDA}} object.
}
\description{
\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#stable}{\figure{lifecycle-stable.svg}{options: alt='[Stable]'}}}{\strong{[Stable]}}
A method that updates existing \code{\link{DataDA}} object with new data.
}
\note{
This function is capable of not only adding new patients but also
updates existing ones with respect to \code{y}, \code{t0}, \code{u} slots.
}
\examples{
# Create an object of class 'DataDA'.
my_data <- DataDA(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 1, 1, 0, 0, 1, 0),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
u = c(42, 30, 15, 5, 20, 25, 30, 60),
t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
Tmax = 60
)
# Update the data.
my_data1 <- update(
object = my_data,
y = c(my_data@y, 0), # The 'y' will be updated according to 'u'.
u = c(my_data@u, 20),
t0 = c(my_data@t0, 95),
x = 20,
trialtime = 120 # This is the global timeline for a trial.
)
my_data1
}
|
69cbe5b608efa08997d0051809103b0df8c438fb | db18b7261e6aeee4fbd66ac7c34a94a97eae4375 | /Modelling.R | 0739b108acc466dc18132b603ab6a10dfca73d61 | [] | no_license | NatasjaFortuin/M2P3 | a748ff470712695ff42a7c98287b1c464f402776 | 5e1b3c85a72ab0c16a0e291718f247e4ce83d693 | refs/heads/master | 2020-08-10T22:32:09.593890 | 2019-10-18T11:49:08 | 2019-10-18T11:49:08 | 214,434,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,476 | r | Modelling.R | library(readr)
library(caret)
library(ggplot2)
library(mlbench)
library(e1071)
library(dplyr)
#Load data----
existingproductattributes2017 <- read_csv("existingproductattributes2017.csv")
Exist <- existingproductattributes2017
#Preprocessing----
# dummify the data
DummyVarsExist <- dummyVars(" ~ .", data = Exist)
readyData <- data.frame(predict(DummyVarsExist, newdata = Exist))
# Final selection relevant features----
Final_relevant_vars <- c(
"ProductTypeLaptop","ProductTypeNetbook","ProductTypePC",
"ProductTypeSmartphone","ProductNum","x4StarReviews",
"x3StarReviews","PositiveServiceReview","Volume"
)
# create correlation matrix----
cor(readyData[Final_relevant_vars])
corrplot(readyData)
corrData <- cor(readyData[Final_relevant_vars])
final_df <- readyData[Final_relevant_vars]
head(final_df)
set.seed(15)
#create a 20% sample of the data----
BWsample <- final_df[sample(1:nrow(final_df), 70,replace=FALSE),]
# define an 75%/25% train/test split of the dataset----
inTraining_lm <- createDataPartition(BWsample$Volume, p = .75, list = FALSE)
training_lm <- BWsample[inTraining,]
testing_lm <- BWsample[-inTraining,]
#CV 10 fold
fitControl_lm <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
#### MODELLING ####
#LINEAR MODEL----
#lm model: lmfFit AUTOM GRID
#type: line y based on x model
#package: baseR
#dataframe = final_df
#Y Value = Volume
lmFit <- lm(Volume~.,
data = training_lm)
#training results
lmFit
saveRDS(lmFit, file = "lmFit.rds")
#LM summary lmFit----
summary(lmFit)
#summaryperformance_lmFit
#multiple R-squared Adjusted R-squared
# 0.8699 0.8467
saveRDS(object = lmFit, file = "lmFit.rds")
#Predict Output----
predicted= predict(lmFit, testing_lm)
print(predicted)
str(predicted)
#Save predictions LM Model in separate column----
final_df$predLM <- predict(lmFit, testing_lm)
#LM postresample----
postResample(pred = predict(object = lmFit, newdata = testing_lm), obs = testing_lm$Volume)
##output = RMSE Rsquared MAE
##lmFit = +/-4354.847 0.5672 271.163
#KNN MODEL----
#K-nn model: KNNfFit AUTOM GRID
#type: neighbour based model
#package: caret
#dataframe = final_df
#Y Value = Volume
set.seed(15)
#SET SPLIT 75%/25% for train/test in the dataset
inTrainingKNN <- createDataPartition(BWsample$Volume, p = .75, list = FALSE)
trainingKNN <- BWsample[inTraining,]
testingKNN <- BWsample[-inTraining,]
#10 fold cross validation
fitControlKNN <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
#train knn model with a tuneLenght = `1`(trains with 1 mtry values for knn)
# preProcess=c("center", "scale") removed because not appl on prod types
KNNFit <- train(Volume~.,
data = trainingKNN,
method = "kknn",
trControl=fitControlKNN,
tuneLength = 1
)
#training results
KNNFit
#KNN traning results----
# RMSE Rsquared MAE
# 864.3071 0.8787 MAE 463.236
#KNN summary KNNFit K3----
summary(KNNFit)
#summaryperformance_KNNFit= Min Mean Abs Error: 420.7407, Min Mean S-error 1945
saveRDS(object = KNNFit, file = "KNNFit.rds")
#KNN postresample----
postResample(pred = predict(object = KNNFit, newdata = testingKNN), obs = testingKNN$Volume)
# RMSE Rsquared MAE
# 380.911 0.6624 210.75
#Predict Output----
predicted= predict(KNNFit, testingKNN)
print(predicted)
str(predicted)
#Save predictions KNN Model in separate column----
final_df$predKNN <- predict(KNNFit, testingKNN)
#RF MODEL----
#Random Forest model: rfFit AUTOM GRID
#type: decision tree for mean prediction of individual trees
#package: caret
#dataframe = final_df
#Y Value = Volume
set.seed(15)
#SET SPLIT 75%/25% for train/test in the dataset
inTrainingrf <- createDataPartition(BWsample$Volume, p = .75, list = FALSE)
trainingrf <- BWsample[inTraining,]
testingrf <- BWsample[-inTraining,]
#10 fold cross validation
fitControlrf <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
#train knn model with a tuneLenght = `1`(trains with 1 mtry values for knn)
# preProcess=c("center", "scale") removed because not appl on prod types
rfFit <- train(Volume~.,
data = trainingrf,
method = "rf",
trControl=fitControlrf,
tuneLength = 1
)
#training results
rfFit
#RF traning results----
# RMSE Rsquared MAE
# 870.75 0.978 464.13
saveRDS(object = rfFit, file = "rfFit.rds")
#RF postresample----
postResample(pred = predict(object = rfFit, newdata = testingrf), obs = testingrf$Volume)
# RMSE Rsquared MAE
# 143.58 0.955 101.373
#Predict Output----
predicted= predict(rfFit, testingrf)
print(predicted)
str(predicted)
#Save predictions RF Model in separate column----
final_df$predRF <- predict(rfFit, testingrf)
#SVM MODEL----
#svmLinear2 model: svmFit AUTOM GRID
#type: neighourhood based implicitly maps inputs to high-dimens feature spaces.
#package: e1071
#dataframe = final_df
#Y Value = Volume
set.seed(15)
#SET SPLIT 75%/25% for train/test in the dataset
inTrainingsvm <- createDataPartition(BWsample$Volume, p = .75, list = FALSE)
trainingsvm <- BWsample[inTraining,]
testingsvm <- BWsample[-inTraining,]
#10 fold cross validation
fitControlsvm <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
#train svm model with a tuneLenght = `1`
# preProcess=c("center", "scale") removed because not appl on prod types
svmFit <- train(Volume~.,
data = trainingsvm,
method = "svmLinear2",
trControl=fitControlsvm,
tuneLength = 1
)
#training results
svmFit
#SVM traning results----
# RMSE Rsquared MAE Tuning par cost was held constant at value 0.25
# 787.177 0.9629 433.4216
saveRDS(object = svmFit, file = "svmFit.rds")
#SVM postresample----
postResample(pred = predict(object = svmFit, newdata = testingsvm), obs = testingsvm$Volume)
# RMSE Rsquared MAE
# 392.461 0.5861 243.71
#Predict Output----
predicted= predict(svmFit, testingsvm)
print(predicted)
str(predicted)
#Save predictions SVM Model in separate column----
final_df$predSVM <- predict(svmFit, testingsvm)
str(final_df)
View(final_df)
#create excel----
write.csv(final_df, file = "ExistVolumeInclPred", row.names = TRUE)
#### REVIEW by PLOTS ####
#NETBOOK----
#Model plot LM----
ggplot(data = final_df, aes(x = ProductTypeNetbook, y = predLM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot KNN----
ggplot(data = final_df, aes(x = ProductTypeNetbook, y = predKNN)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot RF----
ggplot(data = final_df, aes(x = ProductTypeNetbook, y = predRF)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot SVM----
ggplot(data = final_df, aes(x = ProductTypeNetbook, y = predSVM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#LAPTOP----
#Model plot LM----
ggplot(data = final_df, aes(x = ProductTypeLaptop, y = predLM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot KNN----
ggplot(data = final_df, aes(x = ProductTypeLaptop, y = predKNN)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot RF----
ggplot(data = final_df, aes(x = ProductTypeLaptop, y = predRF)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot SVM----
ggplot(data = final_df, aes(x = ProductTypeLaptop, y = predSVM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#PC----
#Model plot LM----
ggplot(data = final_df, aes(x = ProductTypePC, y = predLM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot KNN----
ggplot(data = final_df, aes(x = ProductTypePC, y = predKNN)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot RF----
ggplot(data = final_df, aes(x = ProductTypePC, y = predRF)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot SVM----
ggplot(data = final_df, aes(x = ProductTypePC, y = predSVM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#SMARTPHONE----
#Model plot LM----
ggplot(data = final_df, aes(x = ProductTypeSmartphone, y = predLM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot KNN----
ggplot(data = final_df, aes(x = ProductTypeSmartphone, y = predKNN)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot RF----
ggplot(data = final_df, aes(x = ProductTypeSmartphone, y = predRF)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot SVM----
ggplot(data = final_df, aes(x = ProductTypeSmartphone, y = predSVM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Rename columnnames final_df----
head(final_df)
names(final_df)<-c("Laptop","Netbook","PC", "Phone", "ID", "x4Star", "x3Star",
"PosSerRev", "Volume", "predSVM", "predLM", "predKNN", "predRF")
head(final_df)
#Find outliers
outlier_values <- boxplot.stats(final_df$Volume)$out
boxplot(final_df$Volume)
boxplot(final_df$predSVM)
boxplot(final_df$predLM)
boxplot(final_df$predKNN)
boxplot(final_df$predRF)
boxplot(final_df$Volume)$out
#outliers determined as values 7036 and 11204
#find in which row the outliers are
final_df[which(final_df$Volume %in% outlier_values),]
#outliers are in rows 50 (11204) and 73 (7036)
#ERROR Check ----
#Error check is done with Volume & Pred Volume!!
ggplot(data = final_df) +
geom_point(aes(x = Volume, y = predRF)) +
geom_abline(intercept = 1)
View(outliers_values)
#Remove Outliers----
final_df_ExOut <- final_df[-which(final_df$Volume %in% outlier_values),]
#check removal with boxplot
boxplot(final_df_ExOut)
boxplot(final_df_ExOut$Volume)
#Remove Duplicates----
duplicated(final_df_ExOut$Volume)
duplicates <- duplicated(final_df_ExOut$Volume)
final_df_ExOut[which (final_df_ExOut$Volume %in% duplicates),]
#didn't select the duplicates from prod id 135 tm 141 that I want to remove
#so nothing removed yet
duplicated(final_df_ExOut$PosSerRev)
duplicates2 <- duplicated(final_df_ExOut$PosSerRev)
final_df_ExOut[which (final_df_ExOut$PosSerRev %in% duplicates),]
#works with normalized PosServRev 0-1 so is of no use. Nothing removed.
#doesn't work because duplicate values are not recognized as such
#I know it is product ID's 134 tm 141. I want to keep 134 and remove rest
Finaldf_cleaned <- final_df_ExOut[!(final_df_ExOut$ID==135
final_df_ExOut$ID==136
final_df_ExOut$ID==137
final_df_ExOut$ID==138
final_df_ExOut$ID==139
final_df_ExOut$ID=140
final_df_ExOut$ID=141),]
#not working. Tried +, , AND etc...
subset(Finaldf_cleaned, ID!=135)
subset(Finaldf_cleaned, ID!=136)
subset(Finaldf_cleaned, ID!=137)
subset(Finaldf_cleaned, ID!=138)
subset(Finaldf_cleaned, ID!=139)
subset(Finaldf_cleaned, ID!=140)
subset(Finaldf_cleaned, ID!=141)
View(Finaldf_cleaned)
#did not work, only removes one value at a time. Replace dfcleaned with
#original dataframe ex Outliers
Finaldf_cleaned <- final_df_ExOut
str(Finaldf_cleaned)
Finaldf_cleaned <- distinct(.data = final_df_ExOut, PosSerRev, x4Star, x3Star, Volume, .keep_all = TRUE)
rm(Finalsdf_cleaned)
str(Finaldf_cleaned)
View(Finaldf_cleaned)
#remove prediction colums with dplyr in order to re run the modelling
Finaldf_cleaned <- select (Finaldf_cleaned, -c(predLM, predRF, predKNN, predSVM))
View((Finaldf_cleaned))
set.seed(15)
#create a 20% sample of the data----
BWsample2 <- Finaldf_cleaned[sample(1:nrow(Finaldf_cleaned), 70,replace=FALSE),]
# define an 75%/25% train/test split of the dataset----
inTraining_lm2 <- createDataPartition(BWsample2$Volume, p = .75, list = FALSE)
training_lm2 <- BWsample2[inTraining,]
testing_lm2 <- BWsample2[-inTraining,]
#### MODELLING CLEANED ####
#LINEAR MODEL----
#lm model: lmfFit2 AUTOM GRID
#type: line y based on x model
#package: baseR
#dataframe = Finaldf_cleaned
#Y Value = Volume
lmFit2 <- lm(Volume~.,
data = training_lm2)
#training results
lmFit2
saveRDS(lmFit2, file = "lmFit2.rds")
#LM summary lmFit----
summary(lmFit2)
#summaryperformance_lmFit2
#multiple R-squared Adjusted R-squared
# 0.6784 0.6294
saveRDS(object = lmFit2, file = "lmFit2.rds")
#Predict Output----
predicted= predict(lmFit2, testing_lm2)
print(predicted)
str(predicted)
#Save predictions LM Model in separate column----
Finaldf_cleaned$predLM <- predict(lmFit2, Finaldf_cleaned)
#LM postresample----
postResample(pred = predict(object = lmFit2, newdata = testing_lm2), obs = testing_lm2$Volume)
##output = RMSE Rsquared MAE
##lmFit = +/-320.680 0.6456 223.876
#KNN MODEL----
#K-nn model: KNNfFit2 AUTOM GRID
#type: neighbour based model
#package: caret
#dataframe = Finaldf_Cleaned
#Y Value = Volume
set.seed(15)
#SET SPLIT 75%/25% for train/test in the dataset
inTrainingKNN2 <- createDataPartition(BWsample2$Volume, p = .75, list = FALSE)
trainingKNN2 <- BWsample2[inTraining,]
testingKNN2 <- BWsample2[-inTraining,]
#10 fold cross validation
fitControlKNN2 <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
#train knn model with a tuneLenght = `1`(trains with 1 mtry values for knn)
# preProcess=c("center", "scale") removed because not appl on prod types
KNNFit2 <- train(Volume~.,
data = trainingKNN2,
method = "kknn",
trControl=fitControlKNN2,
tuneLength = 1
)
#training results
KNNFit2
#KNN traning results----
# RMSE Rsquared MAE
# 266.3007 0.84267 MAE 159.5161
#KNN summary KNNFit2 K3----
summary(KNNFit2)
#summaryperformance_KNNFit= Min Mean Abs Error: 150.2986, Min Mean S-error 8705
saveRDS(object = KNNFit2, file = "KNNFit2.rds")
#KNN postresample----
postResample(pred = predict(object = KNNFit2, newdata = testingKNN2), obs = testingKNN2$Volume)
# RMSE Rsquared MAE
# 191.826 0.8312 102.92
#Predict Output----
predicted= predict(KNNFit2, testingKNN2)
print(predicted)
str(predicted)
#Save predictions KNN Model in separate column----
Finaldf_cleaned$predKNN <- predict(KNNFit2, Finaldf_cleaned)
#RF MODEL----
#Random Forest model: rfFit2 AUTOM GRID
#type: decision tree for mean prediction of individual trees
#package: caret
#dataframe = Finaldf_cleaned
#Y Value = Volume
set.seed(15)
#SET SPLIT 75%/25% for train/test in the dataset
inTrainingrf2 <- createDataPartition(BWsample2$Volume, p = .75, list = FALSE)
trainingrf2 <- BWsample2[inTraining,]
testingrf2 <- BWsample2[-inTraining,]
#10 fold cross validation
fitControlrf2 <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
#train knn model with a tuneLenght = `1`(trains with 1 mtry values for knn)
# preProcess=c("center", "scale") removed because not appl on prod types
rfFit2 <- train(Volume~.,
data = trainingrf2,
method = "rf",
trControl=fitControlrf2,
tuneLength = 1
)
#training results
rfFit2
#RF traning results----
# RMSE Rsquared MAE
# 277.3768 0.8645 193.8815
saveRDS(object = rfFit2, file = "rfFit2.rds")
#RF postresample----
postResample(pred = predict(object = rfFit2, newdata = testingrf2), obs = testingrf2$Volume)
# RMSE Rsquared MAE
# 137.120 0.9509 109.22
#Predict Output----
predicted= predict(rfFit2, testingrf2)
print(predicted)
str(predicted)
#Save predictions RF Model in separate column----
Finaldf_cleaned$predRF <- predict(rfFit2, Finaldf_cleaned)
#SVM MODEL----
#svmLinear2 model: svmFit2 AUTOM GRID
#type: neighourhood based implicitly maps inputs to high-dimens feature spaces.
#package: e1071
#dataframe = Finaldf_cleaned
#Y Value = Volume
set.seed(15)
#SET SPLIT 75%/25% for train/test in the dataset
inTrainingsvm2 <- createDataPartition(BWsample2$Volume, p = .75, list = FALSE)
trainingsvm2 <- BWsample2[inTraining,]
testingsvm2 <- BWsample2[-inTraining,]
#10 fold cross validation
fitControlsvm2 <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
#train svm model with a tuneLenght = `1`
# preProcess=c("center", "scale") removed because not appl on prod types
svmFit2 <- train(Volume~.,
data = trainingsvm2,
method = "svmLinear2",
trControl=fitControlsvm2,
tuneLength = 1)
#training results
svmFit2
#SVM traning results----
# RMSE Rsquared MAE Tuning par cost was held constant at value 0.25
# 511.667 0.7061 281.878
saveRDS(object = svmFit2, file = "svmFit2.rds")
#SVM postresample----
postResample(pred = predict(object = svmFit2, newdata = testingsvm2), obs = testingsvm2$Volume)
# RMSE Rsquared MAE
# 286.367 0.63371 121.99
#Predict Output----
predicted= predict(svmFit2, testingsvm2)
print(predicted)
str(predicted)
#Save predictions SVM Model in separate column----
Finaldf_cleaned$predSVM <- predict(svmFit2, Finaldf_cleaned)
str(Finaldf_cleaned)
View(Finaldf_cleaned)
#### IMPROVE DATAFRAME FOR PLOTS ####
PredData <- Finaldf_cleaned
as.integer(PredData$predLM)
as.integer(PredData$predKNN)
as.integer(PredData$predRF)
as.integer(PredData$predSVM)
View(PredData)
#ERROR Check ----
#Error check is done with Volume & Pred Volume!!
ggplot(data = PredData) +
geom_point(aes(x = Volume, y = predLM)) +
geom_abline(intercept = 1)
ggsave("Errorplot_LM.png", width = 5, height = 5)
PlotErrorCheck <- ggplot(data = PredData) +
geom_point(aes(x = Volume, y = predLM)) +
geom_abline(intercept = 1)
ggplot(data = PredData) +
geom_point(aes(x = Volume, y = predKNN)) +
geom_abline(intercept = 1)
ggsave("Errorplot_KNN.png", width = 5, height = 5)
ggplot(data = PredData) +
geom_point(aes(x = Volume, y = predRF)) +
geom_abline(intercept = 1)
ggsave("Errorplot_RF.png", width = 5, height = 5)
ggplot(data = PredData) +
geom_point(aes(x = Volume, y = predSVM)) +
geom_abline(intercept = 1)
ggsave("Errorplot_SVM.png", width = 5, height = 5)
#### REVIEW by PLOTS2 ####
#NETBOOK----
#Model plot LM----
ggplot(data = PredData, aes(x = Netbook, y = predLM)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE)
#Model plot KNN----
ggplot(data = PredData, aes(x = Netbook, y = predKNN)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE)
#Model plot RF----
ggplot(data = PredData, aes(x = Netbook, y = predRF)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE)
#Model plot SVM----
ggplot(data = PredData, aes(x = Netbook, y = predSVM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#LAPTOP----
#Model plot LM----
ggplot(data = PredData, aes(x = Laptop, y = predLM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot KNN----
ggplot(data = PredData, aes(x = Laptop, y = predKNN)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot RF----
ggplot(data = PredData, aes(x = Laptop, y = predRF)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot SVM----
ggplot(data = PredData, aes(x = Laptop, y = predSVM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#PC----
#Model plot LM----
ggplot(data = PredData, aes(x = PC, y = predLM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot KNN----
ggplot(data = PredData, aes(x = PC, y = predKNN)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot RF----
ggplot(data = PredData, aes(x = PC, y = predRF)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#Model plot SVM----
ggplot(data = PredData, aes(x = PC, y = predSVM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
#SMARTPHONE----
#Model plot LM----
ggplot(data = PredData, aes(x = Phone, y = predLM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
ggsave("Smartphoneplot_LM.png", width = 5, height = 5)
#Model plot KNN----
ggplot(data = PredData, aes(x = Phone, y = predKNN)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
ggsave("Smartphoneplot_KNN.png", width = 5, height = 5)
#Model plot RF----
ggplot(data = PredData, aes(x = Phone, y = predRF)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
ggsave("Smartphoneplot_RF.png", width = 5, height = 5)
#Model plot SVM----
ggplot(data = PredData, aes(x = Phone, y = predSVM)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
ggsave("SmartphoneplotRF.png", width = 5, height = 5)
|
66a9a7faa56a0dad040f612ac3f26ea15ba2fd90 | e43ccc719a5df63664598db7614d7b10e3b4d4fb | /man/p1x2.Rd | 51342f0806c7a70459d796bdc3eb41e29484c773 | [] | no_license | opisthokonta/goalmodel | 58fa2236e894df745f4f5985e16c863e55fd6272 | 55a33c620a1c36b51ad634f0e47abf402766cf56 | refs/heads/master | 2023-09-03T09:30:18.823581 | 2023-08-29T08:39:50 | 2023-08-29T08:39:50 | 153,664,398 | 98 | 21 | null | 2019-12-28T23:10:27 | 2018-10-18T17:49:21 | R | UTF-8 | R | false | true | 987 | rd | p1x2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/goalmodel_predict.R
\name{p1x2}
\alias{p1x2}
\title{Compute 1x2 probabilities from expected goals.}
\usage{
p1x2(expg1, expg2, model = "poisson", dispersion = NULL, rho = NULL, uprx = 25)
}
\arguments{
\item{expg1}{Non-negative numeric. The expected number of goals.}
\item{expg2}{Non-negative numeric. The expected number of goals.}
\item{model}{String indicating whether the goals follow a 'poisson' model (default), a Negative Binomial ('negbin'), or Conway-Maxwell-Poisson ('cmp') model.}
\item{dispersion}{Non-negative numeric. The dispersion parameter in the Negative Binomial model or the Conway-Maxwell-Poisson model.}
\item{rho}{Numeric. The Dixon-Coles adjustment.}
\item{uprx}{Numeric. The upper limit for evaluating the underlying distributions.}
}
\value{
A matrix with 3 columns with one row for each pair of expected goals.
}
\description{
Compute 1x2 probabilities from expected goals.
}
|
ab3be2c6b73ddd487f5bd92b1ec5379a2957a0c5 | 2c46f227e81ceec714b4a0170ae39bb0b74bfd5d | /R/summarize-alg.R | 4415c1b41f1ebca4447dbd791b70384dab6d74a4 | [] | no_license | tengfei/chromatoplots | f25d95a521ca09eceb79498588df9b1f93047ae6 | 858cec990aafbf58b8e95bbdc37414a7ac6b833c | refs/heads/master | 2021-01-02T09:33:06.358088 | 2013-07-12T07:03:15 | 2013-07-12T07:03:15 | 926,471 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,542 | r | summarize-alg.R |
summarize.common <- function(object) {
common <- sum_common_peaks(object@peaks, object@comps)
comps <- object@comps[,colnames(object@comps) != "quantity"]
object@comps <- cbind(comps, quantity = common)
object
}
find_spectra <- function(peaks)
{
comp_peaks <- split(seq_len(nrow(peaks)), interaction(peaks[,"comp"], peaks[,"sample"]))
existing_ints <- sapply(comp_peaks, length) > 0
comp_peaks <- comp_peaks[existing_ints]
mass <- peaks[,"mz"]
height <- peaks[,"maxf"]
sigma <- peaks[,"sigma"]
max_mass <- max(mass)
spectrum <- function(p) {
y <- numeric(max_mass)
y[mass[p]] <- 2*height[p]*sqrt(pi/2)*sigma[p]
y
}
sapply(comp_peaks, spectrum)
}
sum_common_peaks <- function(peaks, comps)
{
# ensure component order is compatible with find_spectra()
comps <- cbind(comps, id = seq_len(nrow(comps)))
comps <- comps[order(comps[,"comp"]),]
comps <- comps[order(comps[,"sample"]),]
spectra <- t(find_spectra(peaks))
sums <- unsplit(by(spectra, comps[,"group"], function(group) {
masses <- apply(group, 2, function(column) all(column != 0))
apply(group[,masses,drop=FALSE], 1, sum)
}), comps[,"group"])
result <- numeric(length(sums))
result[comps[,"id"]] <- sums
result
}
scale_log_quantities <- function(comps)
{
quantity <- suppressWarnings(log(comps[,"quantity"]))
quantity[is.infinite(quantity)] <- NA
unsplit(tapply(quantity, comps[,"sample"], function(sample)
{
sample - mean(sample, na.rm = TRUE)
}), comps[,"sample"]) + mean(quantity, na.rm = TRUE)
}
|
66990599857f0e06ace479698eef7b26dba5b0a5 | cedc3d2c404da16d8dac0d2e481de7b329266da8 | /CPM_TPM.R | e888a412d310742e89b34a4775325c28bff2f138 | [] | no_license | holiday10/my-perl | dd98bd8d52604f75e871e6fcfa33cf318a475a89 | 6a25e9fb3d2e7542f33afd9eebc3413da0385083 | refs/heads/main | 2023-02-19T23:49:44.166666 | 2021-01-19T14:07:56 | 2021-01-19T14:07:56 | 329,888,464 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 947 | r | CPM_TPM.R | setwd("E:/omics_data/ml/FF")#设置工作目录
countdata<-read.table("37emb.mod.counts",sep="\t",header = T,row.names = 1)
metadata <- countdata[,1:2]#提取基因信息count数据前的几列,第一列认为是ID（row.names=1），第二列开始索引为1
countdata <- countdata[,3:ncol(countdata)]#提取counts数，counts数据主题部分,从metadata基因信息往后的第一列开始
prefix<-"mla-37emb-mod"#设置输出文件前缀名
cpm <- t(t(countdata)/colSums(countdata) * 1000000)#参考cpm定义
avg_cpm <- data.frame(avg_cpm=rowMeans(cpm))
#-----TPM Calculation------
kb <- metadata$Length / 1000
rpk <- countdata/kb
tpm <- t(t(rpk)/colSums(rpk) * 1000000)
avg_tpm <- data.frame(avg_tpm=rowMeans(tpm))
write.csv(avg_tpm,paste0(prefix,"_avg_tpm.csv"))
write.csv(avg_cpm,paste0(prefix,"_avg_cpm.csv"))
write.csv(tpm,paste0(prefix,"jj_tpm.csv"))
write.csv(cpm,paste0(prefix,"_cpm.csv")) |
43877b5e597d54e90d509e1bd25ee35ad56a308d | f4e7e4cafdba256f221192abe667f36fddc17228 | /man/predictpower.Rd | e662fdbc5354a0814de9f1e0c50c61ceef76f93a | [] | no_license | Rommelio-coli/SSPA | 61d3943592d716061342dddcc6b8bb6d9571abfc | b28a35d65a13ef593e648378db92bf71486c66fe | refs/heads/master | 2023-06-18T21:11:05.477651 | 2021-07-21T19:54:36 | 2021-07-21T19:54:36 | 388,225,161 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 607 | rd | predictpower.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/powerandsamplesize.R
\name{predictpower}
\alias{predictpower}
\title{Predict power for given vector of sample sizes}
\usage{
predictpower(object, samplesizes, alpha = 0.1, verbose = FALSE, plot = FALSE)
}
\arguments{
\item{object}{of class 'SampleSize'}
\item{samplesizes}{vector of total sample sizes.}
\item{alpha}{FDR.}
\item{verbose}{TRUE/FALSE}
\item{plot}{TRUE/FALSE}
}
\value{
predicted power.
}
\description{
Predict power for given vector of sample sizes
}
\details{
details follow.
}
\author{
Maarten van Iterson
}
|
d127d0ab9c5bf9dfc22b9f5610765cec41e1d677 | 80ed2246df21e2ff4f29793602966b699a78d899 | /code/global_editing_index.R | 60862f1fb70f322c1222b5c5b2ae23e2284857de | [] | no_license | bahlolab/brain_scRNAed | cff59bddca84f82a26f3d9f6b45be02ce6722bdf | db8b50c423ae71a6a97a9df4d7a9942ab3a1f95c | refs/heads/master | 2020-09-08T10:34:08.966450 | 2019-11-13T01:38:10 | 2019-11-13T01:38:10 | 221,109,416 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,183 | r | global_editing_index.R | #cell-wise editing proportion testing
#devtools::install_github("tidymodels/broom")
library(tidyverse)
here::here()
BL_joinKey <- readRDS('data/phs000834/BL_metadata_highQualCells.Rds')
mapping_stats_BL <- readRDS("data/mapping_output/mapping_stats_BL.Rds")
dt_filt <- readRDS("data/phs000834/dt_filt.Rds")
dt_siteStats_TDjoin <- readRDS("data/phs000834/dt_siteStats_TDjoin.Rds")
#samtools bqsr depth; contains candidate editing sites in each neuron covered by at least 5 reads.
gte5_DPsites <- read_tsv("data/samtools_depth_output/samDepth_BQSR_sitesGTE5.out", col_names = FALSE,
col_types = cols('X2'=col_character())) %>%
magrittr::set_colnames(c('sample','siteID','depth')) %>%
filter(sample %in% BL_joinKey$SRA) %>%
filter(siteID %in% dt_siteStats_TDjoin$siteID)
#plot covered sites per cell
gte5_DPsites %>% count(sample) %>% ggplot(aes(x=n)) + geom_histogram(fill='dodger blue')
gte5_DPsites %>% count(sample) %>% summary()
#plot n. cells per covered site
gte5_DPsites %>% count(siteID) %>% ggplot(aes(x=n)) + geom_histogram(fill='dodger blue')
gte5_DPsites %>% count(siteID) %>% summary()
#### ### ### ### ### ### ### ### ### ### ###
### ### ### GLOBAL EDITING INDEX ### ### ###
#### ### ### ### ### ### ### ### ### ### ###
### Calculate cell-wise editing as a proportion of covered sites ###
cell_edProp <- left_join(gte5_DPsites %>% count(sample), #n sites covered ≥ 5 high-quality (de-duplicated BQSR) reads per cell.
dt_filt %>% filter(siteID %in% dt_siteStats_TDjoin$siteID) %>% count(sample), by='sample',
suffix=c('_totalCov',"_ed")) %>%
mutate(edProp = n_ed/n_totalCov)
saveRDS(cell_edProp, "data/stat_tests/GEI.Rds")
cell_edProp %>% left_join(BL_joinKey,by=c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
ggplot(aes(x=area,y=edProp, fill=neuType,col=neuType)) +
geom_point(position=position_jitterdodge(jitter.width = 0.2,dodge.width = 0.8), cex=0.25) +
geom_boxplot(alpha=0) +
ylab('Proportion of covered sites edited') + xlab('Brodmann area')
#### Neuronal GroupID ####
#uniquely mapped reads
mapping_stats_BL %>% left_join(BL_joinKey,by=c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
ggplot(aes(x=Group_ID,y=Uniquely_mapped_reads_number, fill=neuType,col=neuType)) +
geom_point(position=position_jitterdodge(jitter.width = 0.2,dodge.width = 0.8), cex=0.25) +
geom_boxplot(alpha=0)
#GEI
# add combined Ex and In groups
## FIG 4c
cell_edProp %>% left_join(BL_joinKey,by=c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
mutate("Collapsed" = neuType) %>%
gather(key,value,Group_ID,`Collapsed`) %>%
mutate(key=factor(key,levels=c('Group_ID','Collapsed'))) %>%
ggplot(aes(x=value,y=edProp, fill=neuType,col=neuType)) +
geom_point(position=position_jitterdodge(jitter.width = 0.2,dodge.width = 0.8), cex=0.25) +
geom_boxplot(alpha=0) +
#facet_wrap(~key)
ggforce::facet_row(vars(key), scales='free_x',space='free') +
xlab('Neuronal phenotype') + ylab('Proportion of sites edited') +
theme_grey() + theme(legend.position = "NONE")
ggsave('charts/Figure_4c.pdf', width=7,height=3.5)
## FIG 4b
cell_edProp %>% left_join(BL_joinKey,by=c('sample'='SRA')) %>%
filter(neuType!="NoN") %>% mutate(`Neuronal type`=neuType) %>%
mutate(area=factor(area,levels=paste0('BA', c(8,10,21,22,41,17)))) %>%
ggplot(aes(x=area,y=edProp, fill=`Neuronal type`,col=`Neuronal type`)) +
geom_point(position=position_jitterdodge(jitter.height=0, jitter.width = 0.15,dodge.width = 0.8),alpha=0.5, cex=0.25) +
geom_boxplot(alpha=0) +
xlab('Brodmann area') + ylab('Proportion of sites edited') +
theme(legend.position='Null')
ggsave('charts/4b.pdf',width=7,height=3.5)
#Calculate mean cell type proportions across cortical regions
BL_joinKey %>% group_by(area) %>% filter(neuType !="NoN") %>% count(neuType) %>%
mutate(prop=n/sum(n)) %>% group_by(neuType) %>% summarize(meanProp = mean(prop))
#Count neuronal type per cortical region
p1 <- cell_edProp %>% left_join(BL_joinKey,by=c('sample'='SRA')) %>%
filter(neuType!="NoN") %>% mutate(`Neuronal type`=neuType) %>%
mutate(area=factor(area,levels=paste0('BA', rev(c(8,10,21,22,41,17))))) %>%
ggplot(aes(x=area,y=edProp, fill=`Neuronal type`,col=`Neuronal type`)) +
geom_point(position=position_jitterdodge(jitter.height=0, jitter.width = 0.15,dodge.width = 0.8),alpha=0.5, cex=0.25) +
geom_boxplot(alpha=0) +
xlab('Brodmann area') + ylab('Proportion of sites edited') +
theme(legend.position='bottom') +
coord_flip()
source('code/colour_palettes.R')
col_mat_full <- matrix(rep(c(drsimonj_pal('hot')(8),drsimonj_pal('cool')(8)),16),ncol=16,nrow=16)
p2 <- cell_edProp %>%
left_join(BL_joinKey,by=c('sample'='SRA')) %>%
mutate(area=factor(area,levels = paste0('BA', c(8,10,21,22,41,17)))) %>%
filter(neuType!="NoN") %>% mutate(`Neuronal type`=neuType) %>%
group_by(area,neuType,Group_ID) %>% summarize("N. nuclei"=n()) %>%
ggplot(aes(x=neuType,y=`N. nuclei`)) + geom_col(aes(fill=Group_ID),position='stack',col='white') +
xlab('') +
coord_flip() +
theme(legend.position = 'bottom') +
scale_fill_manual(values=col_mat_full) +
ggforce::facet_col(vars(area), scales='free',space='free')
cowplot::plot_grid(p1 + theme_grey(), p2 + theme_grey() + theme(legend.position='bottom'),
labels='AUTO',align='h',axis='b')
Fig4a <- p1 + theme_grey() + theme(legend.position='bottom')
ggsave(plot=Fig4a, 'charts/Fig4a.pdf',width=4,height=7)
Fig4b <- p2 + theme_grey() + theme(legend.position='bottom')
ggsave(plot=Fig4b, 'charts/Fig4b.pdf',width=4,height=7)
###### ###### ###### ###### ###### ###### ######
###### ###### ###### ###### ###### ###### ######
broom::tidy(summary(lm(map_sum ~ factor(neuType),
data = mapping_stats_BL %>%
left_join(BL_joinKey, by = c('sample'='SRA')) %>%
filter(neuType!="NoN") )))
#No significant difference in read mapping numbers for excitatory vs inhibitory neurons
broom::tidy(summary(
lm(map_sum ~ factor(area),
data = mapping_stats_BL %>%
left_join(BL_joinKey, by = c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
mutate(map_sum = Uniquely_mapped_reads_number + Number_of_reads_mapped_to_multiple_loci)))) %>%
arrange(p.value)
#Significant differences in read total mapping per cortical region.
broom::tidy(summary(
lm(map_sum ~ factor(Group_ID),
data = mapping_stats_BL %>%
left_join(BL_joinKey, by = c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
mutate(map_sum = Uniquely_mapped_reads_number + Number_of_reads_mapped_to_multiple_loci)))) %>%
arrange(p.value)
#Significant differences in total read mapping per neuronal sub-group: Ex2, In6, In7 and In8 are different to Ex1.
#### TEST GEI by neuronal sub-type (Group_ID) ####
broom::tidy(summary(
lm(edProp ~ factor(Group_ID),
data = cell_edProp %>%
left_join(BL_joinKey, by = c('sample'='SRA')) %>%
filter(neuType!="NoN")))) %>%
arrange(p.value) %>% mutate(FDR=p.adjust(p.value,method="BH")) %>%
filter(p.value > 0.05)
#all sub-groups EXCEPT for Ex8 and In3 have GEI significantly different to Ex1.
#some relationship between mapping stats / total coverage and editing proportion
mapping_stats_BL %>% left_join(cell_edProp,by='sample') %>%
ggplot(aes(x=map_sum,y=edProp)) + geom_hex(bins=100) +
geom_smooth(method="lm")
mapping_stats_BL %>% left_join(cell_edProp,by='sample') %>%
ggplot(aes(x=n_totalCov, y=edProp)) + geom_hex(bins=100) +
geom_smooth(method="lm")
#test collapsed neuType controlling for read coverage
broom::tidy(summary(
lm(edProp ~ factor(neuType) + n_totalCov, # + map_sum + Number_of_reads_mapped_to_multiple_loci
data = cell_edProp %>%
left_join(BL_joinKey, by = c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
left_join(mapping_stats_BL,by='sample')))) %>%
arrange(p.value) %>% mutate(FDR=p.adjust(p.value,method="BH"))
#control for n sites covered [and/or sum of mapped reads]
#test Group_ID
lm_Group_ID_GEI <- broom::tidy(summary(
lm(edProp ~ factor(Group_ID) + n_totalCov , # + map_sum
data = cell_edProp %>%
left_join(BL_joinKey, by = c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
left_join(mapping_stats_BL,by='sample')))) %>%
arrange(estimate) %>% mutate(FDR=p.adjust(p.value,method="BH"))
lm_Group_ID_GEI %>% filter(FDR>0.05)
#GEI differences remain after controlling for site coverage.
#test cortical area ± total coverage
lm_area_GEI <- broom::tidy(summary(
lm(edProp ~ factor(area),
data = cell_edProp %>%
left_join(BL_joinKey, by = c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
left_join(mapping_stats_BL,by='sample')))) %>%
arrange(estimate)
lm_area_GEI_ctrlCov <- broom::tidy(summary(
lm(edProp ~ factor(area) + n_totalCov,
data = cell_edProp %>%
left_join(BL_joinKey, by = c('sample'='SRA')) %>%
filter(neuType!="NoN") %>%
left_join(mapping_stats_BL,by='sample')))) %>%
arrange(estimate)
rbind(lm_area_GEI %>% mutate(covar='none'),
lm_area_GEI_ctrlCov %>% mutate(covar = 'n_totalCov')) %>%
select(term,estimate,covar) %>%
spread(covar,estimate) %>% filter(term!='(Intercept)', term!='n_totalCov') %>%
ggplot(aes(x=none,y=n_totalCov,col=term)) + geom_point() + geom_smooth(method='lm') +
geom_hline(yintercept = 0)+ geom_vline(xintercept = 0)
#only BA22 sign changes.
#for STables
saveRDS(lm_area_GEI, 'data/stat_tests/lm_area_GEI.Rds')
saveRDS(lm_Group_ID_GEI, 'data/stat_tests/lm_Group_ID_GEI.Rds')
###### ###### ###### ###### ###### ###### ###### ######
###### ###### ###### ###### ###### ###### ###### ######
###### ###### ###### ###### ###### ###### ###### ######
#Do inhibitory neurons have greater editing in shared genes; or editing in e.g. inhib.-specific genes?
dt_depth <- left_join(gte5_DPsites, dt_filt %>% filter(siteID %in% gte5_DPsites$siteID), by=c('siteID','sample')) %>%
left_join(BL_joinKey %>% select(SRA,neuType,area), by=c('sample'='SRA'))
# saveRDS(dt_depth, 'data/phs000834/dt_depth.Rds')
# dt_depth <- readRDS('data/phs000834/dt_depth.Rds')
dt_depth_summ <- dt_depth %>% filter(neuType!="NoN") %>% group_by(sample, neuType) %>% summarize(meanDP = mean(depth),medDP = median(depth))
dt_depth_summ %>% ggplot(aes(x=medDP)) + geom_histogram(aes(fill=neuType)) +
facet_wrap(~ neuType, ncol = 1, scales='free_y') +
xlab('Median editing site coverage')
dt_depth_summ %>% group_by(neuType) %>% summarize(meanMED = mean(medDP))
broom::tidy(lm(medDP ~ factor(neuType), data = dt_depth_summ))
# --> Inhibitory neurons have an averaeg median edSite coverage 0.5 units greater than excitatory neurons.
#sample 500 cellsof each neuronal type
set.seed(1234); sample_Ex_In <- dt_depth %>%
select(neuType, sample) %>% distinct() %>%
filter(neuType!="NoN") %>%
group_by(neuType) %>% sample_n(500)
dt_depth %>% filter(str_detect(siteID,"1_")) %>% #sites on chromosome 1
filter(!is.na(alt_al)) %>%
filter(sample %in% sample_Ex_In$sample) %>%
group_by(siteID) %>% mutate(nCells = n()) %>% ungroup() %>%
select(nCells, everything()) %>% arrange(desc(nCells)) %>% #--> exclude marker genes
filter(nCells > 200) %>% count(neuType)
#For inhibitory neurons there are 82 sites in chr1 in > 200 cells; excitatory have 160 sites in chr1 in >200 cells.
#Do In. neurons have more sites than Ex., or greater editing per site?
dt_depth %>% count(sample, neuType) %>%
filter(neuType!="NoN") %>%
ggplot(aes(x=n)) +
geom_histogram(aes(fill=neuType),bins=50)
dt_depth %>%
filter(!is.na(alt_al)) %>%
count(sample, neuType) %>%
filter(neuType!="NoN") %>%
ggplot(aes(x=n)) +
geom_histogram(aes(fill=neuType),bins=50)
#Ex have more editing sites covered, and more edited sites, than Inhibitory...
dt_depth %>% select(sample,siteID,depth,alt_al,neuType) %>%
filter(neuType!="NoN") %>%
mutate(status = ifelse(is.na(alt_al),0,1)) %>%
group_by(neuType) %>% summarize(meanEd = mean(status))
#But In neurons have more editing as a _proportion_ of sites covered.
#N. and % of sites are restricted to In / Ex neurons
dt_depth %>% filter(neuType!="NoN") %>% count(neuType)
dt_depth %>% filter(neuType!="NoN") %>% group_by(neuType) %>% count(siteID) %>%
arrange(siteID) %>%
ungroup() %>% count(siteID,sort=TRUE) %>% count(n)
#Only 253 sites were unique one neuronal type. 99.4% sites detected in both cell types.
1-(253/sum(253,40608))
### ### ### ### ### ### ### ### ###
# If ensemble averaging is suppressing novel sites, we expect those sites to be expressed in small numbers of cells.
dt_siteStats_filt <- readRDS('data/phs000834/dt_siteStats_filt.Rds')
dt_siteStats_filt %>% select(site_type,n_Cells) %>%
mutate(site_status = ifelse(str_detect(site_type,'Novel'),'Novel','Reported')) %>%
ggplot(aes(x=site_type,y=log10(n_Cells))) +
geom_boxplot(aes(col=site_type),outlier.alpha = 0) +
geom_jitter(width=0.05,cex=0.1,alpha=0.25, aes(col=site_type)) + coord_flip() +
scale_color_brewer(palette='Paired') + xlab('Site type') +
theme(legend.position = 'None')
ggsave('charts/Ncells_per_site_bySiteType.pdf',width=6,height=5)
#### #### #### #### #### #### #### ####
dt_siteStats_filt %>% select(site_type,n_Cells) %>%
mutate(site_status = ifelse(str_detect(site_type,'Novel'),'Novel','Reported')) %>%
mutate(log_nCells = log10(n_Cells),
site_status = factor(site_status)) %>%
do(broom::tidy(lm(log_nCells ~ site_status, data = .)))
dt_siteStats_filt %>% select(site_type,n_Cells) %>%
mutate(site_status = ifelse(str_detect(site_type,'Novel'),'Novel','Reported')) %>%
mutate(log_nCells = log10(n_Cells),
site_status = factor(site_status)) %>%
do(broom::tidy(lm(log_nCells ~ site_type, data = .)))
#Indeed, previously reported nonRep and rep-nonAlu sites are edited in significantly more cells than Alu sites; \
# whereas novel sites in each class are detected in significantly fewer cells on average than reported sites.
|
3d5e1f7528989d5ac9da1b690a295975ce6a7e1e | 26f61017226007122b72c7076023b7eee4ff4d73 | /cachematrix.R | dd86a94aaeec9414667c58a5a3588b529ecb7613 | [] | no_license | hessier00/ProgrammingAssignment2 | 5612617d8d147e485d795612f9aa018459cb4c44 | 71f04dd52a185360e6b9ffcefce5736764c0565c | refs/heads/master | 2021-01-24T19:52:04.006785 | 2015-07-24T09:55:31 | 2015-07-24T09:55:31 | 39,617,724 | 0 | 0 | null | 2015-07-24T07:23:24 | 2015-07-24T07:23:23 | null | UTF-8 | R | false | false | 3,138 | r | cachematrix.R | ## This pair of functions are used to effeciently calculate matrix inverses
## Once the inverse of a specific matrix is calculated,
## it's cached, allowing the calculation function to return
## the correct answer without re-preforming the underlying calculation
## makeCacheMatrix adds functions to support caching to a vanilla matrix
makeCacheMatrix <- function(originalMatrix = matrix()) {
## cachedInverse will store the inverse of originalMatrix
## When set to NULL, it indicates no inverse has been cached.
## As we haven't calculated anything yet, it's set to NULL by default
cachedInverse <- NULL
## The setMatrix function resets our enhanced matrix without requring external reassignment
setMatrix <- function(newMatrix) {
## change the value of our originalMatrix to that of newMatrix
originalMatrix <<- newMatrix
## as our originalMatrix has (probably) changed, dump our cached inverse
cachedInverse <<- NULL
}
## The setInverse function receives an inverse matrix to be cached
## inverse gets stored to the cachedInverse in the parent environment
## Note that this function is completely trusting that an outside function
## has correctly calculated the inverse, and isn't just handing it some random vector
setInverse <- function(inverse=matrix()) cachedInverse <<- inverse
## The getMatrix function simply returns the matrix contained in originalMatrix from the parent environment
getMatrix <- function() originalMatrix
## The getInverse function returns the cached inverse from the parent environment, even if it's NULL
getInverse <- function() cachedInverse
## The list below contains named references to each of the functions within makeCacheMatrix(),
## making them available externally via subsetting the list
list(setMatrix=setMatrix,
getMatrix=getMatrix,
setInverse=setInverse,
getInverse=getInverse)
}
## cacheSolve() takes an object created with makeCacheMatrix()
## and finds its cached inverse if one exists. Otherwise,
## it calcualtes the inverse and caches it
cacheSolve <- function(theMatrix, ...) {
## get the existing cahched matrix inverse value from theMatrix
cachedInverse <- theMatrix$getInverse()
## check if a valid inverse alread exists (NULL indicates no cached inverse)
if(!is.null(cachedInverse)) { ## cachedInverse isn't NULL, so a cached inverse exists
## let the world know!
message ("Retreiving Cached Matrix Inverse (SO EFFECIENT!)")
## return the cached inverse, breaking out of cacheSolve() now
return(cachedInverse)
}
## If we reached this point, it's because cachedInverse was NULL
## Therefore, we need to actually compute the inverse and cache it
## First, get the original matrix contained in theMatrix
original <- theMatrix$getMatrix()
## Next, calculate the inverse using solve()
inverse <- solve(original)
## Then, cache the calculated inverse
theMatrix$setInverse(inverse)
## Finally, return the calculated inverse
inverse
}
|
ad5ec1c9cf0c82087fcb6f3c2adb56f8445f2969 | b1c1e9d146157d14c142d24a9e02b95b3a31f584 | /Doutorado/Chapter-2/Ecological Exploration/Ecological exploration of blowdown.R | 9174e5412bfee3e0017254340d1a820700e2c1b1 | [] | no_license | Eduardoqm/Science-Repository | 1ef37904f290cbbea3c060c0a4cf37265f60b699 | d655a12fb833a9dd128672576c93cc6f9303f6ea | refs/heads/master | 2023-07-17T08:24:52.460738 | 2023-07-05T17:22:07 | 2023-07-05T17:22:07 | 200,397,253 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,582 | r | Ecological exploration of blowdown.R | ######################################
# Ecological exploration of blowdown #
# #
# Eduardo Q Marques 10-03-2022 #
######################################
library(tidyverse)
library(reshape2)
#First Part =================================================================================
#Merged field data
setwd("C:/Users/Eduardo Q Marques/Documents/Research/Doutorado/Banco de Dados Tanguro/Area1-plot/Campo vento")
df = read.csv("blowdown_full_update_2021.csv", sep = ",")
#Abundance of time series -------------------------------------------------------------------
df04 = df %>%
filter(dap.04 != "NA")
df04 = length(df04$tipo_de_dano)
#df06 = df %>%
# filter(dap.06 != "NA")
#df06 = length(df06$tipo_de_dano)
#df07 = df %>%
# filter(dap.07 != "NA")
#df07 = length(df07$tipo_de_dano)
df08 = df %>%
filter(dap.08 != "NA")
df08 = length(df08$tipo_de_dano)
df10 = df %>%
filter(dap.10 != "NA")
df10 = length(df10$tipo_de_dano)
df11 = df %>%
filter(dap.11 != "NA")
df11 = length(df11$tipo_de_dano)
df12 = df %>%
filter(dap.12 != "NA")
df12 = length(df12$tipo_de_dano)
df14 = df %>%
filter(dap.14 != "NA")
df14 = length(df14$tipo_de_dano)
df16 = df %>%
filter(dap.16 != "NA")
df16 = length(df16$tipo_de_dano)
df18 = df %>%
filter(dap.18 != "NA")
df18 = length(df18$tipo_de_dano)
abu <- data.frame(Abundance=c(df04, df08, df10, df11, df12, df14, df16, df18),
Year=c(2004, 2008, 2010, 2011, 2012, 2014, 2016, 2018))
ggplot(abu, aes(x = Year, y = Abundance))+
geom_col()
|
abfe18028f08f8c65c5b983d13a78d9623e39c87 | 38c16978738ffac95bfcf1e78fcb243fc4195305 | /man/create_q_vector_multi_kern.Rd | 905fe6d208df81ec92869fb0ce31cc459824fbd1 | [] | no_license | ebenmichael/balancer | ca3e2f733c52450d8e7b5b1a4ebd0d182713d4eb | 55173367e2c91f1a3ce47070f8430c6686a049bd | refs/heads/master | 2023-07-10T20:52:54.547666 | 2023-06-20T14:40:01 | 2023-06-20T14:40:01 | 129,783,286 | 7 | 3 | null | 2023-05-16T19:21:44 | 2018-04-16T17:47:11 | R | UTF-8 | R | false | true | 517 | rd | create_q_vector_multi_kern.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multilevel_kernel_QP.R
\name{create_q_vector_multi_kern}
\alias{create_q_vector_multi_kern}
\title{Create the q vector for an QP that solves min_x 0.5 * x'Px + q'x}
\usage{
create_q_vector_multi_kern(kern_mats, trtz)
}
\arguments{
\item{target}{Vector of population means to re-weight to}
\item{aux_dim}{Dimension of auxiliary weights}
}
\value{
q vector
}
\description{
Create the q vector for an QP that solves min_x 0.5 * x'Px + q'x
}
|
059ac8442c4b614c500a6ee82621821e6044c6ad | 8a313266928dc5e985050ebaeb1feb6fe43a2929 | /novo_data_preload.R | bc9adcebdd2c6f6da132e1ad515a1ca91317396f | [] | no_license | mt-christo/ej | b4a2c283285256f9fd3961b0f67dd89be20f890a | 265a9c171eb64e5e4e5ed7d2e2053908b1d946d4 | refs/heads/master | 2022-12-12T12:03:41.654039 | 2019-10-26T03:22:06 | 2019-10-26T03:22:06 | 102,653,282 | 0 | 1 | null | 2022-12-08T00:54:12 | 2017-09-06T20:07:26 | R | UTF-8 | R | false | false | 107 | r | novo_data_preload.R | # NOVO_UNI <<- load_uni('data-20190506', c('equity', 'equity_metrics', 'h_usd', 'libors'), list())
|
e361f1e29f7ff5916be8838da4be0f6a1e54e188 | d00f0bb26883c1f3073b530bb352764f4b3843f1 | /logistic_regression_practice.R | 5ff05683df09203cab8822f7280ef646b61e7e35 | [] | no_license | sandyqlin/R_Projects | 9e9ee1168b2c6e51aadd354e4db26a9a235bdc7d | cf452941ac1a5d31448d327255240bc6fb8dbc39 | refs/heads/master | 2020-05-20T06:04:30.382698 | 2015-08-31T23:55:04 | 2015-08-31T23:55:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 245 | r | logistic_regression_practice.R | x=rep(c(0,1,2),each=10)
x1=c(rep(0,20),rep(1,10))
#x1=as.factor(x1)
x2=c(rep(0,10),rep(1,10),rep(0,10))
#x2=as.factor(x2)
y=c(rep(1,2),rep(0,8),rep(1,4),rep(0,6),rep(1,6),rep(0,4))
#y=as.factor(y)
fit=glm(y~x1+x2,family=binomial())
summary(fit)
|
1861d0526b606ca2a7ddae6342bc810ccb3622ff | 9fc4d25ba7ddfc50c4a82dc75abc5af749b239cc | /man/diatobc.Rd | 2a096e8db8369b87104e04035149f05bfa44199b | [] | no_license | fkeck/diatobc | 123a804a07edf92f5ebcf8ebfa3f68ebc144750c | 5346927a5da772a183ff50f94897fdc21eef8569 | refs/heads/master | 2020-03-10T11:58:14.466485 | 2018-04-15T09:46:37 | 2018-04-15T09:46:37 | 129,366,839 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 209 | rd | diatobc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/diatobc-package.R
\docType{package}
\name{diatobc}
\alias{diatobc}
\alias{diatobc-package}
\title{diatobc}
\description{
diatobc
}
|
0a9d54c54f6537ce0fdb36311af67f1fc7ac4c76 | 7c4c32c31fa4caab7532a3c11cd9440d144cb8e4 | /Visual_rep.R | 68c06b96155d2efa688b17dd4f0ad0e2306a0731 | [] | no_license | joseconde1997/Business-analytics-with-R | 12edcf5d86d2bb903955d2b1bd5d65142e3c7ad1 | 814f3d712d51110f7e67283c0286a532ebb278e7 | refs/heads/master | 2021-03-03T15:53:14.009529 | 2020-03-09T07:47:49 | 2020-03-09T07:47:49 | 245,971,270 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 758 | r | Visual_rep.R | library(tidyverse)
library(knitr)
kable(smart %>% summarize(
n_brands=n_distinct(brand),
n_models=n_distinct(model)
,n_displays=n_distinct(display)
,n_os=n_distinct(os),booktabs=TRUE))
ggplot(smart,aes(ppi,price,label=model)) +
geom_text(aes(label=model),size=2.5,check_overlap = TRUE)
samsung <- smart %>% filter(brand=="Samsung")
ggplot(samsung,aes(ppi,price,label=model)) +
geom_text(aes(label=model),size=2.5, check_overlap = TRUE)
ggplot(smart) + geom_bar(aes(x=brand, fill=brand)) +
theme(legend.position = "none",axis.text.x = element_text(angle=90,hjust = 1,size = 8))
ggplot(filter(d,count>20)) + geom_bar(aes(x=brand, fill=brand)) +
theme(legend.position = "none", axis.text.x=element_text(angle = 90, hjust = 1, size = 11))
|
9dab3fbbb06ac69ddbe17b27553b85a7c528b713 | 7fdf101f8063e63697e4a174b104b68e57419315 | /Plot_1.R | 054d7d4b77a29be8d4deb4b9fb7af24be194c632 | [] | no_license | JoseLuis1966/ExData_Plotting1 | f6edd8b7bd93420c0350908dfae43e5fffb4d3ac | be743389e3d68e3b502f6a4c553a7f61f6e8cca3 | refs/heads/master | 2021-01-01T05:56:57.032605 | 2017-07-15T14:04:40 | 2017-07-15T14:04:40 | 97,315,301 | 0 | 0 | null | 2017-07-15T12:43:46 | 2017-07-15T12:43:46 | null | UTF-8 | R | false | false | 1,341 | r | Plot_1.R |
##load Packages
library(dplyr)
library(data.table)
library (lubridate)
## set workingdirectory
setwd("C:/Users/NACHO/Documents/cursoR/graficos/")
## read and clean data
consumo <- read.table("household_power_consumption.txt", header=TRUE, sep=";", na.strings = "?", colClasses = c('character','character','numeric','numeric','numeric','numeric','numeric','numeric','numeric'))
## Format date to Type dd/mm/yy
consumo$Date <- as.Date(consumo$Date, "%d/%m/%Y")
## Filter dataset from dates 2007-02-01 and 2007-02-02
consumo <- subset(consumo,Date >= as.Date("2007-2-1") & Date <= as.Date("2007-2-2"))
## Erase incomplete observation
consumo<- consumo[complete.cases(consumo),]
## Combine Date and Time column
dateTime <- paste(consumo$Date, consumo$Time)
## Name the vector
dateTime <- setNames(dateTime, "DateTime")
## EraseDate and Time column from consumo
consumo <- consumo[ ,!(names(consumo) %in% c("Date","Time"))]
## Add DateTime column to Consumo
consumo <- cbind(dateTime, consumo)
## Format dateTime Column
consumo$dateTime <- as.POSIXct(dateTime)
## Create the histogram plot1 "Global Active Power"
hist(consumo$Global_active_power, main="Global Active Power", xlab = "Global Active Power (kilowatts)", col="red")
## Save file and close device
dev.copy(png,"plot_1.png", width=480, height=480)
dev.off()
|
fde8cf6b456d794158123e60506797475afa24cc | 2d277476733ba48dee4bec8bacc6c8dfbb86717b | /tests/testthat/test-BuyseTest-checkValues.R | e0f658196e56caedec03e86671fea6b85d5f6b27 | [] | no_license | cran/BuyseTest | ef75b3c7f93a476b35786e485ae4ab2e56c8d90f | a3dfe49778c8d5e2f0b987dd2e9cfbd6f01cb479 | refs/heads/master | 2023-04-14T00:41:38.778354 | 2023-03-20T21:30:02 | 2023-03-20T21:30:02 | 135,258,510 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 48,633 | r | test-BuyseTest-checkValues.R | if(FALSE){
library(mvtnorm)
library(testthat)
library(BuyseTest)
library(data.table)
}
context("Check BuyseTest without strata")
## * Settings
n.patients <- c(90,100)
BuyseTest.options(check = TRUE,
keep.pairScore = TRUE,
method.inference = "none",
trace = 0)
## * Simulated data
set.seed(10)
dt.sim <- simBuyseTest(n.T = n.patients[1],
n.C = n.patients[2],
argsBin = list(p.T = list(c(0.5,0.5),c(0.25,0.75))),
argsCont = list(mu.T = 1:3, sigma.T = rep(1,3)),
argsTTE = list(scale.T = 1:3, scale.censoring.T = rep(1,3)))
## butils::object2script(dt.sim)
dt.sim$status1.noC <- 1
dtS.sim <- rbind(cbind(dt.sim, strata = 1),
cbind(dt.sim, strata = 2),
cbind(dt.sim, strata = 3))
## * Binary endpoint
## ** No strata
test_that("BuyseTest - binary (no strata)", {
BT.bin <- BuyseTest(treatment ~ bin(toxicity1),
data = dt.sim)
BT2 <- BuyseTest(data = dt.sim,
endpoint = "toxicity1",
treatment = "treatment",
type = "bin")
## *** test against fixed value
test <- list(favorable = as.double(coef(BT.bin, statistic = "count.favorable", cumulative = FALSE)),
unfavorable = as.double(coef(BT.bin, statistic = "count.unfavorable", cumulative = FALSE)),
neutral = as.double(coef(BT.bin, statistic = "count.neutral", cumulative = FALSE)),
uninf = as.double(coef(BT.bin, statistic = "count.uninf", cumulative = FALSE)),
favorable = as.double(coef(BT.bin, statistic = "favorable", cumulative = TRUE)),
unfavorable = as.double(coef(BT.bin, statistic = "unfavorable", cumulative = TRUE)),
netChange = as.double(coef(BT.bin, statistic = "netBenefit", cumulative = TRUE)),
winRatio = as.double(coef(BT.bin, statistic = "winRatio", cumulative = TRUE))
)
GS <- list(favorable = c(1968) ,
unfavorable = c(2478) ,
neutral = c(4554) ,
uninf = c(0) ,
favorable = c(0.21866667) ,
unfavorable = c(0.27533333) ,
netChange = c(-0.05666667) ,
winRatio = c(0.79418886) )
## butils::object2script(test, digit = 6)
expect_equal(test, GS, tol = 1e-6, scale = 1)
BT.bin@call <- list()
BT2@call <- list()
expect_equal(BT.bin,BT2)
## *** count pairs
tableS <- summary(BT.bin, print = FALSE, percentage = FALSE)$table
dt.tableS <- as.data.table(tableS)[strata == "global"]
expect_equal(dt.tableS[,total],
unname(dt.tableS[,favorable + unfavorable + neutral + uninf])
)
})
## ** Strata
test_that("BuyseTest - binary (strata)", {
BT.bin <- BuyseTest(treatment ~ bin(toxicity1) + strata,
data = dtS.sim)
tableS <- summary(BT.bin, print = FALSE, percentage = FALSE)$table
dt.tableS <- as.data.table(tableS)
## *** count pairs
expect_equal(dt.tableS[,total],
unname(dt.tableS[,favorable + unfavorable + neutral + uninf]
))
expect_equal(dt.tableS[,total], c(27000,9000,9000,9000))
expect_equal(dt.tableS[,favorable], c(5904, 1968, 1968, 1968))
expect_equal(dt.tableS[,unfavorable], c(7434, 2478, 2478, 2478))
expect_equal(dt.tableS[,neutral], c(13662, 4554, 4554, 4554))
expect_equal(dt.tableS[,uninf], c(0, 0, 0, 0))
## *** test summary statistic
expect_equal(dt.tableS[,delta], c(-0.05666667, -0.05666667, -0.05666667, -0.05666667), tol = 1e-6)
expect_equal(dt.tableS[,Delta], c(-0.05666667, NA, NA, NA), tol = 1e-6)
})
## * Continuous endpoint
## ** No strata
test_that("BuyseTest - continuous (no strata)", {
BT.cont <- BuyseTest(treatment ~ cont(score1, 1) + cont(score2, 0),
data = dt.sim)
BT2 <- BuyseTest(data = dt.sim,
endpoint = c("score1","score2"),
treatment = "treatment",
type = c("cont","cont"),
threshold = c(1,0)
)
## *** test against fixed value
test <- list(favorable = as.double(coef(BT.cont, statistic = "count.favorable", cumulative = FALSE)),
unfavorable = as.double(coef(BT.cont, statistic = "count.unfavorable", cumulative = FALSE)),
neutral = as.double(coef(BT.cont, statistic = "count.neutral", cumulative = FALSE)),
uninf = as.double(coef(BT.cont, statistic = "count.uninf", cumulative = FALSE)),
favorable = as.double(coef(BT.cont, statistic = "favorable", cumulative = TRUE)),
unfavorable = as.double(coef(BT.cont, statistic = "unfavorable", cumulative = TRUE)),
netChange = as.double(coef(BT.cont, statistic = "netBenefit", cumulative = TRUE)),
winRatio = as.double(coef(BT.cont, statistic = "winRatio", cumulative = TRUE))
)
GS <- list(favorable = c(2196, 2142) ,
unfavorable = c(2501, 2161) ,
neutral = c(4303, 0) ,
uninf = c(0, 0) ,
favorable = c(0.244, 0.482) ,
unfavorable = c(0.27788889, 0.518) ,
netChange = c(-0.03388889, -0.036) ,
winRatio = c(0.87804878, 0.93050193) )
## butils::object2script(test, digit = 6)
BT.cont@call <- list()
BT2@call <- list()
expect_equal(test, GS, tol = 1e-6, scale = 1)
expect_equal(BT.cont,BT2)
## *** count pairs
tableS <- summary(BT.cont, print = FALSE, percentage = FALSE)$table
dt.tableS <- as.data.table(tableS)[strata == "global"]
expect_equal(dt.tableS[,total],
unname(dt.tableS[, favorable + unfavorable + neutral + uninf]
))
})
## ** Strata
test_that("BuyseTest - continuous (strata)", {
BT.cont <- BuyseTest(treatment ~ cont(score1, 1) + cont(score2, 0) + strata,
data = dtS.sim)
tableS <- summary(BT.cont, print = FALSE, percentage = FALSE)$table
dt.tableS <- as.data.table(tableS)
## *** count pairs
expect_equal(dt.tableS[,total],
unname(dt.tableS[,favorable + unfavorable + neutral + uninf]
))
expect_equal(dt.tableS[,total], c(27000, 9000, 9000, 9000, 12909, 4303, 4303, 4303))
expect_equal(dt.tableS[,favorable], c(6588, 2196, 2196, 2196, 6426, 2142, 2142, 2142))
expect_equal(dt.tableS[,unfavorable], c(7503, 2501, 2501, 2501, 6483, 2161, 2161, 2161))
expect_equal(dt.tableS[,neutral], c(12909, 4303, 4303, 4303, 0, 0, 0, 0))
expect_equal(dt.tableS[,uninf], c(0, 0, 0, 0, 0, 0, 0, 0))
## *** test summary statistic
expect_equal(dt.tableS[,delta], c(-0.03388889, -0.03388889, -0.03388889, -0.03388889, -0.00211111, -0.00211111, -0.00211111, -0.00211111), tol = 1e-6)
expect_equal(dt.tableS[,Delta], c(-0.03388889, NA, NA, NA, -0.036, NA, NA, NA), tol = 1e-6)
})
## * Time to event endpoint
## ** No strata - same endpoint
for(method in c("Gehan","Peron")){ ## method <- "Gehan" ## method <- "Peron"
test_that(paste0("BuyseTest - tte (same, ",method,", no strata)"),{
BT.tte <- BuyseTest(treatment ~ tte(eventtime1, status1, threshold = 1) + tte(eventtime1, status1, threshold = 0.5) + tte(eventtime1, status1, threshold = 0.25),
data = dt.sim,
scoring.rule = method,
correction.uninf = FALSE
)
BT.1tte <- BuyseTest(treatment ~ tte(eventtime1, status1, threshold = 0.25),
data = dt.sim,
scoring.rule = method,
correction.uninf = FALSE
)
BT2 <- BuyseTest(data = dt.sim,
endpoint = c("eventtime1","eventtime1","eventtime1"),
status = c("status1","status1","status1"),
treatment = "treatment",
type = c("tte","tte","tte"),
threshold = c(1,0.5,0.25),
scoring.rule = method,
correction.uninf = FALSE
)
## *** compatibility between BuyseTests
BT.tte@call <- list()
BT2@call <- list()
expect_equal(BT.tte, BT2)
expect_equal(sum(coef(BT.tte, statistic = "count.favorable", cumulative = FALSE)),
as.double(coef(BT.1tte, statistic = "count.favorable", cumulative = FALSE)))
expect_equal(sum(coef(BT.tte, statistic = "count.unfavorable", cumulative = FALSE)),
as.double(coef(BT.1tte, statistic = "count.unfavorable", cumulative = FALSE)))
expect_equal(coef(BT.tte, statistic = "count.neutral", cumulative = FALSE)[3],
coef(BT.1tte, statistic = "count.neutral", cumulative = FALSE))
expect_equal(coef(BT.tte, statistic = "count.uninf", cumulative = FALSE)[3],
coef(BT.1tte, statistic = "count.uninf", cumulative = FALSE))
expect_equal(coef(BT.tte, statistic = "netBenefit", cumulative = TRUE)[3],
coef(BT.1tte, statistic = "netBenefit", cumulative = TRUE))
expect_equal(coef(BT.tte, statistic = "winRatio", cumulative = TRUE)[3],
coef(BT.1tte, statistic = "winRatio", cumulative = TRUE))
## *** test against fixed value
test <- list(favorable = as.double(coef(BT.tte, statistic = "count.favorable", cumulative = FALSE)),
unfavorable = as.double(coef(BT.tte, statistic = "count.unfavorable", cumulative = FALSE)),
neutral = as.double(coef(BT.tte, statistic = "count.neutral", cumulative = FALSE)),
uninf = as.double(coef(BT.tte, statistic = "count.uninf", cumulative = FALSE)),
favorable = as.double(coef(BT.tte, statistic = "favorable", cumulative = TRUE)),
unfavorable = as.double(coef(BT.tte, statistic = "unfavorable", cumulative = TRUE)),
netChange = as.double(coef(BT.tte, statistic = "netBenefit", cumulative = TRUE)),
winRatio = as.double(coef(BT.tte, statistic = "winRatio", cumulative = TRUE))
)
if(method == "Gehan"){
GS <- list(favorable = c(438, 719, 543) ,
unfavorable = c(325, 582, 500) ,
neutral = c(2284, 1569, 1084) ,
uninf = c(5953, 5367, 4809) ,
favorable = c(0.04866667, 0.12855556, 0.18888889) ,
unfavorable = c(0.03611111, 0.10077778, 0.15633333) ,
netChange = c(0.01255556, 0.02777778, 0.03255556) ,
winRatio = c(1.34769231, 1.27563396, 1.20824449) )
## butils::object2script(test, digit = 8)
}else if(method == "Peron"){
GS <- list(favorable = c(1289.0425448, 1452.9970531, 682.33602169) ,
unfavorable = c(2044.84933459, 908.62963327, 578.82862552) ,
neutral = c(5666.10812061, 3304.48143424, 2043.31678703) ,
uninf = c(0, 0, 0) ,
favorable = c(0.14322695, 0.30467107, 0.38048618) ,
unfavorable = c(0.22720548, 0.32816433, 0.39247862) ,
netChange = c(-0.08397853, -0.02349326, -0.01199244) ,
winRatio = c(0.6303851, 0.92841006, 0.96944434) )
}
expect_equal(test, GS, tolerance = 1e-6, scale = 1)
## *** count pairs
tableS <- summary(BT.tte, print = FALSE, percentage = FALSE)$table
dt.tableS <- as.data.table(tableS)[strata == "global"]
expect_equal(dt.tableS[,total],
unname(dt.tableS[,favorable + unfavorable + neutral + uninf]),
tolerance = 1e-1, scale = 1) ## inexact for Peron
})
}
## ** No strata - different endpoints
for(method in c("Gehan","Peron")){ ## method <- "Gehan" ## method <- "Peron"
test_that(paste0("BuyseTest - tte (different, ",method,", no strata)"),{
BT.tte <- BuyseTest(treatment ~ tte(eventtime1, status1, threshold = 1) + tte(eventtime2, status2, threshold = 0.5) + tte(eventtime3, status3, threshold = 0.25),
data = dt.sim, scoring.rule = method,
correction.uninf = FALSE)
BT2 <- BuyseTest(data = dt.sim,
endpoint = c("eventtime1","eventtime2","eventtime3"),
status = c("status1","status2","status3"),
treatment = "treatment",
type = c("tte","tte","tte"),
threshold = c(1,0.5,0.25),
scoring.rule = method,
correction.uninf = FALSE
)
test <- list(favorable = as.double(coef(BT.tte, statistic = "count.favorable", cumulative = FALSE)),
unfavorable = as.double(coef(BT.tte, statistic = "count.unfavorable", cumulative = FALSE)),
neutral = as.double(coef(BT.tte, statistic = "count.neutral", cumulative = FALSE)),
uninf = as.double(coef(BT.tte, statistic = "count.uninf", cumulative = FALSE)),
favorable = as.double(coef(BT.tte, statistic = "favorable", cumulative = TRUE)),
unfavorable = as.double(coef(BT.tte, statistic = "unfavorable", cumulative = TRUE)),
netChange = as.double(coef(BT.tte, statistic = "netBenefit", cumulative = TRUE)),
winRatio = as.double(coef(BT.tte, statistic = "winRatio", cumulative = TRUE))
)
## *** compatibility between BuyseTests
BT.tte@call <- list()
BT2@call <- list()
expect_equal(BT.tte, BT2)
## *** test against fixed value
if(method == "Gehan"){
GS <- list(favorable = c(438, 620, 794) ,
unfavorable = c(325, 561, 361) ,
neutral = c(2284, 339, 73) ,
uninf = c(5953, 6717, 5828) ,
favorable = c(0.04866667, 0.11755556, 0.20577778) ,
unfavorable = c(0.03611111, 0.09844444, 0.13855556) ,
netChange = c(0.01255556, 0.01911111, 0.06722222) ,
winRatio = c(1.34769231, 1.19413093, 1.48516439) )
## butils::object2script(test, digit = 8)
}else if(method == "Peron"){
GS <- list(favorable = c(1289.0425448, 2318.38791489, 1231.91554493) ,
unfavorable = c(2044.84933459, 1529.8258322, 491.18260522) ,
neutral = c(5666.10812061, 867.93018367, 94.79622337) ,
uninf = c(0, 949.96418985, 0) ,
favorable = c(0.14322695, 0.40082561, 0.53770511) ,
unfavorable = c(0.22720548, 0.39718613, 0.45176197) ,
netChange = c(-0.08397853, 0.00363948, 0.08594314) ,
winRatio = c(0.6303851, 1.00916315, 1.19023986) )
}
## *** count pairs
tableS <- summary(BT.tte, print = FALSE, percentage = FALSE)$table
dt.tableS <- as.data.table(tableS)[strata == "global"]
expect_equal(dt.tableS[,total],
unname(dt.tableS[,favorable + unfavorable + neutral + uninf]),
tolerance = 1e-1, scale = 1) ## inexact for Peron
})
}
## ** Strata - same endpoint
for(method in c("Gehan","Peron")){ ## method <- "Peron" ## method <- "Gehan"
test_that(paste0("BuyseTest - tte (same, ",method,", strata)"),{
BT.tte <- BuyseTest(treatment ~ tte(eventtime1, status1, threshold = 1) + tte(eventtime1, status1, threshold = 0.5) + tte(eventtime1, status1, threshold = 0.25) + strata,
data = dtS.sim, scoring.rule = method)
## *** test against fixed value
test <- list(favorable = as.double(coef(BT.tte, statistic = "count.favorable", stratified = TRUE, cumulative = FALSE)),
unfavorable = as.double(coef(BT.tte, statistic = "count.unfavorable", stratified = TRUE, cumulative = FALSE)),
neutral = as.double(coef(BT.tte, statistic = "count.neutral", stratified = TRUE, cumulative = FALSE)),
uninf = as.double(coef(BT.tte, statistic = "count.uninf", stratified = TRUE, cumulative = FALSE)),
favorable = as.double(coef(BT.tte, statistic = "favorable", stratified = FALSE, cumulative = TRUE)),
unfavorable = as.double(coef(BT.tte, statistic = "unfavorable", stratified = FALSE, cumulative = TRUE)),
netChange = as.double(coef(BT.tte, statistic = "netBenefit", stratified = FALSE, cumulative = TRUE)),
winRatio = as.double(coef(BT.tte, statistic = "winRatio", stratified = FALSE, cumulative = TRUE))
)
if(method == "Gehan"){
GS <- list(favorable = c(438, 438, 438, 719, 719, 719, 543, 543, 543) ,
unfavorable = c(325, 325, 325, 582, 582, 582, 500, 500, 500) ,
neutral = c(2284, 2284, 2284, 1569, 1569, 1569, 1084, 1084, 1084) ,
uninf = c(5953, 5953, 5953, 5367, 5367, 5367, 4809, 4809, 4809) ,
favorable = c(0.04867, 0.12856, 0.18889) ,
unfavorable = c(0.03611, 0.10078, 0.15633) ,
netChange = c(0.01256, 0.02778, 0.03256) ,
winRatio = c(1.34769, 1.27563, 1.20824) )
} else if(method == "Peron"){
GS <- list(favorable = c(1289.04254, 1289.04254, 1289.04254, 1452.99705, 1452.99705, 1452.99705, 682.33602, 682.33602, 682.33602) ,
unfavorable = c(2044.84933, 2044.84933, 2044.84933, 908.62963, 908.62963, 908.62963, 578.82863, 578.82863, 578.82863) ,
neutral = c(5666.10812, 5666.10812, 5666.10812, 3304.48143, 3304.48143, 3304.48143, 2043.31679, 2043.31679, 2043.31679) ,
uninf = c(0, 0, 0, 0, 0, 0, 0, 0, 0) ,
favorable = c(0.14323, 0.30467, 0.38049) ,
unfavorable = c(0.22721, 0.32816, 0.39248) ,
netChange = c(-0.08398, -0.02349, -0.01199) ,
winRatio = c(0.63039, 0.92841, 0.96944) )
## butils::object2script(test, digit = 5)
}
expect_equal(GS, test, tol = 1e-4, scale = 1)
## *** same result for each pair
tableS <- summary(BT.tte, print = FALSE, percentage = FALSE)$table
expect_equal(tableS[tableS$strata=="1","Delta"],tableS[tableS$strata=="2","Delta"])
expect_equal(tableS[tableS$strata=="1","Delta"],tableS[tableS$strata=="3","Delta"])
expect_equal(tableS[tableS$strata=="1","Delta"],tableS[tableS$strata=="3","Delta"])
## *** count pairs
dt.tableS <- as.data.table(tableS)[strata == "global"]
expect_equal(dt.tableS[,total],
unname(dt.tableS[,favorable + unfavorable + neutral + uninf]),
tolerance = 1e-1, scale = 1) ## inexact for Peron
})
}
## * Mixed endpoints
for(method in c("Gehan","Peron")){ ## method <- "Peron" ## method <- "Gehan"
test_that(paste0("BuyseTest - mixed (",method,", no strata)"),{
BT.mixed <- BuyseTest(treatment ~ tte(eventtime1, status1, threshold = 0.5) + cont(score1, 1) + bin(toxicity1) + tte(eventtime1, status1, threshold = 0.25) + cont(score1, 0.5),
data = dt.sim, scoring.rule = method)
BT2 <- BuyseTest(data=dt.sim,
endpoint=c("eventtime1","score1","toxicity1","eventtime1","score1"),
status=c("status1","..NA..","..NA..","status1","..NA.."),
treatment="treatment",
type=c("timeToEvent","continuous","binary","timeToEvent","continuous"),
threshold=c(0.5,1,NA,0.25,0.5),
scoring.rule=method)
## *** compatibility between BuyseTests
BT.mixed@call <- list()
BT2@call <- list()
expect_equal(BT.mixed, BT2)
## *** test against fixed value
test <- list(favorable = as.double(coef(BT.mixed, statistic = "count.favorable", cumulative = FALSE)),
unfavorable = as.double(coef(BT.mixed, statistic = "count.unfavorable", cumulative = FALSE)),
neutral = as.double(coef(BT.mixed, statistic = "count.neutral", cumulative = FALSE)),
uninf = as.double(coef(BT.mixed, statistic = "count.uninf", cumulative = FALSE)),
favorable = as.double(coef(BT.mixed, statistic = "favorable", cumulative = TRUE)),
unfavorable = as.double(coef(BT.mixed, statistic = "unfavorable", cumulative = TRUE)),
netChange = as.double(coef(BT.mixed, statistic = "netBenefit", cumulative = TRUE)),
winRatio = as.double(coef(BT.mixed, statistic = "winRatio", cumulative = TRUE))
)
if(method == "Gehan"){
GS <- list(favorable = c(1157, 1753, 751, 134, 373) ,
unfavorable = c(907, 1806, 949, 129, 323) ,
neutral = c(1569, 3377, 1677, 277, 718) ,
uninf = c(5367, 0, 0, 1137, 0) ,
favorable = c(0.12855556, 0.32333333, 0.40677778, 0.42166667, 0.46311111) ,
unfavorable = c(0.10077778, 0.30144444, 0.40688889, 0.42122222, 0.45711111) ,
netChange = c(0.02777778, 0.02188889, -0.00011111, 0.00044444, 0.006) ,
winRatio = c(1.27563396, 1.07261334, 0.99972693, 1.00105513, 1.01312591) )
## butils::object2script(test, digit = 8)
}else if(method == "Peron"){
GS <- list(favorable = c(2742.0395979, 792.80301972, 403.03891763, 160.70305305, 134.38721963) ,
unfavorable = c(2953.47896786, 896.93725328, 407.50415506, 142.85049401, 122.54879121) ,
neutral = c(3304.48143424, 1614.74116124, 804.19808854, 500.64454148, 243.70853064) ,
uninf = c(0, 0, 0, 0, 0) ,
favorable = c(0.30467107, 0.39276029, 0.43754239, 0.45539829, 0.4703302) ,
unfavorable = c(0.32816433, 0.42782402, 0.47310226, 0.48897454, 0.50259107) ,
netChange = c(-0.02349326, -0.03506373, -0.03555987, -0.03357625, -0.03226087) ,
winRatio = c(0.92841006, 0.91804169, 0.92483682, 0.93133333, 0.93581089) )
}
expect_equal(test, GS, tolerance = 1e-6, scale = 1)
## *** count pairs
tableS <- summary(BT.mixed, print = FALSE, percentage = FALSE)$table
dt.tableS <- as.data.table(tableS)[strata == "global"]
expect_equal(dt.tableS[,total],
unname(dt.tableS[,favorable + unfavorable + neutral + uninf])
)
})
}
test_that("ordering does not matter", {
BT.mixed1 <- BuyseTest(treatment ~ tte(eventtime1, status1, threshold = 0.25) + cont(score1, 1),
data = dt.sim, scoring.rule = method)
BT.mixed2 <- BuyseTest(treatment ~ tte(eventtime1, status1, threshold = 0.5) + tte(eventtime1, status1, threshold = 0.25) + cont(score1, 1),
data = dt.sim, scoring.rule = method)
expect_equal(coef(BT.mixed2, statistic = "netBenefit")[2:3], coef(BT.mixed1, statistic = "netBenefit"), tol = 1e-6)
expect_equal(coef(BT.mixed2, statistic = "winRatio")[2:3], coef(BT.mixed1, statistic = "winRatio"), tol = 1e-6)
})
test_that(paste0("BuyseTest - Peron scoring rule with 2 TTE, one without censoring"),{
## 1 continuous
## 2 Gehan left-censoring
## 3 Gehan right-censoring
## 4 Peron right-censoring survival
## 5 Peron right-censoring competing risks
BT.mixed <- BuyseTest(treatment ~ tte(eventtime2, status2, threshold = 0.5) + tte(eventtime1, status1.noC, threshold = 0),
data = dt.sim, scoring.rule = "Peron")
expect_equal(unname(attr(BT.mixed@scoring.rule,"method.score")), c("SurvPeron","continuous"))
## summary(BT.mixed)
BT.mixed <- BuyseTest(treatment ~ tte(eventtime1, status1.noC, threshold = 0) + tte(eventtime2, status2, threshold = 0.5),
data = dt.sim, scoring.rule = "Peron")
## summary(BT.mixed)
expect_equal(unname(attr(BT.mixed@scoring.rule,"method.score")), c("continuous","SurvPeron"))
})
## * Left censoring
test_that("BuyseTest - left vs. right censoring", {
BT.left <- BuyseTest(treatment ~ tte(eventtime1, status = status1, censoring = "left"),
data = dt.sim,
scoring.rule = "Gehan")
expect_equal(as.double(coef(BT.left)), 0.09488889, tol = 1e-6)
BT.left <- BuyseTest(treatment ~ tte(eventtime1, status = status1, censoring = "left"),
data = dt.sim,
scoring.rule = "Gehan",
correction.uninf = TRUE)
expect_equal(as.double(coef(BT.left)), 0.1768116, tol = 1e-6)
})
## * Gaussian endpoint
## ** uncorrelated
df.1 <- data.frame(mean = 0:1, sd = 1, treatment = c("C","T"))
df.2 <- data.frame(mean = 0:1, sd = c(2,0.5), treatment = c("C","T"))
df.3 <- rbind(df.1,df.2)
test_that("BuyseTest - uncorrelated gaussians", {
GS.1 <- 1 - pnorm(0, mean = 1, sd = sqrt(2))
## GS.1 - mean( rnorm(n.GS, mean = 1)> rnorm(n.GS, mean = 0))
BTG.1 <- BuyseTest(treatment ~ gaus(mean, sd),
data = df.1, method.inference = "none")
expect_equal(GS.1,as.double(coef(BTG.1, statistic = "favorable")),tol=1e-6)
GS.2 <- 1 - pnorm(0, mean = 1, sd = sqrt(4.25))
## GS.2 - mean( rnorm(n.GS, mean = 1, sd = 0.5)> rnorm(n.GS, mean = 0, sd = 2))
BTG.2 <- BuyseTest(treatment ~ gaus(mean = mean, std = sd),
data = df.2, method.inference = "none")
expect_equal(GS.2,as.double(coef(BTG.2, statistic = "favorable")),tol=1e-6)
GS.3 <- mean(c(GS.1, (1 - pnorm(0, mean = 1, sd = sqrt(5))), 1 - pnorm(0, mean = 1, sd = sqrt(1.25)), GS.2))
## GS.3 - mean(c(GS.1,mean( rnorm(n.GS, mean = 1)> rnorm(n.GS, mean = 0, sd = 2)), mean( rnorm(n.GS, mean = 1, sd = 0.5)> rnorm(n.GS, mean = 0)),GS.2))
BTG.3 <- BuyseTest(treatment ~ gaus(mean = mean, std = sd),
data = df.3, method.inference = "none")
expect_equal(GS.3,as.double(coef(BTG.3, statistic = "favorable")),tol=1e-6)
})
## ** correlated
complement <- function(rho, n) {## generate a dataset with given correlation
## adapted from
## https://stats.stackexchange.com/questions/15011/generate-a-random-variable-with-a-defined-correlation-to-an-existing-variables
x <- rnorm(n)
y <- rnorm(n)
y.perp <- residuals(lm(x ~ y))
z <- rho * sd(y.perp) * y + sqrt(1 - rho^2) * sd(y) * y.perp
return(list(Y=as.double(y),X=as.double(z)))
}
## cor(complement(rho = 0.5, n = 10))
df.1$iid <- complement(rho = 0.5, n = 10)
df.2$iid <- complement(rho = 0.5, n = 10)
df.3 <- rbind(df.1,df.2)
test_that("BuyseTest - correlated gaussians", {
GS.1 <- 1 - pnorm(0, mean = 1, sd = sqrt(1))
## GS.1 - mean(apply(mvtnorm::rmvnorm(n.GS, mean = 0:1, sigma = matrix(c(1,0.5,0.5,1),2,2)),1, FUN = function(x){x[2]>x[1]}))
BTG.1 <- BuyseTest(treatment ~ gaus(mean, sd, iid),
data = df.1, method.inference = "none")
expect_equal(GS.1,as.double(coef(BTG.1, statistic = "favorable")),tol=1e-6)
GS.2 <- 1 - pnorm(0, mean = 1, sd = sqrt(3.25)) ## 2^2+0.5^2-2*0.5*0.5*2
## GS.2 - mean(apply(mvtnorm::rmvnorm(10*n.GS, mean = 0:1, sigma = matrix(c(0.5^2,0.5,0.5,2^2),2,2)),1, FUN = function(x){x[2]>x[1]}))
BTG.2 <- BuyseTest(treatment ~ gaus(mean = mean, std = sd, iid),
data = df.2, method.inference = "none")
expect_equal(GS.2,as.double(coef(BTG.2, statistic = "favorable")),tol=1e-6)
GS.3 <- mean(c(GS.1,
1 - pnorm(0, mean = 1, sd = sqrt(1.25-cor(df.1$iid[[1]],df.2$iid[[2]]))),
1 - pnorm(0, mean = 1, sd = sqrt(5-4*cor(df.1$iid[[2]],df.2$iid[[1]]))),
GS.2))
## GS.3 - c(GS.1,mean( rnorm(n.GS, mean = 1)> rnorm(n.GS, mean = 0, sd = 2)), mean( rnorm(n.GS, mean = 1, sd = 0.5)> rnorm(n.GS, mean = 0)),GS.2)
BTG.3 <- BuyseTest(treatment ~ gaus(mean = mean, std = sd, iid = iid),
data = df.3, method.inference = "none")
expect_equal(GS.3,as.double(coef(BTG.3, statistic = "favorable")),tol=1e-6)
})
## * dataset [save]
## dt.sim <- data.table("treatment" = c("C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T"),
## "eventtime1" = c(0.29972933, 0.17098301, 0.03202016, 0.44202235, 0.10311930, 1.15511106, 0.56124439, 1.21925282, 0.17187895, 0.29113518, 0.09773182, 0.67653288, 0.03861652, 0.07063795, 0.01458732, 0.71823701, 1.08732315, 0.15199073, 0.60176965, 0.41266664, 0.70669922, 0.43573838, 0.07363507, 0.96531658, 0.05755952, 0.94544071, 3.92245778, 0.26717898, 0.16922697, 0.73171108, 1.56385321, 0.00937406, 0.16954569, 0.09197029, 0.09036225, 0.18974451, 0.04073623, 0.02378406, 0.56634940, 1.61507125, 0.49139404, 0.19115956, 0.13740882, 0.36936734, 0.36919469, 1.41367871, 0.94269045, 0.01191205, 0.37297697, 0.19502322, 0.01296422, 0.31343847, 0.00213360, 0.28171661, 0.17320335, 0.06992269, 0.03277328, 0.21255628, 1.43421433, 0.50712777, 0.24571909, 1.00813698, 0.51160661, 0.09173387, 0.28063656, 1.14177394, 0.12593593, 1.58859472, 0.07077964, 0.11041468, 0.09741926, 0.56342077, 0.23108781, 0.76598116, 0.02193362, 0.14312356, 1.36059222, 0.85553186, 0.38761972, 0.05592164, 0.24080708, 2.23146741, 0.65659820, 0.12662146, 0.33644115, 0.93738422, 0.93216642, 0.80139621, 0.65390255, 0.60241389, 0.34299720, 0.66186296, 1.10529116, 0.14865979, 0.12501623, 0.04451988, 0.48423927, 0.92904199, 0.32060823, 0.20941169, 0.29373301, 0.99816128, 1.33980963, 0.16543365, 1.22099704, 0.03737215, 0.16298912, 0.32335369, 0.39027702, 0.10348081, 1.03796020, 0.47513692, 0.24106903, 0.45926525, 0.49608224, 1.44827529, 0.52186516, 0.68353467, 0.01981440, 0.18416592, 0.97426659, 1.77382739, 0.33398520, 0.19615994, 0.03780470, 0.17649501, 0.22901002, 0.02323039, 0.20845366, 0.52986278, 0.74053528, 0.27117162, 0.19489030, 0.66019467, 0.88323068, 0.32137872, 0.17473734, 0.10029520, 0.08490402, 0.34625873, 1.92253508, 1.24734174, 0.20656446, 1.47517308, 0.00019922, 0.33644227, 0.26031585, 0.24064544, 0.87270237, 0.50428180, 0.55966656, 1.09623527, 0.00653237, 0.51073806, 0.36994855, 0.74641533, 0.44120419, 0.98441248, 0.27874909, 0.29785716, 0.19272977, 0.03585685, 0.07027667, 0.00518237, 0.13138687, 0.03262746, 0.26673138, 0.22325116, 0.71796943, 0.29428682, 0.74450076, 0.29965883, 0.17469397, 1.73676014, 1.38930578, 1.61992553, 0.73321636, 0.79600567, 0.04142438, 0.94565307, 0.00825042, 0.65877918, 0.76745178, 1.11121647, 1.58163545, 0.10784914, 0.94274529, 0.05602611, 0.59380396, 1.25969953),
## "status1" = c(1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1),
## "eventtime2" = c(0.13556029, 0.53306031, 0.75447141, 0.25613517, 2.07382035, 0.03757998, 0.14045294, 1.43718090, 0.25834648, 0.00990976, 0.09679214, 0.10656134, 0.12146487, 1.65973920, 0.36003623, 0.27730941, 0.38657147, 0.24456645, 1.45490109, 1.36873057, 0.59120666, 0.13334589, 0.01156500, 0.53612156, 0.50263578, 0.83841401, 1.13769949, 0.32597204, 2.28409336, 0.24155223, 0.14974690, 0.44926621, 0.04444326, 1.74047606, 0.14653971, 0.05550446, 0.91919142, 0.19709401, 0.13809616, 0.19533969, 2.16819532, 1.63080095, 0.63950588, 0.39308933, 0.93342527, 1.44372187, 0.07228017, 1.65850544, 1.65081603, 1.59301263, 0.56652696, 1.18547005, 0.17771856, 0.88895104, 0.55326678, 0.53893584, 0.06138524, 0.41325058, 0.50743982, 0.56196957, 0.05072848, 0.78399042, 0.14126094, 0.37339708, 1.71804695, 0.61959578, 0.37048513, 0.19876601, 1.13166471, 0.16526419, 1.00895604, 0.27660263, 0.15692162, 0.56680821, 1.02953170, 0.15395316, 0.18412961, 0.35121113, 1.71637364, 0.37027203, 0.05331582, 0.41455140, 0.40164440, 0.40714141, 1.60638089, 0.42633103, 0.21886920, 0.12911882, 0.21075684, 0.41380614, 0.13020199, 0.83162531, 0.33213999, 0.25378188, 0.03565690, 1.79972143, 0.49513339, 0.85519650, 0.95797393, 1.18930068, 1.52944416, 0.21211345, 0.36342043, 1.12946317, 0.11842668, 1.50611081, 0.47826400, 0.58815796, 0.20995225, 0.25050953, 0.38504902, 2.57865824, 2.37486593, 0.37757152, 0.11404643, 0.05407206, 0.42755586, 0.06360704, 0.04317937, 0.45965630, 0.40623887, 0.21847145, 0.39437507, 0.88480211, 1.40718306, 0.64707974, 0.08332118, 0.36962127, 0.60152779, 0.39706135, 0.55125693, 0.36913746, 1.42278678, 0.69311190, 1.01065256, 1.08925374, 1.34066288, 0.59957988, 0.04203430, 2.77233260, 3.28708257, 1.73709539, 0.45768357, 0.32263242, 0.29657430, 0.02366551, 0.20247683, 1.35654772, 0.00694441, 1.38201424, 0.89090216, 0.88823543, 1.41377148, 0.37135459, 0.36557318, 1.90512208, 0.31316393, 1.10058790, 0.36843826, 1.04621615, 0.99875000, 0.12788404, 0.36530394, 0.05811976, 2.05009814, 0.51824171, 0.87219406, 0.13617999, 1.00594646, 0.74437044, 0.00258926, 0.57609633, 0.39368111, 0.39772202, 0.31094959, 0.37548816, 2.17934168, 0.99261368, 0.25028018, 0.04431970, 0.77118728, 1.56589807, 2.07293061, 0.90534207, 1.07834985, 0.16480664, 0.14750491, 0.30542754, 0.19788267, 0.07055950),
## "status2" = c(0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1),
## "eventtime3" = c(0.25956955, 0.50870505, 0.38243703, 0.48507356, 0.58090695, 0.09143226, 0.11595455, 2.45142917, 0.31529917, 0.01063624, 0.23269836, 0.00314356, 0.35192745, 0.05702643, 0.77426085, 0.04011495, 0.47305223, 1.32361908, 0.49734166, 0.08057535, 0.31222287, 0.49705636, 0.78467962, 0.85062628, 0.01585449, 0.84971402, 0.14097533, 0.64007436, 0.47504948, 0.47065190, 0.91619564, 3.14863908, 0.72449497, 0.34146462, 0.06298503, 1.26862569, 0.07311503, 0.26950937, 0.24296576, 1.46229570, 0.44175144, 0.08437995, 0.11765742, 1.26484624, 0.13972311, 0.88368353, 0.10077329, 0.30071004, 0.18178031, 0.40319616, 0.19262871, 0.84156278, 1.01319195, 1.09334295, 0.06925393, 0.08496785, 0.38289653, 0.77851078, 2.90459458, 0.70906019, 1.44433835, 0.31710947, 0.83804625, 0.52672195, 1.39708324, 0.05738464, 0.00424703, 0.02745054, 0.15640178, 0.60252617, 0.45624187, 0.03877660, 0.26583575, 1.93489936, 0.16157491, 0.16150214, 3.12000133, 1.15754730, 0.20733374, 1.36244884, 0.85908195, 1.17088649, 1.04190785, 0.58512798, 0.17684563, 0.39304759, 0.50360868, 0.25826671, 1.36782193, 0.79286184, 0.54019913, 0.54899883, 0.01927732, 0.83191354, 2.95844611, 0.66324356, 0.37850024, 1.01325887, 0.68367717, 0.16975714, 1.07644784, 2.05425366, 0.76593812, 0.93194348, 0.46623093, 2.96814573, 0.12555074, 1.85279179, 0.91838000, 2.96795061, 0.20853482, 0.55747755, 1.16290689, 0.25204838, 1.45458273, 0.47887218, 2.14245439, 0.73046914, 1.21973505, 0.24528169, 0.60239017, 1.79625436, 0.09840920, 0.42368372, 0.07741995, 1.28366723, 0.51361326, 1.44102172, 0.23235485, 0.00745763, 1.46408645, 0.28432717, 0.50773758, 0.12780739, 1.62522052, 0.60240232, 0.53551248, 0.37865307, 1.43088588, 0.49141086, 0.27257514, 1.28147177, 1.11686803, 0.37442988, 1.00084367, 1.78079525, 0.36024791, 1.50952573, 0.36300718, 0.73043847, 0.25946183, 0.86342025, 0.86724760, 0.65525025, 0.34944216, 0.78352676, 0.76614068, 0.03508025, 1.10827027, 0.13490347, 2.82395488, 0.42936653, 0.15014156, 0.82605928, 0.38453132, 1.19652345, 0.54175957, 0.40951641, 0.25130183, 2.10913985, 3.90959749, 0.83906640, 0.35827788, 0.82174584, 0.43750343, 0.72346693, 2.07799650, 0.03194980, 0.02397542, 0.84753338, 0.39459503, 1.40010494, 1.05098332, 2.16823693, 1.17526902, 1.21647314, 0.20328870, 0.08513324, 0.20774038, 0.14752052),
## "status3" = c(0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0),
## "toxicity1" = c("2", "1", "1", "2", "1", "2", "2", "1", "1", "1", "2", "2", "2", "1", "2", "1", "2", "2", "2", "2", "2", "2", "2", "2", "1", "2", "2", "2", "2", "1", "1", "2", "1", "1", "2", "2", "2", "1", "2", "2", "2", "2", "1", "2", "2", "2", "2", "1", "1", "1", "2", "1", "2", "1", "2", "1", "1", "2", "2", "2", "2", "2", "1", "2", "1", "1", "1", "1", "1", "1", "2", "1", "2", "2", "1", "1", "2", "1", "2", "2", "2", "2", "1", "1", "1", "2", "2", "2", "1", "1", "2", "2", "2", "2", "1", "2", "1", "2", "1", "2", "1", "1", "2", "2", "2", "1", "1", "2", "2", "2", "1", "2", "1", "2", "1", "2", "2", "1", "2", "2", "1", "2", "1", "1", "2", "2", "2", "2", "2", "2", "1", "2", "1", "1", "2", "1", "2", "1", "1", "1", "2", "2", "1", "1", "1", "1", "2", "2", "2", "2", "1", "1", "1", "2", "2", "2", "2", "2", "2", "2", "1", "1", "2", "1", "1", "1", "2", "2", "1", "1", "2", "1", "2", "1", "1", "1", "2", "1", "2", "1", "1", "1", "2", "2", "1", "2", "2", "1", "2", "2"),
## "toxicity2" = c("2", "2", "2", "2", "2", "1", "2", "2", "1", "2", "1", "2", "2", "2", "2", "2", "1", "2", "1", "1", "2", "2", "2", "1", "1", "1", "2", "2", "2", "2", "2", "2", "2", "1", "2", "2", "2", "2", "2", "2", "2", "2", "1", "1", "1", "2", "2", "2", "2", "1", "2", "2", "2", "2", "1", "2", "2", "2", "2", "2", "1", "2", "1", "2", "2", "2", "1", "2", "2", "1", "2", "2", "2", "2", "1", "2", "1", "1", "1", "2", "2", "2", "2", "2", "1", "1", "1", "2", "2", "2", "1", "2", "2", "1", "2", "2", "2", "2", "2", "2", "2", "2", "1", "2", "2", "2", "2", "2", "2", "2", "2", "2", "1", "1", "1", "2", "1", "1", "1", "1", "2", "2", "2", "2", "1", "2", "2", "1", "2", "1", "2", "2", "2", "1", "2", "1", "2", "2", "2", "1", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "1", "2", "2", "2", "2", "1", "1", "1", "1", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "1", "1", "1", "2", "1", "1", "1", "1", "2", "2", "1", "2", "2", "2", "2", "1", "2", "2"),
## "score1" = c( 1.37248589, 2.19299163, 1.73483525, -0.00752315, 1.88739264, 1.41874690, 1.29806497, 1.10816840, 1.04006544, 2.47152734, -0.75581445, 0.90738612, 2.23780927, 1.42039507, 1.16518174, 0.17216119, 1.20579932, 0.00104613, 1.02850830, 1.72188935, 1.20350882, 0.75061290, 3.07140367, 0.01723101, 1.24266243, 1.00337130, -0.55113598, 2.22269088, 0.62567229, 1.03200894, 0.76908950, 0.72202409, -0.33953529, 0.16539723, 1.33126684, 0.13868705, 1.44159579, 1.73077039, 2.37832330, 1.48123808, 0.43160998, 0.60855246, 1.64288621, 1.27620613, 0.74186695, 2.46582983, -0.97327590, 1.92010988, 0.85092865, 2.23738748, 1.52738054, 2.49049279, 0.60756862, 1.18796458, 0.98872659, -0.24592590, 1.94396826, 1.45941533, -0.65322897, 0.22658973, 0.31024850, 2.67183286, 0.90395013, 0.20399559, 0.77525623, -0.26017269, -0.42640232, -1.65363714, -0.03865936, 1.57982435, 3.24926361, 0.65269903, 1.35892133, 0.43765887, 0.03994969, 1.34253659, -0.04648803, 1.55333508, 0.71948130, 0.74599877, 1.99124934, 0.33964643, 1.69329156, 2.52728405, 1.23695659, 2.17387684, -1.48614865, 2.49742470, 1.97382087, 0.51588451, 1.27685067, 0.78477715, 2.70827066, 2.53843123, 0.76378630, -1.07643183, 0.39430703, 1.95235963, -0.18769287, 4.81258034, 2.16396750, -0.52338460, -1.51833505, 0.29247077, 0.71256712, 0.56469169, 0.65692123, 0.96068912, 1.88696599, 0.64005160, 0.27104573, 2.75174562, 0.91396141, 2.10636302, 0.98082216, -0.49346018, 3.70063662, 0.25630558, 2.06519498, 0.96791827, 0.46004031, -0.92564357, 2.00783138, 0.72076520, -0.24956586, 2.24849113, 0.80778662, 1.91197623, -1.15826968, 2.28961053, 0.57189003, 0.74499711, 2.32715824, 1.83869012, -0.46500068, 0.38374730, 1.10610584, -0.84401676, -0.61642285, 1.22767670, 1.92121670, 0.66879656, 2.28723403, 1.05726080, -1.20393313, 0.47708965, 2.08078531, 0.75685866, 0.09055344, 0.13170049, 1.86947504, 0.31999040, 1.17321454, 0.84056196, 1.79349943, 2.69435049, 2.23996869, 1.02943674, 2.65721450, 2.13122314, -0.40241063, 1.15677046, 1.86750860, 0.96676484, 1.95406456, 2.13009673, 1.39369731, 1.37680833, 0.74825709, 1.50409489, -0.32980347, 0.88170055, 0.36675153, 1.67769134, 0.70211824, 1.62792225, 2.67843073, -1.40000254, -0.34984289, 2.20200790, 2.29384272, 0.94040565, 0.31493765, -0.45343930, 2.29412656, 2.17616909, 1.86404508, -1.29673252, -0.39181001, -0.38587675),
## "score2" = c( 2.65317519, 2.19804984, 3.39958837, 2.36418421, 3.64077145, 3.13527144, 0.78355959, 2.90098701, 1.38803990, 3.13141377, 0.84382164, 0.89827149, 2.57784739, 2.18087366, 0.01207711, 3.36059519, 0.82579966, 1.06209054, 3.26831405, 3.36400522, 0.15778253, 1.43518251, 0.34003090, 3.25256983, 0.36760006, 3.05366327, -0.76870569, 0.21894658, 2.25633914, 2.00545663, 2.74176995, -0.68560240, 3.10162133, 3.21802045, 3.50102058, 0.81409682, 0.57813789, 1.51272481, 2.58998056, 1.54575954, 1.78942788, 2.93706409, 1.41297510, 2.49658599, 1.73979822, 3.38222640, 2.56692197, 2.07484513, 1.08979248, 1.04274682, 0.72159509, 2.00701403, 1.81918381, 0.86052608, 2.74381724, 2.62444167, 0.58176694, 2.93314119, 3.15064738, 2.47002921, 2.87963124, 1.07675302, 2.25137934, 0.74625923, 1.84460737, 2.51732094, 2.42306429, 3.15885851, 3.90794404, 2.12231655, 2.95211161, 3.38124150, 1.31147508, 1.99527444, 2.70690665, 2.63814472, 0.21252668, 1.79456144, 2.15728030, 2.77809395, 2.44579281, 1.93244981, 0.51950279, 1.79247109, 1.68026738, 3.28908680, 2.07278306, 1.99467467, 2.81879144, 2.95246510, 0.06090360, 2.87884707, 1.59755053, 1.11761304, 2.61240767, 3.13087171, 0.36334251, 3.08359626, 1.91447252, 2.56312532, 3.09462984, 2.72640935, 0.60034286, 0.71220484, 1.03158452, 0.26331424, 2.83169736, 1.24104689, 4.15507700, 1.57674112, 1.99435006, 2.40036173, 1.63432220, 1.57085616, 2.24469713, 4.21977625, 2.31426981, 1.43621493, 0.51430384, 2.41529859, 1.47193632, 1.64952938, 2.73062194, 1.44337583, 0.55023946, 0.94800357, 1.31716138, 1.38705882, 3.89078004, 1.85458947, 1.36057628, 1.58882395, 2.34279188, 0.86675325, 2.06607030, 2.03799977, 2.92106475, 4.13496564, 1.40017307, 3.24444646, 1.92054298, 3.18175155, 4.18614406, 2.40617493, 1.26164090, 0.04351330, 0.04995430, 1.05900218, 3.19778677, 1.37576061, 1.86713034, 1.98277908, 1.54037072, 3.47293679, 4.16931961, -1.00143131, 0.22801442, 1.63516078, 2.37453378, 0.76592207, 2.47489342, 3.23305746, 1.40687134, 2.75226244, 2.61204551, 1.77134263, 1.29516065, 3.22951605, 3.51851513, 3.34509418, 1.87630847, 2.93390245, 2.01949685, 2.83085211, 1.69550533, 1.94071872, 3.00739670, 0.62220491, 0.99579727, 1.97671970, 3.54674727, 1.24063675, 3.00679266, 2.27421492, 1.91961764, 1.09644750, 2.47314510, 3.38848898, 2.03474609, 0.70741497),
## "score3" = c(1.1422912, 3.7273816, 3.3474971, 3.8424655, 2.9674466, 1.8834731, 1.9977386, 1.9200766, 2.9129226, 1.6749344, 2.7190415, 1.9429009, 4.8992433, 4.5210321, 3.2763423, 1.7971510, 2.1940079, 2.4692713, 3.7387694, 5.6624053, 2.4008662, 2.5411284, 1.4538581, 3.9670348, 0.6024646, 4.3625804, 3.2195859, 2.3472134, 3.2848838, 3.1957958, 2.0305307, 1.8458276, 2.9608437, 2.6592635, 4.0677380, 2.8616322, 3.8583077, 4.1206752, 2.1229097, 2.2782372, 3.5029077, 1.6258354, 3.0368000, 3.5862009, 3.6839435, 2.4312439, 3.2703556, 2.4580630, 3.4026097, 2.8134101, 3.6427508, 3.6714750, 2.5989603, 5.1650258, 1.6047065, 2.8292999, 2.8305656, 2.9670166, 5.3885198, 2.0099881, 2.8989458, 1.0858509, 2.4511256, 3.7138949, 4.8744887, 3.7946575, 2.9052863, 1.6529439, 3.2264245, 3.0241093, 1.1952988, 3.7405965, 2.6342226, 3.9380437, 2.7353587, 2.8336375, 2.9961175, 3.4351701, 2.3799796, 2.7542375, 2.8883925, 3.5208775, 2.7418243, 2.9288784, 4.1494674, 0.5699271, 1.2895520, 3.2585224, 3.4980213, 2.9506039, 4.4946761, 0.4182285, 3.0610335, 3.3277493, 3.1286587, 2.5031202, 3.4321744, 4.4126444, 0.9265798, 3.6894032, 2.7362238, 2.6441475, 2.5188452, 3.2227355, 5.4299103, 4.4962981, 2.2827918, 2.5329461, 3.6629235, 5.3001769, 3.3275101, 3.0638635, 1.8604391, 4.1804102, 3.0413885, 1.7864064, 3.0731958, 2.7426754, 3.2668064, 4.3877243, 3.1930796, 3.5923166, 2.1700255, 3.3925733, 3.3848676, 4.0510447, 4.1557975, 1.9655620, 2.7455319, 4.2736843, 4.5025446, 3.5904095, 2.3693145, 3.7923495, 3.1253846, 3.3227550, 2.5544168, 3.7668439, 1.5964970, 1.8239532, 3.5115965, 4.3167653, 5.3929130, 2.9322306, 2.7962149, 4.9964344, 4.5432178, 1.7716624, 5.6444880, 3.7570137, 4.0553452, 3.9579449, 3.8338791, 2.8307244, 2.5955664, 3.2956016, 2.7681255, 2.5519218, 3.0223108, 3.0444673, 3.4807212, 3.6356199, 0.9992576, 2.3093445, 2.8693548, 2.6557482, 2.9475872, 3.0961239, 3.2664071, 3.5547935, 4.2354459, 3.2851334, 2.5412071, 3.6234780, 2.2760049, 4.6194187, 2.3833266, 3.3845411, 5.1145213, 1.9714326, 2.1132120, 4.2711460, 1.3949146, 4.1222734, 5.1584386, 3.4282466, 4.2011787, 4.0316901, 3.6538742, 5.0120818),
## "status1.noC" = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1))
|
ac60c1321379ab32732f5b914af799cb7b4bd85b | 0feca096f50f64f6cad998204c4c848b10cd37a9 | /R/status_messages.R | f05fb738f8ef7d5e25e759d3e0e6ffa313838d1c | [] | no_license | dakep/examinr | f75487896192294539ac3a3bfb4e15b0de78012c | e02570c5c6f79b10ec126fa2343f6dd0b6ab4fc7 | refs/heads/main | 2023-04-05T23:38:04.656653 | 2021-04-30T21:59:21 | 2021-04-30T21:59:21 | 301,797,514 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,904 | r | status_messages.R | #' @include state_frame.R
#' @importFrom yaml read_yaml
.status_messages <- state_frame(yaml::read_yaml(system.file('messages.yaml', package = 'examinr', mustWork = TRUE),
eval.expr = FALSE))
#' Customize Status Messages
#'
#' Customize the status messages displayed at various times and states during an exam.
#' A template with the default status messages can be saved as a YAML text file with `status_message_template()`.
#'
#' Note that `status_message_template()` should not be called from within an exam document. Rather, generate the
#' template once, modify as necessary, and use in the exam document by specifying the file name in the [exam_document()]
#' or by calling `status_messages()`
#'
#' Calling `status_messages()` without arguments invisibly returns the currently set status messages.
#'
#' @param file path to the messages file. For `status_message_template()`, this is where the template is saved
#' (if `NULL`, only returns the default messages). For `status_messages()`, the file to read the status messages
#' from.
#' @param messages Optionally specify the messages via an R list, in the same format as returned by
#' `status_message_template()`. If `messages` is set, `file` is ignored with a warning.
#'
#' @return `status_message_template()` invisibly returns the default messages as R list.
#' `status_messages()` invisibly returns the new status messages as R list.
#'
#' @importFrom yaml read_yaml
#' @importFrom rlang warn
#' @family localization
#' @export
status_message_template <- function (file) {
if (isTRUE(getOption('knitr.in.progress'))) {
warn("`status_message_template()` should not be called from a knitr document")
}
template <- system.file('messages.yaml', package = 'examinr', mustWork = TRUE)
messages <- read_yaml(template, eval.expr = FALSE)
if (is.null(file)) {
cat(readLines(template, encoding = 'UTF-8'), sep = '\n')
} else {
file.copy(template, file, overwrite = FALSE)
}
return(invisible(messages))
}
#' @rdname status_message_template
#' @importFrom yaml read_yaml
#' @importFrom rlang warn abort
#' @importFrom knitr opts_current
#' @export
status_messages <- function (file, messages) {
if (missing(file) && missing(messages)) {
return(invisible(get_status_message()))
}
if (!is_knitr_context('setup')) {
abort("`status_messages()` must be called in a context='setup' chunk.")
}
if (is_missing(messages) || is.null(messages)) {
messages <- read_yaml(file, eval.expr = FALSE)
} else {
if (!is_missing(file)) {
warn("Ignoring argument `file` as argument `messages` is also given.")
}
}
validate_status_messages(messages)
set_status_messages(messages)
return(invisible(messages))
}
set_status_messages <- function (messages) {
.status_messages$set(messages)
}
#' @importFrom rlang abort
get_status_message <- function (what) {
if (is_missing(what)) {
.status_messages$get()
} else {
.status_messages$get(what) %||% abort(paste("Requested unknown status message", what))
}
}
#' @importFrom yaml read_yaml
#' @importFrom rlang abort
validate_status_messages <- function (messages, template = NULL, path = NULL) {
if (is.null(template)) {
template <- read_yaml(system.file('messages.yaml', package = 'examinr', mustWork = TRUE), eval.expr = FALSE)
}
for (name in names(template)) {
subpath <- paste(c(path, name), collapse = ' > ')
if (is.null(messages[[name]])) {
abort(sprintf("Message string %s is missing.", subpath))
} else if (is.list(template[[name]])) {
if (is.list(messages[[name]])) {
validate_status_messages(messages[[name]], template[[name]], subpath)
} else {
abort(sprintf("Messages malformed: %s must contain sub-items %s.", subpath,
paste(names(template[[name]]), collapse = ', ')))
}
}
}
}
|
741d25e1938a262800d4eec22109b6d535850ef0 | 4f3d98129625eaa618dc25f54988bce179b841fc | /R/gxeRC.R | ca8639015fd8d431a44bd58c87c48548b59d1808 | [] | no_license | SharonLutz/gxeRC | eb62cad249083be7cfc902e63a9139dae58e45b1 | 4e32a4de03232f3c46ff7e03df2b14bc463711c5 | refs/heads/master | 2022-05-30T00:17:04.402843 | 2020-03-02T19:36:52 | 2020-03-02T19:36:52 | 131,533,759 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,057 | r | gxeRC.R | gxeRC <-
function(n=5000,nSNP=3,MAF=c(0.05,0.01,0.005),betaX=c(0.25,0.25,0.25),betaI=c(0,0.05,0.1),
zMu=0,zVar=1,yVar=1,nSim=1000,alpha=0.05,plot.name="gxeRC.pdf"){
####################################
# input parameters
####################################
# n is the number of subjects
# nSNP= the number of SNPS
# MAF= minor allele frequency for the SNPS
# betaX = genetic effect of each SNP
# betaI= effect of interaction for each SNP
# zMu is the mean for the environmental effect
# zVar is the variance for the environmental effect
# nSim is the number of simulations
# alpha is the alpha level, default=0.05
####################################
# Error Checks
####################################
# Check nSNP = length(MAF)==length(betaX)==length(betaI)
if(nSNP!=length(MAF)){stop("Error: nSNP must equal length(MAF).")}
if(nSNP!=length(betaX)){stop("Error: nSNP must equal length(betaX).")}
if(length(betaI)<2){stop("Error: length(betaI) must be 2 or greater.")}
# Check n, nSNP and nSim are integers
if(floor(n)!=ceiling(n)){stop("Error: n must be an integer.")}
if(floor(nSNP)!=ceiling(nSNP)){stop("Error: nSNP must be an integer.")}
if(floor(nSim)!=ceiling(nSim)){stop("Error: nSim must be an integer.")}
# Check n, nSNP and nSim are greater than 0
if(!(n>0)){stop("Error: n must be greater than 0.")}
if(!(nSNP>0)){stop("Error: nSNP must be greater than 0.")}
if(!(nSim>0)){stop("Error: nSim must be greater than 0.")}
# Check zVar > 0 and yVar > 0
if(!(zVar>0)){stop("Error: zVar must be greater than 0.")}
if(!(yVar>0)){stop("Error: yVar must be greater than 0.")}
# Check length(zVar)==1 length(zMu)==1 & length(yVar==1)
if(length(zVar)!=1){stop("Error: zVar must be of length 1")}
if(length(zMu)!=1){stop("Error: zMu must be of length 1")}
if(length(yVar)!=1){stop("Error: yVar must be of length 1")}
# Check alpha>0 & alpha<1
if(alpha<0 | alpha>1){stop("Error: alpha must be between 0 and 1.")}
####################################
# Store Results
####################################
rejectH0<-matrix(0,nrow=length(betaI),ncol=(nSNP+1))
colnames(rejectH0)<-c(paste("lmX",1:nSNP,sep=""),"lmAll")
####################################
# Run Simulations
####################################
for(GLOBALVAR in 1:nSim){
set.seed(GLOBALVAR)
if(floor(GLOBALVAR/100)==ceiling(GLOBALVAR/100)){print(GLOBALVAR)}
####################################
# Generate Data
####################################
# CYCLE through values of betaI
for(bb in 1:length(betaI)){
betaIv<-betaI[bb]
####################################
# simulate data
####################################
# generate the matrix of SNPs
X<-matrix(0,nrow=n,ncol=nSNP)
errorFound <- F
for(xx in 1:nSNP){
X[,xx]<-rbinom(n,2,MAF[xx])
# Check X is not all zero
if(mean(X[,xx])==0|mean(X[,xx])==2){
problemSNP<- xx
errorFound <- T
break
} # let user know they need to increase n or MAF because there is no variability in SNP xx <- give what xx is don't give xx as an index
}
if(errorFound){
errormessage <- paste("Error: Increase n or MAF because there is no variability in SNP ",problemSNP,sep = "")
stop(errormessage)}
# generate the environment Z
z<- rnorm(n,zMu,sqrt(zVar))
zz<-matrix(0,nrow=n,ncol=1)
zz[,1]<-z
# generate the outcome Y
mainEffects<- X%*%betaX
intEffects<- (X%*%rep(betaIv,nSNP))*zz
yMu<- mainEffects+ intEffects
y<-rnorm(n,yMu,sqrt(yVar))
####################################
# linear regression for interaction
####################################
modelA<-lm(y~z+X+X*z)
modelAA<-summary(modelA)$coef
if(nrow(modelAA)<(nSNP+2+nSNP)){stop("Error: Increase n or MAF because there is not enough variability")}
nRow<-nSNP+2
for(rr in 1:nSNP){
if(modelAA[(nRow+rr),4]<(alpha/nSNP)){rejectH0[bb,rr]<-rejectH0[bb,rr]+1}
}
modelR<-lm(y~z+X)
if(anova(modelA,modelR)$P[2]<alpha){rejectH0[bb,"lmAll"]<-rejectH0[bb,"lmAll"]+1}
####################################
# Compile results
####################################
}#end betaI loop
}#end globalvar
rejectMat<-rejectH0/nSim
####################################
# Create plot
####################################
nn<-ncol(rejectMat)
pdf(plot.name)
plot(-1,-1,xlim=c(min(betaI),max(betaI)),ylim=c(0,1),xlab="betaI",ylab="",main="")
for(pp in 1:nn){
lines(betaI,rejectMat[,pp],pch=pp,col=pp,type="b")
}
legend("topleft",c(paste("SNP",1:nSNP,": MAF=",c(MAF),sep=""),"All SNPs"),col=c(1:nn),pch=(1:nn),lwd=1)
dev.off()
####################################
# End function
####################################
list(rejectMat)}
|
0818dd506966db6e2824d2af141e428aee040369 | 6b8d5069dbfd473a14c14458efd33d3e85fc2a4d | /run_analysis.R | c54615d6139f44b741fa0c0b2c6be4e00e0f3149 | [] | no_license | vipvipvip/CleanData_CourseProject | faded72631bf36420f0e34f42fa9aa583bdf8192 | 1199f684c016edaf90d6d13cd4dc96b89507e26a | refs/heads/master | 2021-01-16T18:03:35.592381 | 2014-08-19T20:32:27 | 2014-08-19T20:32:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,625 | r | run_analysis.R | library(data.table)
library(plyr)
run <- function () {
# read 'train/X_train.txt': Training set.
# read 'test/X_test.txt'
# read features.txt -- apply these as columns name for the data sets.
# remove _, -, (, ) from each column
# merge two data set now since col names are identical
# select columns with word "mean" and "sd"
if (!file.exists("UCI HAR Dataset")) {
stop ("./UCI HAR Dataset directory not found.")
}
dfFeatures <- read.csv("./UCI HAR Dataset/features.txt", colClasses = "character", header=F, sep=" ")
dfFeatures[,2] <- gsub("-","", dfFeatures[,2])
dfFeatures[,2] <- gsub("-","", dfFeatures[,2])
dfFeatures[,2] <- gsub('\\(',"", dfFeatures[,2])
dfFeatures[,2] <- gsub('\\)',"", dfFeatures[,2])
dfFeatures[,2] <- gsub('\\,',"", dfFeatures[,2])
dfXTest <- read.table("./UCI HAR Dataset/test/X_test.txt")
colnames(dfXTest) <-dfFeatures[,2]
dfyTest <- read.table("./UCI HAR Dataset/test/y_test.txt")
colnames(dfyTest) <- c("ActivityCode")
dfTestSubjects <- read.table("./UCI HAR Dataset/test/subject_test.txt")
colnames(dfTestSubjects) <- c("Subjects")
dfTest <- cbind(dfXTest, dfyTest, dfTestSubjects)
#2 extract mean & std cols & additional columns
dfTest <- dfTest[,sort(grep("mean|std|ActivityCode|Subjects", names(dfTest)))]
#str(dfTest)
dfXTrain <- read.table("./UCI HAR Dataset/train/X_train.txt")
colnames(dfXTrain) <-dfFeatures[,2]
dfyTrain <- read.table("./UCI HAR Dataset/train/y_train.txt")
colnames(dfyTrain) <- c("ActivityCode")
dfTrainSubjects <- read.table("./UCI HAR Dataset/train/subject_train.txt")
colnames(dfTrainSubjects) <- c("Subjects")
dfTrain <- cbind(dfXTrain, dfyTrain, dfTrainSubjects)
#2 extract mean & std cols & additional columns
dfTrain <- dfTrain[,sort(grep("mean|std|ActivityCode|Subjects", names(dfTrain)))]
#str(dfTrain)
#1 merge both dataset
dfALL <- rbind(dfTrain, dfTest)
#2 extract mean & std cols & additional columns
#dfALL <- dfALL[,sort(grep("mean|std|ActivityCode|Subjects", names(dfALL)))]
#3 descriptive activity names
numCols <- 82 #ncol(dfALL)
dfALL$ActivityLabel <- seq(1:nrow(dfALL))
dfALL[dfALL$ActivityCode %in% c(1),numCols] = "WALKING"
dfALL[dfALL$ActivityCode %in% c(2),numCols] = "WALKING_UPSTAIRS"
dfALL[dfALL$ActivityCode %in% c(3),numCols] = "WALKING_DOWNSTAIRS"
dfALL[dfALL$ActivityCode %in% c(4),numCols] = "SITTING"
dfALL[dfALL$ActivityCode %in% c(5),numCols] = "STANDING"
dfALL[dfALL$ActivityCode %in% c(6),numCols] = "LAYING"
#table(dfALL$ActivityLabel)
dfALL <- dfALL[,c(80:82,1:79)]
#4 columns already labeled correctly. it has 180 rows
#5 prepare tidy data set
final <- data.frame()
for (i in 1:30) {
for (j in 1:6) {
final <- rbind(final, colMeans(dfALL[dfALL$Subjects==i & dfALL$ActivityCode==j, 4:82], c(4,82)))
}
}
colnames(final) <- c(names(dfALL[,4:82]))
final$Subject <- 1
final$ActivityCode <- 1
nr=1
for (i in 1:30) {
for (j in 1:6) {
final$Subject[nr] <- i
final$ActivityCode[nr] <- j
nr = nr + 1
}
}
final$ActivityLabel <- seq(1:nrow(final))
final[final$ActivityCode %in% c(1),ncol(final)] = "WALKING"
final[final$ActivityCode %in% c(2),ncol(final)] = "WALKING_UPSTAIRS"
final[final$ActivityCode %in% c(3),ncol(final)] = "WALKING_DOWNSTAIRS"
final[final$ActivityCode %in% c(4),ncol(final)] = "SITTING"
final[final$ActivityCode %in% c(5),ncol(final)] = "STANDING"
final[final$ActivityCode %in% c(6),ncol(final)] = "LAYING"
final <- final[,c(80,82,1:79)]
#final
write.table(final,"tidySet.csv",row.names=F)
} |
f22765e0b55a015888c2b1230fdd5c348d5eaa7d | 84a81beb43008d608479b4e5c993ca86cfe86873 | /man/gap.barplot.cust.Rd | 507e59e4fb981d4ca011e7fc3b876988974bef24 | [] | no_license | andrew-edwards/sizeSpectra | bb3204c5190ec6ccf09ef3252da30f0c2b4ac428 | 517c18d84f4326b59807de5235ab4cddac74876b | refs/heads/master | 2023-06-22T17:57:26.718351 | 2023-06-12T16:51:23 | 2023-06-12T16:51:23 | 212,250,882 | 7 | 8 | null | null | null | null | UTF-8 | R | false | true | 1,954 | rd | gap.barplot.cust.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotting.R
\name{gap.barplot.cust}
\alias{gap.barplot.cust}
\title{Customising \code{plotrix::gap.barplot} for a histogram with a gap in y-axis}
\usage{
gap.barplot.cust(
y,
gap = c(9, 980),
xaxlab,
xtics,
yaxlab,
ytics = c(seq(0, 8, by = 4), seq(980, 988, by = 4)),
midpoints,
breakpoints,
xlim,
ylim = c(0, 17),
xlab = expression(paste("Values, ", italic(x))),
ylab = "Count in each bin",
horiz = FALSE,
col = NULL,
N = 1000,
...
)
}
\arguments{
\item{y}{vector of data values}
\item{gap}{range of values to be left out}
\item{xaxlab}{labels for the x axis ticks}
\item{xtics}{position of the x axis ticks}
\item{yaxlab}{labels for the y axis ticks}
\item{ytics}{position of the y axis ticks}
\item{midpoints}{midpoints of the bins}
\item{breakpoints}{breaks of the bins}
\item{xlim}{optional x limits for the plot}
\item{ylim}{optional y limits for the plot}
\item{xlab}{label for the x axis}
\item{ylab}{label for the y axis}
\item{horiz}{whether to have vertical or horizontal bars}
\item{col}{color(s) in which to plot the values}
\item{N}{value of highest top short tickmark}
\item{...}{arguments passed to 'barplot'.}
}
\value{
Barplot with a gap in the y-axis
}
\description{
For Figure 1 of MEE paper, to make a histogram (barplot) with a gap in the y-axis.
Customising \code{gap.barplot()} from the package plotrix by Jim Lemon and others
Several default options here are customised for the particular plot (and to
change a few of the defaults in gap.barplot) so the code would
require some modifiying to use more generally.
}
\details{
This function modifies \code{plotrix::gap.barplot()}, between 2nd Sept 2014 and
finalised here in October 2019. \code{plotrix} was written by Jim Lemon and
others and is available from CRAN at https://cran.r-project.org/web/packages/plotrix/index.html.
}
\author{
Andrew Edwards
}
|
9343eededf0aa867e58a66eb8f4be454c8dc620c | 956615ffd5cb4f5d6695f55b17737164b66b4428 | /man/edit_collection_folder.Rd | 1e813e75d14f6d3f063bda07d05d6a25871cc805 | [] | no_license | Pascallio/discogsAPI | dcef3691ce050955343914695bacd8bf93c03af3 | be00217a931009cd4f6821677946b48fd83ded16 | refs/heads/master | 2023-01-22T15:18:51.054985 | 2020-11-19T21:47:09 | 2020-11-19T21:47:09 | 311,048,605 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 792 | rd | edit_collection_folder.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Collection.R
\name{edit_collection_folder}
\alias{edit_collection_folder}
\title{Edit a folder’s metadata.}
\usage{
edit_collection_folder(
username,
folder_id
token,
name = ""
)
}
\arguments{
\item{username}{String containing a valid username}
\item{folder_id}{Valid identifier for a folder}
\item{token}{Token object obtained from authorize() or a string containing your personal access token}
\item{name}{(optional) Name for the folder}
}
\description{
Edit a folder’s metadata.
}
\details{
Folders 0 and 1 cannot be renamed.
Authentication as the collection owner is required.
}
\examples{
token <- authorize("key", "secret")
edit_collection_folder("username", 3, token, name = "new_folder_name")
}
|
4fff7228ea80e76c6e081d5bc5912c38599df46e | d7d100225fe95a58431b89ae2e48126f2589dbdb | /hello.R | 7d49c4f26165a6d858aea88ddd3bfd645422054d | [] | no_license | shubhamkalra27/datasciencecoursera | 8253ce1e1b29d5bce71193e62cb148767e4b92fc | a37e00a905a3f2c8157e143dc29307673b100903 | refs/heads/master | 2021-01-10T03:20:41.688090 | 2015-11-04T15:01:00 | 2015-11-04T15:01:00 | 45,500,079 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,662 | r | hello.R | # Topic: R Sessions - MSU
# Purpose: Introduce basic concepts of R
# Session 1
# Set working directory
setwd("D:/MSU/R/Working")
# Get the location of the working directory
getwd()
# Read a data file into R
# Read a csv file
trans <- read.csv("TransactionMaster.csv")
cust <- read.csv("CustomerMaster.csv")
# Read files using the read.table command
trans_1 <- read.table("TransactionMaster.csv", header=FALSE, sep=",",
stringsAsFactors=FALSE, na.strings = TRUE)
# Create different types of data
# vectors/array
a <- c(1,3,4,6)
a
b <- c("LedZep","Floyd","Who?","Doors")
b
1:16
# Matrix
mat1 <- matrix(c(1:16), ncol=4, byrow=TRUE,
dimnames = list(c("row1", "row2","row3","row4"),
b))
mat1
# list
list1 <- list(a,mat1)
list1
# dataframe
data1 <- data.frame(a,b)
data2 <- data.frame(BandName = b, Rank=a)
data1
data2
# Get basic statistics about the data
summary(trans)
head(trans)
str(trans)
colnames(cust)
# Accessing elements from R objects
a
a[3]
list1[1]
# Access Columns
data2[2]
data2[,2] # What's the difference in these 2 methods?
cust["Branch_Num"]
trans$System_Period
# Access rows
data2[2,]
trans[1223,]
# Subset based on rows and columns
cust[c(1,2,3,4),c(3,4,5)]
# Session 2
# Basic functions in R
# Dimensions of dataframes
nrow(trans)
ncol(cust)
# Identifying unique entries
unique(trans$Branch_Number)
# Counting the number of entries
length(trans$Invoice_Date)
length(unique(trans$Branch_Number))
# 'Which' function
which(cust$City == 'ATLANTA')
which(cust$City == 'NEW YORK')
# Subset the dataset based on the 'which' function
Atlanta_cust <- cust[which(cust$City == 'ATLANTA'),]
# Subset function
Atlanta_cust_1 <- subset(cust, cust$City == 'ATLANTA')
# Text manipulation functions
# Find function
grep("CARTUM", cust$Customer_Name)
which(cust$Customer_Name == 'CARTUM') # Why we use grep when 'which' function is available
cartum_cust <- cust[grep("CARTUM", cust$Customer_Name),]
# Find and Replace
gsub("-","",cust$Phone_Number)
cust$Phone_Number <- gsub("-","",cust$Phone_Number)
# Check out regexpr function
# Concatenation function
cust$Full_Name <- paste(cust$Contact_Name_First,
cust$Contact_Name_Last,sep=" ")
# Sorting data
order(a)
order(-trans$Sales_Amount)
trans_sorted <- trans[order(-trans$Sales_Amount),]
# Merging data
# Inner Join
inner <- merge(trans,cust,by.x="Customer_Number", by.y="Customer_Number", all=FALSE)
# Outer Join
outer <- merge(trans,cust,by.x="Customer_Number", by.y="Customer_Number", all=TRUE)
# Left Join
left <- merge(trans,cust,by.x="Customer_Number", by.y="Customer_Number", all.x=TRUE)
# Right Join
right <- merge(trans,cust,by.x="Customer_Number", by.y="Customer_Number", all.y=TRUE)
# Session 3
# Reading Date formats:
# Reference: http://www.statmethods.net/input/dates.html
inv_date <- as.Date(trans$Invoice_Date, format = c("%d-%b-%y"))
inv_date
# Test on real data
trans$Invoice_Date <- as.Date(trans$Invoice_Date, format = c("%d-%b-%y"))
trans$Service_Date <- as.Date(trans$Service_Date, format = c("%d-%b-%y"))
# Get the system time
Sys.time()
which(trans$Invoice_Date < trans$Service_Date)
# Read up on as.POSIXct and as.POSIXlt. When and why are they useful??
# Extract time related values
format(trans$Invoice_Date, "%d")
max(format(trans$Invoice_Date, "%y"))
max(format(trans$Service_Date, "%Y"))
trans$Service_Date
max(trans$Service_Date)
# Subset based on year
trans_sub <- subset(trans, as.numeric(format(trans$Invoice_Date,"%m")) %in% c(1,2,3))
# Aggregate function
trans_agg <- aggregate(trans$Sales_Amount, by=list(trans$Branch_Number), FUN=sum)
trans_agg <- aggregate(trans["Sales_Amount"], by=list(trans[,"Branch_Number"]), FUN=sum)
trans_agg_1 <- aggregate(trans$Sales_Amount ~ trans$Branch_Number, FUN=max)
trans_agg_2 <- aggregate(trans$Sales_Amount ~ trans$Branch_Number + trans$Product_Number, FUN = sum)
# Let's say i want to do 'Max - Min' when aggregating, how do i do that?
# Flow Control
x <- runif(100,min=100, max=10000)
measure <- "max"
if(measure == "median" ) {
print(median(x))
} else if (measure == "mean") {
print(mean(x))
} else {
print("Wrong Input")
}
## Include apply family
# SQLDF in R
install.packages("sqldf")
library(sqldf)
df <- sqldf("select distinct Product_Number from trans")
sqldf("select distinct Customer_Number as Customers
from cust where State = 'FL'")
leftjoin <- sqldf("select
a.*,
b.*
from cust as a
left join trans as b
on a.Customer_Number = b.Customer_Number")
innerjoin <- sqldf("select
a.Customer_Number,
a.City,
b.Sales_Amount
from cust as a
inner join trans as b
on a.Customer_Number = b.Customer_Number")
SalesByCustomer <- sqldf("select
Customer_Number as Customer,
sum(Sales_Amount) as Total_Sales
from innerjoin
group by Customer_Number")
# Session 4
# Custom Functions in R
oddcount <- function(x) {
k <- 0 ## Assign the value 0 to k
for (n in x) { ## Start a FOR loop for every element in x
if (n %% 2 == 1) k <- k + 1 ## %% is a modulo operator
}
return(k)
}
oddcount(c(1,2,3,5,7,9,14))
# For-Loops
for ( i in 1:5) {
print(i)
}
# While-Loop
i <- 0
while (i < 10) {
print(i)
i <- i + 1
}
# Basic plots and charts in R
# Good reference site: http://www.harding.edu/fmccown/r/
# Read in world bank dataset
world <- read.csv("worldbank.csv")
summary(world)
# Subset the dataset
attach(world)
# Plotting
# Histogram
hist(life_expectancy)
hist(infant_mortality_rate , breaks=10,
main = "Infant Mortality rate", xlab = "Infant MR")
plot(density(infant_mortality_rate))
# Scatterplots
# Test the hypothesis - 'Higher the life expectancy, lower the infant mortality'
plot(life_expectancy, infant_mortality_rate)
plot(life_expectancy, infant_mortality_rate, main = "Hypothesis Test",
xlab = "Life Expectancy", ylab = "Infant mortality rate",
col = "blue", pch = 20)
pairs(~life_expectancy + infant_mortality_rate + birth_rate )
pairs(world)
# Look at 'Pairs' function
# Sample Bar plot
# Subset the data
world_subset <- subset(world, country_name %in% c("Australia", "India",
"Mexico", "Bulgaria",
"Finland", "Uruguay"))
detach(world)
# Bar and Pie charts
attach(world_subset)
barplot(energy_use_percapita, main = "Energy per capita",
xlab = "Country", ylab = "Consumption",names.arg= country_name)
barplot(energy_use_percapita, main = "Energy per capita",
xlab = "Country", ylab = "Consumption",
col=rainbow(length(country_name)), legend = country_name)
# Sample Pie plot
pie(x=fertility_rate, col = rainbow(length(country_name)),
label = paste(country_name, fertility_rate, sep = "-"),
main = "Fertility rate")
detach(world_subset)
# Session 5
# Misc operations and Mathematical functions
# Missing values
is.na(world)
which(is.na(world))
# What's the difference between NA and Null??
world_2 <- na.omit(world)
# Quick test 1: Create a dataset removing all NA's
world_subset_2 <- world[which(!is.na(world$energy_use_percapita)),]
world <- world[-1,]
# Quick test 2: Calculate the % of NA values in each column in the dataset 'world'
per <- function(x)
{
k <- (length(which(is.na(x)))*100/length(x))
k <- round(k, digits = 2)
k <- paste(k,"%", sep = "")
}
out <- sapply(world[,2:9], per)
out
# Tables
table(as.factor(cust$City), as.factor(cust$Customer_Number))
# Correlations
attach(world)
cor(energy_use_percapita, gni_per_capita)
cor(world$energy_use_percapita, world$gni_per_capita,use="pairwise.complete",
method = "spearman")
# Quantile subsets
quantile(energy_use_percapita, probs = c(0.05,0.95), na.rm=T)
# Small mathematics
# mean
mean(birth_rate)
# standard deviation
sd(birth_rate)
# Sampling
# From normal distribution
rnorm(10, mean = 10, sd = 22)
# From t-distribution
rt(100, df=2,ncp=23)
set.seed(2123)
rnorm(10, mean = 10, sd = 22)
# Create your own dataset with your employee number
set.seed(3547)
c1 <- c("India","Pakistan","Sri Lanka", "Bangladesh")
c2 <- rnorm(4, mean = 200, sd = 50)
c3 <- rnorm(4, mean = 5, sd = 2)
c4 <- rbeta(4, 1,2)
asia <- data.frame(Country = c1, Avg_team_score = c2,
Avg_team_Wickets = c3, Stat = c4)
asia
a <- na.omit(world[2:6])
b <- cor(a[1],a[2:5])
max(b)
colnames(b)[order(-b)[which(b[1,] %in% b[1,order(-b)[1:4]])]]
order(-b)[1:2]
which(b = max(b))
|
869eb541eaebc734b13141f070d9705727f4696e | 4840eb138586354c1d12adc6a77367cfaffb4d8e | /WebCrawler/csx/Debug/ServiceDefinition.rd | 313fa4076ce0209d8d684f7c6f93449c55ca4e26 | [] | no_license | rhenvar/WebCrawler | c1a42d75815f18df017604995c5ffff6786c8a8f | 108f1012721849f37dadeb7359672b10c1b76b90 | refs/heads/master | 2016-09-13T13:17:19.918077 | 2016-05-16T22:22:56 | 2016-05-16T22:22:56 | 58,058,898 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,987 | rd | ServiceDefinition.rd | <?xml version="1.0" encoding="utf-8"?>
<serviceModel xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema" name="WebCrawler" generation="1" functional="0" release="0" Id="7ec79cca-40a9-4d07-bb1c-a46cb37add4f" dslVersion="1.2.0.0" xmlns="http://schemas.microsoft.com/dsltools/RDSM">
<groups>
<group name="WebCrawlerGroup" generation="1" functional="0" release="0">
<componentports>
<inPort name="CrawlerWebRole:Endpoint1" protocol="http">
<inToChannel>
<lBChannelMoniker name="/WebCrawler/WebCrawlerGroup/LB:CrawlerWebRole:Endpoint1" />
</inToChannel>
</inPort>
</componentports>
<settings>
<aCS name="CrawlerWebRole:APPINSIGHTS_INSTRUMENTATIONKEY" defaultValue="">
<maps>
<mapMoniker name="/WebCrawler/WebCrawlerGroup/MapCrawlerWebRole:APPINSIGHTS_INSTRUMENTATIONKEY" />
</maps>
</aCS>
<aCS name="CrawlerWebRole:Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" defaultValue="">
<maps>
<mapMoniker name="/WebCrawler/WebCrawlerGroup/MapCrawlerWebRole:Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" />
</maps>
</aCS>
<aCS name="CrawlerWebRoleInstances" defaultValue="[1,1,1]">
<maps>
<mapMoniker name="/WebCrawler/WebCrawlerGroup/MapCrawlerWebRoleInstances" />
</maps>
</aCS>
<aCS name="CrawlerWorkerRole:APPINSIGHTS_INSTRUMENTATIONKEY" defaultValue="">
<maps>
<mapMoniker name="/WebCrawler/WebCrawlerGroup/MapCrawlerWorkerRole:APPINSIGHTS_INSTRUMENTATIONKEY" />
</maps>
</aCS>
<aCS name="CrawlerWorkerRole:Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" defaultValue="">
<maps>
<mapMoniker name="/WebCrawler/WebCrawlerGroup/MapCrawlerWorkerRole:Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" />
</maps>
</aCS>
<aCS name="CrawlerWorkerRoleInstances" defaultValue="[1,1,1]">
<maps>
<mapMoniker name="/WebCrawler/WebCrawlerGroup/MapCrawlerWorkerRoleInstances" />
</maps>
</aCS>
</settings>
<channels>
<sFSwitchChannel name="IE:CrawlerWorkerRole:WorkerEndpoint">
<toPorts>
<inPortMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWorkerRole/WorkerEndpoint" />
</toPorts>
</sFSwitchChannel>
<lBChannel name="LB:CrawlerWebRole:Endpoint1">
<toPorts>
<inPortMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWebRole/Endpoint1" />
</toPorts>
</lBChannel>
</channels>
<maps>
<map name="MapCrawlerWebRole:APPINSIGHTS_INSTRUMENTATIONKEY" kind="Identity">
<setting>
<aCSMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWebRole/APPINSIGHTS_INSTRUMENTATIONKEY" />
</setting>
</map>
<map name="MapCrawlerWebRole:Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" kind="Identity">
<setting>
<aCSMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWebRole/Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" />
</setting>
</map>
<map name="MapCrawlerWebRoleInstances" kind="Identity">
<setting>
<sCSPolicyIDMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWebRoleInstances" />
</setting>
</map>
<map name="MapCrawlerWorkerRole:APPINSIGHTS_INSTRUMENTATIONKEY" kind="Identity">
<setting>
<aCSMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWorkerRole/APPINSIGHTS_INSTRUMENTATIONKEY" />
</setting>
</map>
<map name="MapCrawlerWorkerRole:Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" kind="Identity">
<setting>
<aCSMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWorkerRole/Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" />
</setting>
</map>
<map name="MapCrawlerWorkerRoleInstances" kind="Identity">
<setting>
<sCSPolicyIDMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWorkerRoleInstances" />
</setting>
</map>
</maps>
<components>
<groupHascomponents>
<role name="CrawlerWebRole" generation="1" functional="0" release="0" software="C:\Users\iGuest\Source\Repos\WebCrawler\WebCrawler\csx\Debug\roles\CrawlerWebRole" entryPoint="base\x64\WaHostBootstrapper.exe" parameters="base\x64\WaIISHost.exe " memIndex="-1" hostingEnvironment="frontendadmin" hostingEnvironmentVersion="2">
<componentports>
<inPort name="Endpoint1" protocol="http" portRanges="80" />
</componentports>
<settings>
<aCS name="APPINSIGHTS_INSTRUMENTATIONKEY" defaultValue="" />
<aCS name="Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" defaultValue="" />
<aCS name="__ModelData" defaultValue="<m role="CrawlerWebRole" xmlns="urn:azure:m:v1"><r name="CrawlerWebRole"><e name="Endpoint1" /></r><r name="CrawlerWorkerRole"><e name="WorkerEndpoint" /></r></m>" />
</settings>
<resourcereferences>
<resourceReference name="DiagnosticStore" defaultAmount="[4096,4096,4096]" defaultSticky="true" kind="Directory" />
<resourceReference name="EventStore" defaultAmount="[1000,1000,1000]" defaultSticky="false" kind="LogStore" />
</resourcereferences>
</role>
<sCSPolicy>
<sCSPolicyIDMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWebRoleInstances" />
<sCSPolicyUpdateDomainMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWebRoleUpgradeDomains" />
<sCSPolicyFaultDomainMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWebRoleFaultDomains" />
</sCSPolicy>
</groupHascomponents>
<groupHascomponents>
<role name="CrawlerWorkerRole" generation="1" functional="0" release="0" software="C:\Users\iGuest\Source\Repos\WebCrawler\WebCrawler\csx\Debug\roles\CrawlerWorkerRole" entryPoint="base\x64\WaHostBootstrapper.exe" parameters="base\x64\WaWorkerHost.exe " memIndex="-1" hostingEnvironment="consoleroleadmin" hostingEnvironmentVersion="2">
<settings>
<aCS name="APPINSIGHTS_INSTRUMENTATIONKEY" defaultValue="" />
<aCS name="Microsoft.WindowsAzure.Plugins.Diagnostics.ConnectionString" defaultValue="" />
<aCS name="__ModelData" defaultValue="<m role="CrawlerWorkerRole" xmlns="urn:azure:m:v1"><r name="CrawlerWebRole"><e name="Endpoint1" /></r><r name="CrawlerWorkerRole"><e name="WorkerEndpoint" /></r></m>" />
</settings>
<resourcereferences>
<resourceReference name="DiagnosticStore" defaultAmount="[4096,4096,4096]" defaultSticky="true" kind="Directory" />
<resourceReference name="EventStore" defaultAmount="[1000,1000,1000]" defaultSticky="false" kind="LogStore" />
</resourcereferences>
</role>
<sCSPolicy>
<sCSPolicyIDMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWorkerRoleInstances" />
<sCSPolicyUpdateDomainMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWorkerRoleUpgradeDomains" />
<sCSPolicyFaultDomainMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWorkerRoleFaultDomains" />
</sCSPolicy>
</groupHascomponents>
</components>
<sCSPolicy>
<sCSPolicyUpdateDomain name="CrawlerWebRoleUpgradeDomains" defaultPolicy="[5,5,5]" />
<sCSPolicyUpdateDomain name="CrawlerWorkerRoleUpgradeDomains" defaultPolicy="[5,5,5]" />
<sCSPolicyFaultDomain name="CrawlerWebRoleFaultDomains" defaultPolicy="[2,2,2]" />
<sCSPolicyFaultDomain name="CrawlerWorkerRoleFaultDomains" defaultPolicy="[2,2,2]" />
<sCSPolicyID name="CrawlerWebRoleInstances" defaultPolicy="[1,1,1]" />
<sCSPolicyID name="CrawlerWorkerRoleInstances" defaultPolicy="[1,1,1]" />
</sCSPolicy>
</group>
</groups>
<implements>
<implementation Id="40abb8c0-0966-43ea-bde0-8a60f8102ecc" ref="Microsoft.RedDog.Contract\ServiceContract\WebCrawlerContract@ServiceDefinition">
<interfacereferences>
<interfaceReference Id="bd17416d-0cae-4ebe-828b-60c322f69850" ref="Microsoft.RedDog.Contract\Interface\CrawlerWebRole:Endpoint1@ServiceDefinition">
<inPort>
<inPortMoniker name="/WebCrawler/WebCrawlerGroup/CrawlerWebRole:Endpoint1" />
</inPort>
</interfaceReference>
</interfacereferences>
</implementation>
</implements>
</serviceModel> |
12ef18f55b43fb00388363716556fae1ca98fc8f | 119b181488acae0e7d49a5d35ee7decf527ebe44 | /man/getDisaggCommodityPercentages.Rd | a1b7b7eae29c1bd16f31c83d138df6b9012a448f | [
"MIT"
] | permissive | USEPA/useeior | 0d46f1ca9ca1756e1760b153be620a234fddda03 | 169ae5a16c4e367a3c39ceabff3c85f0b4e187a1 | refs/heads/master | 2023-08-06T19:03:28.121338 | 2023-07-14T18:39:13 | 2023-07-14T18:39:13 | 221,473,707 | 30 | 24 | MIT | 2023-09-06T15:47:55 | 2019-11-13T14:07:05 | R | UTF-8 | R | false | true | 627 | rd | getDisaggCommodityPercentages.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DisaggregateFunctions.R
\name{getDisaggCommodityPercentages}
\alias{getDisaggCommodityPercentages}
\title{Obtain default disaggregation percentages for commodities from the disaggregation input files.}
\usage{
getDisaggCommodityPercentages(disagg)
}
\arguments{
\item{disagg}{Specifications for disaggregating the current Model}
}
\value{
A dataframe with the default disaggregation percentages for the Commodities of the current model
}
\description{
Obtain default disaggregation percentages for commodities from the disaggregation input files.
}
|
b002965ba1dabb7ccfc4fc65e96d03308d0fbbc6 | 48c55aad7dcb6196d98be4d13878d2617509e404 | /shiny/ui.R | 1eceb2260969536e1273d6faa978e31291c1388d | [
"MIT"
] | permissive | majbc1999/APPR-2019-20 | 234cd5c06bdc24a62b0e7e95dbd8940061a6b1e3 | 3a75dc1f3ccdff4565250ad8769da57bedc68434 | refs/heads/master | 2020-09-10T03:44:06.745219 | 2020-08-07T16:07:23 | 2020-08-07T16:07:23 | 221,640,084 | 1 | 0 | MIT | 2019-11-28T13:50:09 | 2019-11-14T07:45:22 | R | UTF-8 | R | false | false | 126 | r | ui.R | library(shiny)
shinyUI(fluidPage(
titlePanel("Obnovljivi viri po svetu"),
DT::dataTableOutput("TD_world_obnovljivi"))
) |
3f962ec2767e918b78dc6125220cd551ccec7bd5 | bbf8cd2c300eb3c7f5c18dda9855969dc537ebfc | /R/estimate.R | f615655f7c2b353e278e3cc18a02e55088bdf607 | [] | no_license | Gootjes/semlj | 11eb07aa4f6b945f4acd91fe395da66260a56477 | e9bbcc305b52d4e043937d27aca1656b4426ed8e | refs/heads/main | 2023-05-08T09:46:35.041344 | 2021-05-24T16:05:26 | 2021-05-24T16:05:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,410 | r | estimate.R | ## This class takes care of estimating the model and return the results. It inherit from Syntax, and define the same tables
## defined by Syntax, but it fill them with the results.
Estimate <- R6::R6Class("Estimate",
inherit = Syntax,
cloneable=FALSE,
class=FALSE,
list(
model=NULL,
tab_fit=NULL,
tab_fitindices=NULL,
ciwidth=NULL,
tab_constfit=NULL,
initialize=function(options,datamatic) {
super$initialize(options=options,datamatic=datamatic)
self$ciwidth<-options$ciWidth/100
},
estimate=function(data) {
## prepare the options based on Syntax definitions
lavoptions<-list(model = private$.lav_structure,
data = data,
se=self$options$se,
bootstrap=self$options$bootN,
estimator=self$options$estimator
)
if (is.something(self$multigroup)) {
lavoptions[["group"]]<-self$multigroup$var
lavoptions[["group.label"]]<-self$multigroup$levels
}
if (self$options$estimator=="ML") {
lavoptions[["likelihood"]]<-self$options$likelihood
}
## estimate the models
results<-try_hard({do.call(lavaan::lavaan,lavoptions) })
self$warnings<-list(topic="info",message=results$warning)
self$errors<-results$error
if (is.something(self$errors))
return(self$errors)
## ask for the paramters estimates
self$model<-results$obj
.lav_params<-lavaan::parameterestimates(
self$model,
ci=self$options$ci,
standardized = T,
level = self$ciwidth,
boot.ci.type = self$options$bootci
)
## we need some info initialized by Syntax regarding the parameters properties
.lav_structure<-private$.lav_structure
sel<-grep("==|<|>",.lav_structure$op,invert = T)
.lav_structure<-.lav_structure[sel,]
## make some change to render the results
.lav_params$free<-(.lav_structure$free>0)
## collect regression coefficients table
self$tab_coefficients<-.lav_params[.lav_params$op=="~",]
## collect loadings table
self$tab_loadings<-.lav_params[.lav_params$op=="=~",]
## collect variances and covariances table
self$tab_covariances<-.lav_params[.lav_params$op=="~~",]
## collect defined parameters table
self$tab_defined<-.lav_params[.lav_params$op==":=",]
if (nrow(self$tab_defined)==0) self$tab_defined<-NULL
tab<-self$tab_covariances
### collect intercepts
self$tab_intercepts<-.lav_params[.lav_params$op=="~1",]
if (nrow(self$tab_intercepts)==0) self$tab_intercepts<-NULL
#### fit tests ###
alist<-list()
ff<-lavaan::fitmeasures(self$model)
alist<-list()
if (ff[["df"]]>0)
alist[[1]]<-list(label="User Model",chisq=ff[["chisq"]],df=ff[["df"]],pvalue=ff[["pvalue"]])
try(alist[[length(alist)+1]]<-list(label="Baseline Model",chisq=ff[["baseline.chisq"]],df=ff[["baseline.df"]],pvalue=ff[["baseline.pvalue"]]))
self$tab_fitindices<-as.list(ff)
self$tab_fit<-alist
# fit indices
alist<-list()
alist[[length(alist)+1]]<-c(info="Estimation Method",value=self$model@Options$estimator)
alist[[length(alist)+1]]<-c(info="Number of observations",value=lavaan::lavInspect(self$model,"ntotal"))
alist[[length(alist)+1]]<-c(info="Free parameters",value=self$model@Fit@npar)
alist[[length(alist)+1]]<-c(info="Converged",value=self$model@Fit@converged)
alist[[length(alist)+1]]<-c(info="",value="")
try(alist[[length(alist)+1]]<-c(info="Loglikelihood user model",value=round(ff[["logl"]],digits=3) ))
try(alist[[length(alist)+1]]<-c(info="Loglikelihood unrestricted model",value=round(ff[["unrestricted.logl"]],digits=3)))
alist[[length(alist)+1]]<-c(info="",value="")
self$tab_info<-alist
if (is.something(self$tab_constfit)) {
check<-sapply(self$tab_constfit$op,function(con) length(grep("<|>",con))>0,simplify = T)
if (any(check)) {
self$warnings<-list(topic="constraints",message=WARNS[["scoreineq"]])
} else {
tab<-lavaan::lavTestScore(self$model,
univariate = self$options$scoretest,
cumulative = self$options$cumscoretest)
if (self$options$scoretest) {
names(tab$uni)<-c("lhs","op","rhs","chisq","df","pvalue")
self$tab_constfit<-tab$uni
self$tab_constfit$type="Univariate"
}
if (self$options$cumscoretest) {
names(tab$cumulative)<-c("lhs","op","rhs","chisq","df","pvalue")
tab$cumulative$type<-"Cumulative"
self$tab_constfit<-rbind(self$tab_constfit,tab$cumulative)
}
self$tab_fit[[length(self$tab_fit)+1]]<-list(label="Constraints Score Test",
chisq=tab$test$X2,
df=tab$test$df,
pvalue=tab$test$p.value)
}
} # end of checking constraints
ginfo("Estimation is done...")
} # end of private function estimate
) # end of private
) # end of class
|
266d159d99c5846e73735ceef49cf1efbfa08ed4 | 14b088d7a841ea2391a3c85626eacc00410603e8 | /src/mitX_theAnalyticsEdge/mitX-15.071x-analyticEdge_unit5_recitation.R | c794b0eb773aac18bf444b24ceef470c33f0219b | [
"MIT"
] | permissive | pparacch/PlayingWithDataScience | f634080bee7b93fbc9071469b043996db72da061 | 5a753edde6a479cc3ee797bd30cfc88317557bda | refs/heads/master | 2020-04-15T23:46:49.848710 | 2017-04-07T13:48:30 | 2017-04-07T13:48:30 | 28,199,608 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,951 | r | mitX-15.071x-analyticEdge_unit5_recitation.R | # Let's begin by creating a data frame called emails
# using the read.csv function.
# And loading up energy_bids.csv.
#
# And as always, in the text analytics week,
# we're going to pass stringsAsFactors=FALSE to this
# function.
# So we can take a look at the structure of our new data frame
# using the str function.
# Load the dataset
emails = read.csv("energy_bids.csv", stringsAsFactors=FALSE)
# We can see that there are 855 observations.
# This means we have 855 labeled emails in the data set.
# And for each one we have the text of the email
# and whether or not it's responsive to our query
# about energy schedules and bids.
str(emails)
# So let's take a look at a few example emails in the data set,
# starting with the first one.
# So the first email can be accessed with emails$email[1],
# and we'll select the first one.
# Note use the strwrap function and pass it the long string you
# want to print out, in this case emails$email.
# Now we can see that this has broken down our long string
# into multiple shorter lines that are much easier to read.
# Look at emails
# So let's take a look now at this email,
# now that it's a lot easier to read.
# We can see just by parsing through the first couple
# of lines that this is an email that's
# talking about a new working paper,
# "The Environmental Challenges and Opportunities
# in the Evolving North American Electricity Market"
# is the name of the paper.
# And it's being released by the Commission
# for Environmental Cooperation, or CEC.
# So while this certainly deals with electricity markets,
# it doesn't have to do with energy schedules or bids.
# So it is not responsive to our query.
# So we can take a look at the value in the responsive
# variable for this email using emails$responsive and selecting
# the first one.
# And we have value 0 there.
emails$email[1]
strwrap(emails$email[1])
emails$responsive[1]
# And scrolling up to the top here we can
# see that the original message is actually very short,
# it just says FYI (For Your Information),
# and most of it is a forwarded message.
# So we have all the people who originally
# received the message.
# And then down at the very bottom is the message itself.
# "Attached is my report prepared on behalf of the California
# State auditor."
# And there's an attached report, ca report new.pdf.
# Now our data set contains just the text of the emails
# and not the text of the attachments.
# But it turns out, as we might expect,
# that this attachment had to do with Enron's electricity bids
# in California,
# and therefore it is responsive to our query.
emails$email[2]
strwrap(emails$email[2])
emails$responsive[2]
# So now let's look at the breakdown
# of the number of emails that are responsive to our query using
# the table function.
# We're going to pass it emails$responsive.
#
# And as we can see the data set is unbalanced,
# with a relatively small proportion of emails responsive
# to the query.
# And this is typical in predictive coding problems.
# Responsive emails
table(emails$responsive)
# Now it's time to construct and preprocess the corpus.
# So we'll start by loading the tm package with library(tm).
# Load tm package
install.packages("tm")
library(tm)
# Then we also need to install the package SnowballC.
# This package helps us use the tm package.
# And go ahead and load the snowball package as well.
install.packages("SnowballC")
library(SnowballC)
# we'll construct a variable called corpus using the Corpus
# and VectorSource functions and passing in all the emails
# in our data set, which is emails$email.
# Create corpus
corpus = Corpus(VectorSource(emails$email))
# So now that we've constructed the corpus,
# we can output the first email in the corpus.
# We'll start out by calling the strwrap function to get it
# on multiple lines, and then we can select the first element
# in the corpus using the double square bracket notation
# and selecting element 1.
strwrap(corpus[[1]])
# And we can see that this is exactly
# the same email that we saw originally,
# the email about the working paper.
# So now we're ready to preprocess the corpus using the tm_map
# function.
# So first, we'll convert the corpus
# to lowercase using tm_map and the tolower function.
# So we'll have corpus = tm_map(corpus, tolower).
# Pre-process data
corpus = tm_map(corpus, tolower)
strwrap(corpus[[1]])
# IMPORTANT NOTE: If you are using the latest version of the tm package,
# you will need to run the following line before continuing (it converts corpus to a Plain Text Document).
# This is a recent change having to do with the tolower function that occurred after this video
# was recorded.
corpus = tm_map(corpus, PlainTextDocument)
strwrap(corpus[[1]])
# And then we'll do the exact same thing except removing
# punctuation, so we'll have corpus = tm_map(corpus,
# removePunctuation).
corpus = tm_map(corpus, removePunctuation)
strwrap(corpus[[1]])
# We'll remove the stop words with removeWords function
# and we'll pass along the stop words of the English language
# as the words we want to remove.
corpus = tm_map(corpus, removeWords, stopwords("english"))
strwrap(corpus[[1]])
# And lastly, we're going to stem the document.
# So corpus = tm_map(corpus, stemDocument).
corpus = tm_map(corpus, stemDocument)
# And now that we've gone through those four preprocessing steps,
# we can take a second look at the first email in the corpus.
# So again, call strwrap(corpus[[1]]).
strwrap(corpus[[1]])
# And now it looks quite a bit different.
# We can come up to the top here.
# It's a lot harder to read now that we removed
# all the stop words and punctuation and word stems,
# but now the emails in this corpus
# are ready for our machine learning algorithms.
# BAG OF WORDS
# Now let's build the document-term matrix
# for our corpus.
# So we'll create a variable called
# dtm that contains the DocumentTermMatrix(corpus).
# Create matrix
dtm = DocumentTermMatrix(corpus)
dtm
# The corpus has already had all the pre-processing run on it.
# So to get the summary statistics about the document-term matrix,
# we'll just type in the name of our variable, dtm.
# And what we can see is that even though we
# have only 855 emails in the corpus,
# we have over 22,000 terms that showed up at least once,
# which is clearly too many variables
# for the number of observations we have.
# So we want to remove the terms that
# don't appear too often in our data set,
# and we'll do that using the removeSparseTerms function.
# And we're going to have to determine the sparsity,
# so we'll say that we'll remove any term that doesn't appear
# in at least 3% of the documents.
# To do that, we'll pass 0.97 to removeSparseTerms.
# Remove sparse terms
dtm = removeSparseTerms(dtm, 0.97)
dtm
# Now we can take a look at the summary statistics
# for the document-term matrix, and we
# can see that we've decreased the number of terms
# to 788, which is a much more reasonable number.
# So let's build a data frame called labeledTerms out
# of this document-term matrix.
# So to do this, we'll use as.data.frame
# of as.matrix applied to dtm, the document-term matrix.
# So this data frame is only including right now
# the frequencies of the words that appeared in at least 3%
# of the documents,
# Create data frame
labeledTerms = as.data.frame(as.matrix(dtm))
# But in order to run our text analytics
# models, we're also going to have the outcome variable, which
# is whether or not each email was responsive.
# So we need to add in this outcome variable.
# So we'll create labeledTerms$responsive,
# and we'll simply copy over the responsive variable from
# the original emails data frame so it's equal
# to emails$responsive.
# Add in the outcome variable
labeledTerms$responsive = emails$responsive
# So finally let's take a look at our newly constructed data
# frame with the str function.
#
# So as we expect, there are an awful lot of variables, 789 in total.
# 788 of those variables are the frequencies
# of various words in the emails, and the last one is responsive,
# the outcome variable.
str(labeledTerms)
# At long last, we're ready to split our data into a training
# and testing set, and to actually build a model.
# So we'll start by loading the caTools package,
# so that we can split our data.
# So we'll do library(caTools).
# Split the data
library(caTools)
# And then, as usual, we're going to set our random seed so
# that everybody has the same results.
# So use set.seed and we'll pick the number 144.
# Again, the number isn't particularly important.
# The important thing is that we all use the same one.
# So as usual, we're going to obtain the split variable.
# We'll call it spl, using the sample.split function.
# The outcome variable that we pass is
# labeledTerms$responsive.
# And we'll do a 70/30 split.
# So we'll pass 0.7 here.
# So then train, the training data frame,
# can be obtained using subset on the labeled terms where
# spl is TRUE.
# And test is the subset when spl is FALSE.
set.seed(144)
spl = sample.split(labeledTerms$responsive, 0.7)
train = subset(labeledTerms, spl == TRUE)
test = subset(labeledTerms, spl == FALSE)
# So now we're ready to build the model.
# And we'll build a simple CART model
# using the default parameters.
# But a random forest would be another good choice
# from our toolset.
# So we'll start by loading up the packages for the CART model.
# We'll do library(rpart).
#
# And we'll also load up the rpart.plot package, so
# that we can plot the outcome.
# Build a CART model
library(rpart)
library(rpart.plot)
# So we'll create a model called emailCART,
# using the rpart function.
# We're predicting responsive.
# And we're predicting it using all
# of the additional variables.
# All the frequencies of the terms that are included.
# Obviously tilde period is important here,
# because there are 788 terms.
# Way too many to actually type out.
# The data that we're using to train the model
# is just our training data frame, train.
# And then the method is class, since we
# have a classification problem here.
emailCART = rpart(responsive~., data=train, method="class")
# And once we've trained the CART model,
# we can plot it out using prp.
prp(emailCART)
# So we can see at the very top is the word California.
# If California appears at least twice in an email,
# we're going to take the right part over here and predict
# that a document is responsive.
# It's somewhat unsurprising that California shows up,
# because we know that Enron had a heavy involvement
# in the California energy markets.
# So further down the tree, we see a number of other terms
# that we could plausibly expect to be related
# to energy bids and energy scheduling,
# like system, demand, bid, and gas.
# Down here at the bottom is Jeff, which is perhaps
# a reference to Enron's CEO, Jeff Skillings, who ended up
# actually being jailed for his involvement
# in the fraud at the company.
# Now that we've trained a model, we
# need to evaluate it on the test set.
# So let's build an object called pred
# that has the predicted probabilities
# for each class from our CART model.
# So we'll use predict of emailCART, our CART model,
# passing it newdata=test, to get test set predicted
# probabilities.
# Make predictions on the test set
pred = predict(emailCART, newdata=test)
# So to recall the structure of pred,
# we can look at the first 10 rows with pred[1:10,].
pred[1:10,]
# So this is the rows we want.
# We want all the columns.
# So we'll just leave a comma and nothing else afterward.
# So the left column here is the predicted probability
# of the document being non-responsive.
# And the right column is the predicted probability
# of the document being responsive.
# They sum to 1.
# So in our case, we want to extract
# the predicted probability of the document being responsive.
# So we're looking for the rightmost column.
# So we'll create an object called pred.prob.
# And we'll select the rightmost or second column.
# So pred.prob now contains our test set
# predicted probabilities.
pred.prob = pred[,2]
# And we're interested in the accuracy
# of our model on the test set.
# So for this computation, we'll use a cutoff of 0.5.
# And so we can just table the true outcome,
# which is test$responsive against the predicted outcome,
# which is pred.prob >= 0.5.
# What we can see here is that in 195 cases,
# we predict false when the left column and the true outcome
# was zero, non-responsive.
# So we were correct.
# And in another 25, we correctly identified a responsive
# document.
# In 20 cases, we identified a document as responsive,
# but it was actually non-responsive.
# And in 17, the opposite happened.
# We identified a document as non-responsive,
# but it actually was responsive.
# Compute accuracy
table(test$responsive, pred.prob >= 0.5)
(195+25)/(195+25+17+20)
# So we have an accuracy in the test set of 85.6%.
# And now we want to compare ourselves
# to the accuracy of the baseline model.
# As we've already established, the baseline model
# is always going to predict the document is non-responsive.
# So if we table test$responsive, we see that it's going to be
# correct in 215 of the cases.
# So then the accuracy is 215 divided
# by the total number of test set observations.
# So that's 83.7% accuracy.
# So we see just a small improvement
# in accuracy using the CART model, which, as we know,
# is a common case in unbalanced data sets.
# Baseline model accuracy
table(test$responsive)
215/(215+42)
# However, as in most document retrieval applications,
# there are uneven costs for different types of errors here.
# Typically, a human will still have to manually review
# all of the predicted responsive documents
# to make sure they are actually responsive.
# Therefore, if we have a false positive,
# in which a non-responsive document is labeled
# as responsive, the mistake translates
# to a bit of additional work in the manual review
# process but no further harm, since the manual review process
# will remove this erroneous result.
# But on the other hand, if we have a false negative,
# in which a responsive document is labeled as non-responsive
# by our model, we will miss the document entirely
# in our predictive coding process.
# Therefore, we're going to assign a higher cost to false negatives
# than to false positives, which makes this a good time to look
# at other cut-offs on our ROC curve.
# Now let's look at the ROC curve so we
# can understand the performance of our model
# at different cutoffs.
# We'll first need to load the ROCR package
# with a library(ROCR).
# ROC curve
library(ROCR)
# Next, we'll build our ROCR prediction object.
# So we'll call this object predROCR =
# prediction(pred.prob, test$responsive).
predROCR = prediction(pred.prob, test$responsive)
# So now we want to plot the ROC curve
# so we'll use the performance function to extract
# the true positive rate and false positive rate.
# So create something called perfROCR =
# performance(predROCR, "tpr", "fpr").
perfROCR = performance(predROCR, "tpr", "fpr")
# And then we'll plot(perfROCR, colorize=TRUE),
# so that we can see the colors for the different cutoff
# thresholds.
plot(perfROCR, colorize=TRUE)
# Now, of course, the best cutoff to select
# is entirely dependent on the costs assigned by the decision
# maker to false positives and true positives.
# However, again, we do favor cutoffs
# that give us a high sensitivity.
# We want to identify a large number of the responsive
# documents.
# So something that might look promising
# might be a point right around here,
# in this part of the curve, where we
# have a true positive rate of around 70%,
# meaning that we're getting about 70%
# of all the responsive documents, and a false positive rate
# of about 20%, meaning that we're making mistakes
# and accidentally identifying as responsive 20%
# of the non-responsive documents.
# Now, since, typically, the vast majority of documents
# are non-responsive, operating at this cutoff
# would result, perhaps, in a large decrease
# in the amount of manual effort needed
# in the eDiscovery process.
# And we can see from the blue color
# of the plot at this particular location
# that we're looking at a threshold around maybe 0.15
# or so, significantly lower than 50%, which is definitely
# what we would expect since we favor
# false positives to false negatives.
# So lastly, we can use the ROCR package
# to compute our AUC value.
# So, again, call the performance function
# with our prediction object, this time extracting the AUC value
# and just grabbing the y value slot of it.
# We can see that we have an AUC in the test set of 79.4%, which
# means that our model can differentiate
# between a randomly selected responsive and non-responsive
# document about 80% of the time.
# Compute AUC
performance(predROCR, "auc")@y.values
|
7bc86fdf36fda77a414ad5d8cf5f2a8e2d4b0476 | 9e758a1fd686a06c99eccf25e02bf736640531c7 | /man/fetch_google_analytics_4.Rd | ca2421f651953552d0090e01271986beb6cde86f | [] | no_license | addixvietnam/googleAnalyticsR_v0.4.2 | 2873c59bd23c76a06deaa036d676e6675c275869 | d60ff8f8c6f6748b1fc985b9b079ac6b4738f8b3 | refs/heads/master | 2020-08-26T12:54:05.693373 | 2019-10-23T09:28:49 | 2019-10-23T09:28:49 | 217,016,500 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,605 | rd | fetch_google_analytics_4.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ga_v4_get.R
\name{fetch_google_analytics_4}
\alias{fetch_google_analytics_4}
\title{Fetch multiple GAv4 requests}
\usage{
fetch_google_analytics_4(request_list, merge = FALSE)
}
\arguments{
\item{request_list}{A list of requests created by \link{make_ga_4_req}}
\item{merge}{If TRUE then will rbind that list of data.frames}
}
\value{
A dataframe if one request, or a list of data.frames if multiple.
}
\description{
Fetch the GAv4 requests as created by \link{make_ga_4_req}
}
\details{
For same viewId, daterange, segments, samplingLevel and cohortGroup, v4 batches can be made
}
\examples{
\dontrun{
library(googleAnalyticsR)
## authenticate,
## or use the RStudio Addin "Google API Auth" with analytics scopes set
ga_auth()
## get your accounts
account_list <- google_analytics_account_list()
## pick a profile with data to query
ga_id <- account_list[23,'viewId']
ga_req1 <- make_ga_4_req(ga_id,
date_range = c("2015-07-30","2015-10-01"),
dimensions=c('source','medium'),
metrics = c('sessions'))
ga_req2 <- make_ga_4_req(ga_id,
date_range = c("2015-07-30","2015-10-01"),
dimensions=c('source','medium'),
metrics = c('users'))
fetch_google_analytics_4(list(ga_req1, ga_req2))
}
}
\seealso{
Other GAv4 fetch functions: \code{\link{fetch_google_analytics_4_slow}},
\code{\link{google_analytics_4}},
\code{\link{make_ga_4_req}}
}
|
7c859ac50dc52f09af721099580c59203053e677 | 1d4c729a11381851e0b5c8578bf5cd7289fc082f | /man/xSimplifyNet.Rd | 9b35bc23066e15d2b939c70e203bf9fa2a2f83bf | [] | no_license | hfang-bristol/XGR | 95b484a0350e14ad59fa170ead902689a34be89a | 7b947080b310363e2b82c24c82d3394335906f54 | refs/heads/master | 2023-02-05T12:35:24.074365 | 2023-01-28T05:49:33 | 2023-01-28T05:49:33 | 52,982,296 | 9 | 3 | null | null | null | null | UTF-8 | R | false | true | 857 | rd | xSimplifyNet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xSimplifyNet.r
\name{xSimplifyNet}
\alias{xSimplifyNet}
\title{Function to simplify networks from an igraph object}
\usage{
xSimplifyNet(g, verbose = TRUE)
}
\arguments{
\item{g}{an "igraph" object}
\item{verbose}{logical to indicate whether the messages will be
displayed in the screen. By default, it sets to true for display}
}
\value{
an object of class "igraph"
}
\description{
\code{xSimplifyNet} is supposed to simplify networks from an igraph
object by keeping root-tip shortest paths only.
}
\note{
none
}
\examples{
\dontrun{
# Load the library
library(XGR)
}
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
\dontrun{
g <- xRDataLoader(RData.customised='ig.DO',
RData.location=RData.location)
ig <- xSimplifyNet(g)
}
}
\seealso{
\code{\link{xSimplifyNet}}
}
|
d9d9881e64be7764256e2e821c2bcb798685b871 | 60bb1cc368bfa719822a6d5c6d2d6c96770766cf | /data_cleaning_draft.R | eb8c657e1d5d32054b2fcbeceff2322912cbc68c | [] | no_license | markerenberg/TMDB-Box-Office-Prediction-Kaggle | db849ee8ed2736507058f8fb7920bcba6cf48230 | dc511ffd33e9415cfb478a3e4d95e1735f762355 | refs/heads/master | 2020-04-29T22:09:21.665861 | 2019-04-15T15:14:10 | 2019-04-15T15:14:10 | 176,436,916 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,535 | r | data_cleaning_draft.R | #-------------------------------
#This is a draft code generally separating some json columns
#Creating dummy variables based on common genere, production_companies etc.
#Creating new variables such as size of cast, size of crew etc.
#-------------------------------
#Modified on 31 MAR 2019
#new column: ratio of size of each department to total size of crew
#new column: ratio of male crew to (male+female)
#new column: ratio of male cast to (male+female)
library(data.table)
library(plotly)
library(ggplot2)
library(dplyr)
library(tidyr)
library(tidyverse)
library(stringi)
library(lubridate)
library(scales)
library(DT)
library(dplyr)
library(stringr)
library(jsonlite)
library(randomForest)
setwd("Desktop/ST4248/project")
train_raw <- read.csv("train.csv",header = TRUE,stringsAsFactors = F)
train_raw %>% glimpse()
##Cleaning of training data
train <- train_raw %>%
separate(belongs_to_collection, 'idPart', sep = 'name', remove = TRUE) %>% # Get the collection ID
separate(release_date, c('releaseMonth', 'releaseDay', 'releaseYear'), sep = '/', remove = TRUE) %>% # Separate the release_date
mutate(collectionID = ifelse(is.na(idPart) == FALSE, gsub("\\D", "", idPart), idPart), # Get digitis from collection
collectionID = ifelse(is.na(collectionID) == TRUE, 0, collectionID), # If collection value is NA the movie is not part of collection
mainSpokenLanguage = substr(spoken_languages,17,18), # This contains the ISO value for the first spoken language
mainSpokenLanguage = ifelse(is.na(mainSpokenLanguage), 'NA', mainSpokenLanguage),
spokenEn = ifelse(mainSpokenLanguage == 'en', TRUE, FALSE), # Hot vec for spoken language == en
partOfCollection = ifelse(is.na(idPart) == FALSE, TRUE, FALSE), # Hot vec for is in collection
hasHomePage = ifelse(is.na(homepage) == FALSE, TRUE, FALSE), # Hot vec for has homepage
hasTagline = ifelse(is.na(tagline) == FALSE, TRUE, FALSE), # Hot vec for has tagline
hasOverview = ifelse(is.na(overview) == FALSE, TRUE, FALSE), # Hot vec for has overview
genres = ifelse(is.na(genres) == TRUE, 'NoGen', genres), # Hot vecs for the different genres
genComedy = ifelse(stri_detect_fixed(genres, 'Comedy'),TRUE, FALSE),
genDrama = ifelse(stri_detect_fixed(genres, 'Drama'),TRUE, FALSE),
genThriller = ifelse(stri_detect_fixed(genres, 'Comedy'),TRUE, FALSE),
genAction = ifelse(stri_detect_fixed(genres, 'Action'),TRUE, FALSE),
genAnimation = ifelse(stri_detect_fixed(genres, 'Comedy'),TRUE, FALSE),
genHorror = ifelse(stri_detect_fixed(genres, 'Horror'),TRUE, FALSE),
genDocumentary = ifelse(stri_detect_fixed(genres, 'Documentary'),TRUE, FALSE),
genAdventure = ifelse(stri_detect_fixed(genres, 'Adventure'),TRUE, FALSE),
genCrime = ifelse(stri_detect_fixed(genres, 'Crime'),TRUE, FALSE),
genMystery = ifelse(stri_detect_fixed(genres, 'Mystery'),TRUE, FALSE),
genFantasy = ifelse(stri_detect_fixed(genres, 'Fantasy'),TRUE, FALSE),
genWar = ifelse(stri_detect_fixed(genres, 'War'),TRUE, FALSE),
genScienceFiction = ifelse(stri_detect_fixed(genres, 'Science Fiction'),TRUE, FALSE),
genRomance = ifelse(stri_detect_fixed(genres, 'Romance'),TRUE, FALSE),
genMusic = ifelse(stri_detect_fixed(genres, 'Music'),TRUE, FALSE),
genWestern = ifelse(stri_detect_fixed(genres, 'Western'),TRUE, FALSE),
genFamily = ifelse(stri_detect_fixed(genres, 'Family'),TRUE, FALSE),
genHistory = ifelse(stri_detect_fixed(genres, 'Comedy'),TRUE, FALSE),
genForeign = ifelse(stri_detect_fixed(genres, 'Foreign'),TRUE, FALSE),
genTVMovie = ifelse(stri_detect_fixed(genres, 'TV Movie'),TRUE, FALSE),
genNoGen = ifelse(genres == 'NoGen', TRUE, FALSE),
production_companies = ifelse(is.na(production_companies) == TRUE, 'NoProd', production_companies), # Hot vecs for the most popular production companies
prodUniversal = ifelse(stri_detect_fixed(production_companies, 'Universal Pictures'),TRUE, FALSE),
prodParamount = ifelse(stri_detect_fixed(production_companies, 'Paramount Pictures'),TRUE, FALSE),
prodTCF = ifelse(stri_detect_fixed(production_companies, 'Twentieth Century Fox Film Corporation'),TRUE, FALSE),
prodColumbia = ifelse(stri_detect_fixed(production_companies, 'Columbia Pictures'),TRUE, FALSE),
prodWarner = ifelse(stri_detect_fixed(production_companies, 'Warner Bros.'),TRUE, FALSE),
prodNLC = ifelse(stri_detect_fixed(production_companies, 'New Line Cinema'),TRUE, FALSE),
prodDisney = ifelse(stri_detect_fixed(production_companies, 'Walt Disney Pictures'),TRUE, FALSE),
prodColumbiaPictures = ifelse(stri_detect_fixed(production_companies, 'Columbia Pictures Corporation'),TRUE, FALSE),
prodTriStar = ifelse(stri_detect_fixed(production_companies, 'TriStar Pictures'),TRUE, FALSE),
prodMGM = ifelse(stri_detect_fixed(production_companies, 'Metro-Goldwyn-Mayer (MGM)'),TRUE, FALSE),
prodUnitedArtists = ifelse(stri_detect_fixed(production_companies, 'United Artists'),TRUE, FALSE),
prodMiramax = ifelse(stri_detect_fixed(production_companies, 'Miramax Films'),TRUE, FALSE),
prodTouchstone = ifelse(stri_detect_fixed(production_companies, 'Touchstone Pictures '),TRUE, FALSE),
prodFoxSearchlight = ifelse(stri_detect_fixed(production_companies, 'Fox Searchlight Pictures'),TRUE, FALSE),
releaseYear = ifelse(as.integer(releaseYear) <= 18, paste0('20', releaseYear), paste0('19', releaseYear)), # Year of relese
release_date = as.Date(paste(releaseYear, releaseMonth, releaseDay, sep = '-')),
age = as.integer(today() - release_date) / 365, # Age of movie in years
quarterRelease = quarter(release_date), # Relese quarter
weekRelease = week(release_date), # Relese week
dayRelease = wday(release_date), # Relese day of week
runtime = ifelse(is.na(runtime) == TRUE, 0, runtime), # If runtime is missing set it to zero, this will be fixed later
sizeOfCast = str_count(cast, 'cast_id'), # Size of cast
sizeOfCrew = str_count(crew, 'name'), # Size of crew
sizeOfDirecting=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Directing')/sizeOfCrew), #department size ratio
sizeOfWriting=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Writing')/sizeOfCrew),
sizeOfProduction=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Production')/sizeOfCrew),
sizeOfSound=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Sound')/sizeOfCrew),
sizeOfCamera=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Camera')/sizeOfCrew),
sizeOfEditing=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Editing')/sizeOfCrew),
sizeOfArt=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Art')/sizeOfCrew),
sizeOfCostumeMakeUp=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Costume MakeUp')/sizeOfCrew),
sizeOfLighting=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Lighting')/sizeOfCrew),
sizeOfVisualEffects=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Visual Effects')/sizeOfCrew),
sizeOfActors=ifelse(is.na(sizeOfCrew)==TRUE,0,str_count(crew,'Actors')/sizeOfCrew),
sizeOfCrew = ifelse(is.na(sizeOfCrew), 0, sizeOfCrew),
numberOfKeywords = str_count(Keywords, 'name'), # Get nmber of keywords by conting how many "name" instances there is
numberOfKeywords = ifelse(is.na(numberOfKeywords) == TRUE, 0, numberOfKeywords),
numberOfProductionCompanies = str_count(production_companies, 'name'), # Get nmber of production companies by conting how many "name" instances there is
numberOfProductionCompanies = ifelse(is.na(numberOfProductionCompanies) == TRUE, 0, numberOfProductionCompanies),
numberOfProductionCountries = str_count(production_countries, 'name'), # Get nmber of production countries by conting how many "name" instances there is
numberOfProductionCountries = ifelse(is.na(numberOfProductionCountries) == TRUE, 0, numberOfProductionCountries),
numberOfGenres = str_count(genres, 'name'), # Get nmber of genres by conting how many "name" instances there is
collectionID = as.factor(collectionID)) %>% # Make collectionID a factor
group_by(collectionID) %>%
mutate(sizeOfCollection = n()) %>%
ungroup() %>%
mutate(sizeOfCollection = ifelse(sizeOfCollection > 1000, 0, sizeOfCollection)) %>% # Most movies are not in a collection. Collection size for the biggest collection i set to zero
select(-idPart, -homepage, -imdb_id, -poster_path, -original_title, -genres, -overview, # Drop all unwanted columns
-tagline, -production_companies, -status, -spoken_languages, -production_countries, -releaseYear, -releaseMonth, -releaseDay,
-title, -collectionID, -mainSpokenLanguage)
train %>% glimpse()
df = train
n = nrow(df)
cast = unlist(as.list(df$cast))
# Creating a dataframe for each row, with 3 columns:
# C1: Name, C2: Gender, C3: Order
cast_lists = lapply(cast,function(x){unlist(as.list(strsplit(x,'},')[[1]]))})
cast_dfs = lapply(cast_lists,function(x){
as.data.frame(cbind(
# extract name
unlist(lapply(x,function(y){
substr(y,str_locate(y,'name')[,2]+5,str_locate(y,'order')-5)})),
# extract gender
unlist(lapply(x,function(y){
substr(y,str_locate(y,'gender')[,2]+4,str_locate(y,'gender')[,2]+4)})),
# extract order
unlist(lapply(x,function(y){
substr(y,str_locate(y,'order')[,2]+4,str_locate(y,'profile_path')-4)}))),
stringsAsFactors = F)
})
# Counting how many times a cast member appears in a movie:
cast_members = unique(unlist(lapply(cast_dfs,function(x){x[,1]})))
cast_tally = unlist(lapply(cast_members,function(actor){
sum(unlist(lapply(cast_dfs,function(df){
sum(rowSums(df == actor))
})
),na.rm=T)}
))
# Putting cast_member and cast_tally in a dataframe, sort by cast_tally DESC
cast = as.data.frame(cbind(cast_members,as.numeric(cast_tally)),stringsAsFactors = F)
names(cast) = c('cast_members','cast_tally')
cast$cast_tally = as.numeric(cast$cast_tally)
cast = cast[order(-cast_tally),]
# Bar Graph of Top Actors
ggplot(data=cast,aes(x=cast_members,y=cast_tally))+
geom_bar(data=cast,
aes(fill=cast_members),stat='identity',show.legend=F)+
ggtitle('Actor Count')+
theme(plot.title = element_text(hjust = 0.5))+
ylab('Count')+
xlab('Actor')
# Get list of top 300 actors (after removing empty strings)
if(length(which(cast$cast_members == '')) != 0){
cast = cast[-which(cast$cast_members == ''),]}
topactors = cast[1:301,1]
# Create a dataframe that contains dummy variables for every actor's
# apperance in a movie
actors_df = as.data.frame(seq(1:n))
for(actor in topactors){
old_names = names(actors_df)
actor_count = unlist(lapply(cast_dfs,function(df){
sum(rowSums(df == actor),na.rm=T)
}))
actors_df = cbind(actors_df,actor_count)
names(actors_df) = c(old_names,actor)
}
# Get Male to Female ratio
actors_df$actor_males = unlist(lapply(cast_dfs,function(df){sum(df$V2 == 2,na.rm=T)}))
actors_df$actor_females = unlist(lapply(cast_dfs,function(df){sum(df$V2 == 1,na.rm=T)}))
actors_df$actor_gender_ratio = ifelse(is.finite(actors_df$males/actors_df$females),
actors_df$males/actors_df$females, 0)
# M:F ratio in top 10 actors
actors_df$actor_top10males = unlist(lapply(cast_dfs,function(df){sum(df$V2[1:11] == 2,na.rm=T)}))
actors_df$actor_top10females = unlist(lapply(cast_dfs,function(df){sum(df$V2[1:11] == 1,na.rm=T)}))
actors_df$actor_top10gender_ratio = ifelse(
is.finite(actors_df$top10males/actors_df$top10females),
actors_df$top10males/actors_df$top10females,
0)
# Create a list for each movie, containing all keywords for that movie
keyw = df$Keywords
key_lists = lapply(keyw,function(x){unlist(as.list(strsplit(x,'},')[[1]]))})
key_df = lapply(key_lists,function(lst){
# extract names
unlist(lapply(lst,function(strng){
# if string in list is last string, remove the '}]"' characters
ifelse(match(strng,lst)==length(lst),
substr(strng,str_locate(strng,'name')[,2]+5,nchar(strng)-3),
substr(strng,str_locate(strng,'name')[,2]+5,nchar(strng)-1))
}
))
})
# To count how many times a keyword appears in a movie:
unique_keynames = unique(unlist(key_df))
keyword_tally = unlist(lapply(unique_keynames,function(key){
sum(unlist(lapply(key_df,function(lst){
sum(lst == key, na.rm=T)
})
),na.rm=T)}
))
# Create a dataframe containing each keyword and the keyword_tally
keywords = as.data.frame(cbind(unique_keynames,keyword_tally),
stringsAsFactors = F)
names(keywords) = c('keyword','keyword_tally')
keywords = keywords[order(-keyword_tally),]
# Get list of top 300 keywords (after removing empty strings)
if(length(which(keywords$keyword == '')) != 0){
keywords = keywords[-which(keywords$keyword == ''),]
}
topkeys = keywords[1:301,1]
# Create a dataframe that contains dummy variables for every keyword's
# apperance in a movie description
keyword_df = as.data.frame(seq(1:n))
for(key in topkeys){
old_names = names(keyword_df)
key_count = unlist(lapply(key_df,function(lst){
sum(lst == key,na.rm=T)
}))
keyword_df = cbind(keyword_df,key_count)
names(keyword_df) = c(old_names,key)
}
# Creating a dataframe for each row, with 3 columns:
# C1: Name, C2: Gender, C3: Department
crew = unlist(as.list(df$crew))
crew_lists = lapply(toString(crew),function(x){unlist(as.list(strsplit(x,'},')[[1]]))})
crew_dfs = lapply(crew_lists,function(x){
as.data.frame(cbind(
# extract name
unlist(lapply(x,function(y){
substr(y,str_locate(y,'name')[,2]+5,str_locate(y,'profile_path')-5)})),
# extract gender
unlist(lapply(x,function(y){
substr(y,str_locate(y,'gender')[,2]+4,str_locate(y,'gender')[,2]+4)})),
# extract department
unlist(lapply(x,function(y){
substr(y,str_locate(y,'department')[,2]+5,str_locate(y,'gender')-5)}))),
stringsAsFactors = F)
})
# Counting how many times a crew member appears in a movie:
crew_members = unique(unlist(lapply(crew_dfs,function(x){x[,1]})))
crew_tally = unlist(lapply(crew_members,function(crew_member){
sum(unlist(lapply(crew_dfs,function(df){
sum(rowSums(df == crew_member))
})
),na.rm=T)}
))
# Putting crew_member and crew_tally in a dataframe, sort by crew_tally DESC
crew = as.data.frame(cbind(crew_members,as.numeric(cast_tally)),stringsAsFactors = F)
colnames(crew) = c('crew_members','crew_tally')
crew$crew_tally = as.numeric(crew$crew_tally)
crew = crew[order(-crew$crew_tally),]
# Bar Graph of Top Crew Members
ggplot(data=crew,aes(x=crew_members,y=crew_tally))+
geom_bar(data=crew,
aes(fill=crew_members),stat='identity',show.legend=F)+
ggtitle('Crew Member Count')+
theme(plot.title = element_text(hjust = 0.5))+
ylab('Count')+
xlab('Crew Member')
# Get list of top 300 actors (after removing empty strings)
if(length(which(crew$crew_members == '')) != 0){
crew = crew[-which(crew$crew_members == ''),]}
topcrewmembers = crew[1:301,1]
# Create a dataframe that contains dummy variables for every crew's
# apperance in a movie
crews_df = as.data.frame(seq(1:n))
for(crews in topcrewmembers){
old_cnames = names(crews_df)
crew_count = unlist(lapply(crew_dfs,function(df){
sum(rowSums(df == crews),na.rm=T)
}))
crews_df = cbind(crews_df,crew_count)
names(crews_df) = c(old_cnames,crews)
}
# Get Male to Female ratio
crews_df$crew_males = unlist(lapply(crew_dfs,function(df){sum(df$V2 == 2,na.rm=T)}))
crews_df$crew_females = unlist(lapply(crew_dfs,function(df){sum(df$V2 == 1,na.rm=T)}))
crews_df$crew_gender_ratio = ifelse(is.finite(crews_df$males/crews_df$females),
crews_df$males/crews_df$females, 0)
# M:F ratio in top 10 crews
crews_df$top10males = unlist(lapply(crew_dfs,function(df){sum(df$V2[1:11] == 2,na.rm=T)}))
crews_df$top10females = unlist(lapply(crew_dfs,function(df){sum(df$V2[1:11] == 1,na.rm=T)}))
crews_df$top10gender_ratio = ifelse(
is.finite(crews_df$top10males/crews_df$top10females),
crews_df$top10males/crews_df$top10females,
0)
# Append Cast, Keyword, and Crew variables to train/test dataframe
cast_key_crew_vars = cbind(actors_df[,-1],keyword_df[,-1],crews_df[,-1])
train_df = cbind(df,cast_key_crew_vars)
|
db3d54649e06f358a6da844dbb1005392f958c8b | e67259f518e61f2b15dda1eb767f012a5f3a6958 | /tools/rpkg/dependencies.R | 5b9dd80112b27349daeb72f3b9aa636466e95cb5 | [
"MIT"
] | permissive | AdrianRiedl/duckdb | e0151d883d9ef2fa1b84296c57e9d5d11210e9e3 | 60c06c55973947c37fcf8feb357da802e39da3f1 | refs/heads/master | 2020-11-26T13:14:07.776404 | 2020-01-31T11:44:23 | 2020-01-31T11:44:23 | 229,081,391 | 2 | 0 | MIT | 2019-12-19T15:17:41 | 2019-12-19T15:17:40 | null | UTF-8 | R | false | false | 117 | r | dependencies.R | install.packages(c("DBI", "DBItest", "testthat", "dbplyr", "RSQLite", "callr"), repos=c("http://cran.rstudio.com/"))
|
a516db5cc2ebcd1899f919545e544f2c42bbdc2f | 3ff323d4cbd2c81e044024be225166c022ff6728 | /R/bindings.R | c542134909d17ab9f061f8652824495d2736231f | [] | no_license | dhh15/techhist | 88a50ff543f7a0805bcb00a7f4e80ce4d4c2150d | 7b105ea2b638cc49ba63e16c617e1519927ff729 | refs/heads/master | 2020-04-06T06:38:04.195335 | 2015-05-19T17:17:17 | 2015-05-19T17:17:17 | 35,090,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 700 | r | bindings.R | library(dplyr)
library(tau)
bin <- df %>% group_by(BindingId) %>% tally(sort=TRUE)
binn <- rapply(bin[1],c)
n_vol <- length(binn)
vol <- tbl_df(data.frame(list(row.index = 1:n_vol)))
vol$Year <- rep(NA,n_vol)
vol$Month <- rep(NA,n_vol)
vol$Day <- rep(NA,n_vol)
vol$Lang <- rep(NA,n_vol)
vol$Text <- rep(NA,n_vol)
for (i in 1:n_vol) {
id <- binn[i]
vol$BindingId[i] <- id
vol$Year[i] <- (df %>% filter(BindingId == id))$Year[1]
vol$Month[i] <- (df %>% filter(BindingId == id))$Month[1]
vol$Day[i] <- (df %>% filter(BindingId == id))$Day[1]
vol$Lang[i] <- (df %>% filter(BindingId == id))$Lang[1]
vol$Text[i] <- toString(rapply((df %>% filter(BindingId == id))$Text,c))
}
saveRDS(vol,"vol.Rds")
|
5ca9a8fe6385af2868a8f3c7ac53d2509ea96d59 | 1552c44b53a9a071532792cc611ce76d43795453 | /Inteligencia_Artificial/pso_I.r | b0d1434c8e38ab1ae880b59f6e3bee5c3f4b012f | [] | no_license | robl-25/Faculdade | 8ce16cee93f5948d33d45714de66578d189163f4 | 0801f5748d8d7d79314699b2e35258e402a55bd1 | refs/heads/master | 2021-01-10T05:16:45.842442 | 2017-11-09T02:26:26 | 2017-11-09T02:26:26 | 45,509,015 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,649 | r | pso_I.r | # Programa que implementa o PSO
rm()
#particulas
xmin = -10
xmax = 10
vmin = -1
vmax = 1
max_iter = 100
# Numero de particulas
n = 100
# Numero de variaveis de cada particula
qtd_var = 2
# Parametros da componente cognitiva e sociavel
c1 = c2 = 2.05
# Funcao objetivo
fobj = function(x){
sum(x^2)/sqrt(abs(max(x)))
}
# Particulas
x = matrix(runif(n*qtd_var, xmin, xmax), ncol = qtd_var)
#memoria
y = x
#velocidades
v = matrix(runif(n*qtd_var, vmin, vmax), ncol = qtd_var)
# Calcula o fx
fx = apply(x, 1, fobj)
# Melhor solucao encontrada (linha do melhor cara)
gbest = y[which.max(fx), ]
# Valor da melhor solucao
gbestValor = max(fx)
x11()
for(i in 1:max_iter){
cat("\nGBest = ", gbest, " e GBestVal = ", gbestValor)
plot(x, xlim = c(xmin, xmax), ylim = c(xmin, xmax), pch="*")
points(y, col = 2)
points(gbest[1], gbest[2], col=3, pch="x")
Sys.sleep(1)
# Numero gerado de uma distribuicao
r1 = runif(1)
r2 = runif(1)
# Formula da velocidade
v = v + r1*c1*(y-x) + c2*r2*t(gbest-t(x))
# Deixa a particula dentro dos limites
v[ v > vmax] = vmax
v[ v > vmin] = vmin
# Anda com a particula
x = x + v
# Volta aprticula para dentro do intervalo permitido
x[x<xmin] = xmin
x[x>xmax] = xmax
fx_novo = apply(x, 1, fobj)
# Procura as pos em que fx melhora (eh um vetor de true ou false)
pos = fx_novo > fx
# Ve se alguem melhorou
if(length(pos) > 0){
# Atualiza a memoria e o fx dos que melhoraram
y[pos, ] = x[pos, ]
fx[pos] = fx_novo[pos]
# Atualizando o gbest e o maior valor
gbest = y[which.max(fx), ]
gbestValor = max(fx)
}
} |
f7214f4587c8ead6ca6a20f4e389e8879fa23e71 | 14c2f47364f72cec737aed9a6294d2e6954ecb3e | /man/assertEdgeToptable.Rd | 610d9e5ef2f97b8449f013e61306beed6cd3eac6 | [] | no_license | bedapub/ribiosNGS | ae7bac0e30eb0662c511cfe791e6d10b167969b0 | a6e1b12a91068f4774a125c539ea2d5ae04b6d7d | refs/heads/master | 2023-08-31T08:22:17.503110 | 2023-08-29T15:26:02 | 2023-08-29T15:26:02 | 253,536,346 | 2 | 3 | null | 2022-04-11T09:36:23 | 2020-04-06T15:18:41 | R | UTF-8 | R | false | true | 370 | rd | assertEdgeToptable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/edgeR-funcs.R
\name{assertEdgeToptable}
\alias{assertEdgeToptable}
\title{Assert that the input data.frame is a valid EdgeTopTable}
\usage{
assertEdgeToptable(x)
}
\arguments{
\item{x}{A data.frame}
}
\value{
Logical
}
\description{
Assert that the input data.frame is a valid EdgeTopTable
}
|
710d161472e97a1bda8a1ee13c69ce2e83911548 | 44d17a26766da7ad02445b214fb26fa06c73e5a3 | /R_programming/practice_assignment.R | 5f52598ae36dfbb3d65da2531e1ea344d7ad486f | [] | no_license | elbacilon/datascience | 9adc5dfa17e444ba52c14905eda8960b559e5f0b | 74e50c7f2e62277e156a684685b2d82f52c77002 | refs/heads/master | 2020-05-04T16:08:51.364962 | 2015-04-22T19:40:13 | 2015-04-22T19:40:13 | 30,263,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,174 | r | practice_assignment.R | # Download Data
setwd("C:/Users/Cedric/Documents/GitHub/datascience/R_programming/")
dataset_url <- "http://s3.amazonaws.com/practice_assignment/diet_data.zip"
download.file(dataset_url, "diet_data.zip")
# Explore data
unzip("diet_data.zip", exdir = "diet_data") # unzip in a new directory
list.files("diet_data") # list files of the directory diet_data => 5 files
## explore 1 file
andy <- read.csv("diet_data/Andy.csv")
head(andy) # 4 columns
# how many rows
length(andy$Day) # 30 observations
# OR
# dim(andy)
# str(andy)
# summary(andy)
# names(andy)
# all of the other files match this format and length.
# 30 days worth of weight data for 5 subjects of an imaginary diet study.
# Andy's starting weight?
# subset the data => first row of the 'Weight' column:
andy[1, "Weight"] # [1] 140
# create a subset of the 'Weight' column where the value of the 'Day' column is equal to 30
andy[which(andy$Day == 30), "Weight"]
# OR: andy[which(andy[,"Day"] == 30), "Weight"]
# OR: subset(andy$Weight, andy$Day==30)
# assign Andy's starting and ending weight to vectors:
andy_start <- andy[1, "Weight"]
andy_end <- andy[30, "Weight"]
# find out how much weight he lost by subtracting the vectors:
andy_loss <- andy_start - andy_end
andy_loss # Andy lost 5 pounds over the 30 days
# look at everybody at once
# list.files() command. It returns the contents of a directory in alphabetical order.
# need full.names = TRUE to include "diet_data" else read funcrtion we'll look in working directory
files_full <- list.files("diet_data", full.names=TRUE)
dat <- data.frame()
for (i in 1:5) {
dat <- rbind(dat, read.csv(files_full[i]))
}
str(dat)
# median(dat$Weight) # return NA
# sum(is.na(dat)) # there are 13 NA
median(dat$Weight, na.rm=TRUE)
# median weight of day 30 by taking the median of a subset of the data where Day=30.
median(dat[dat$Day == 30, "Weight"], na.rm = TRUE)
# build a function that will return the median weight of a given day.
# argument should be directory and day for which they want to calculate the median.
weightmedian <- function(directory, day) {
files_full <- list.files(directory, full.names=TRUE) # create list of files
dat <- data.frame() # create empty data.frame
for (i in 1:5) {
#loops through the files, rbinding them together
dat <- rbind(dat, read.csv(files_full[i]))
}
dat_subset <- dat[which(dat[, "Day"] == day),] #subsets the rows that match the 'day' argument
median(dat_subset[, "Weight"], na.rm=TRUE) #identifies the median weight while stripping out the NAs
}
weightmedian(directory = "diet_data", day = 20)
weightmedian("diet_data", 4)
weightmedian("diet_data", 17)
##################################### ALTERNATIVE ##################################
# loop by copying and recopying it. It works, but it's slow and if you've got a lot of data,
# The better approach is to create an output object of an appropriate size and then fill it up.
# create an empty list that's the length of our expected output.
# In this case, our input object is going to be files_full and our empty list is going to be tmp.
files_full <- list.files(directory, full.names=TRUE) # create list of files
tmp <- vector(mode = "list", length = length(files_full))
summary(tmp)
# read in those csv files and drop them into tmp.
for (i in seq_along(files_full)) {
tmp[[i]] <- read.csv(files_full[[i]])
}
str(tmp)
# we have a list of 5 elements called tmp
# each element of the list is a data frame containing one of the csv files.
# It just so happens that what we just did is functionally identical to using lapply.
# str(lapply(files_full, read.csv))
# need to go from a list to a single data frame
# str(tmp[[1]])
# head(tmp[[1]][,"Day"])
# We can use a function called do.call() to combine tmp into a single data frame.
# do.call lets you specify a function and then passes a list as if each element of the list were an argument to the function.
output <- do.call(rbind, tmp)
str(output)
|
e8e98b4a3953814e93ee989917ebd36c49c0b359 | a1fb1bf5ffb6ca117bbdff2b47f8a6a84fa8129f | /scripts/dada2/06_dbOTU_into.R | f0dc3969d3b0b153156f335d5165086eca9c2a12 | [] | no_license | lvelosuarez/Snakemake_amplicon | 5e439121595cf4d6b1508fa4ba7ce1a8f07b6372 | 9a8d36332d50e41d7b2fc6d2fd82b2cec805d06f | refs/heads/master | 2023-08-25T07:39:46.037942 | 2021-10-28T14:07:30 | 2021-10-28T14:07:30 | 337,670,914 | 0 | 1 | null | 2021-02-16T10:07:49 | 2021-02-10T09:11:26 | R | UTF-8 | R | false | false | 840 | r | 06_dbOTU_into.R | #!/usr/bin/Rscript
suppressPackageStartupMessages(library(tidyverse))
sink(snakemake@log[[1]])
seqtab= readRDS(snakemake@input[['seqtab']])
dbOTU <- seqtab %>% t() %>% as.data.frame(stringsAsFactors = FALSE) %>% rownames_to_column(var="seqs") %>% mutate(asv_id=paste0("asv", 1:nrow(.))) %>% dplyr::select(asv_id,seqs,everything())
table <- read_tsv(snakemake@input[['dbOTU']], col_names=TRUE)
dada2 <- data.frame(OTU_ID=dbOTU$asv_id,seq=dbOTU$seqs, stringsAsFactors= FALSE) %>%
right_join(table, by="OTU_ID") %>%
as.data.frame() %>%
dplyr::select(-OTU_ID) %>%
column_to_rownames(var = "seq") %>%
t() %>%
as.matrix()
saveRDS(dada2, snakemake@output[['rds']]) |
d162025b2d91f4348f064046188a71315c527e46 | 7e2811cb7005bba30a4ff64fa8c93e8f41bcbb72 | /filterVCF.Rcheck/00_pkg_src/filterVCF/R/filterVCF_functions.R | c11f8560aca65ed58608d5d8f051e9c6fff6b981 | [] | no_license | benjaminlaenen/filterVCF | 38a2c242240519f72b0b7c55dfc2248099a17512 | 79ed3f783c84de85c14c5afd8ab390d97ba3f51c | refs/heads/master | 2022-12-14T17:43:08.854767 | 2020-09-06T19:45:52 | 2020-09-06T19:45:52 | 293,107,005 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,114 | r | filterVCF_functions.R | # ==========================================================================
# Main functions
# ==========================================================================
#' Extract the META info from a vcf
#'
#' Extract the META info from a vcfR object and can report plot.
#'
#'
#' @param vcf a vcfR object
#' @param vcf_file names fro plotting if opt$plot = TRUE
#' @param ... options from initialise_option()
#' @return list
#' \item{META }{META data extracted}
#' \item{Description_META }{description taken from the vcf header}
#' \item{INFO_per_snp }{META data per SNPs}
#' \item{CI_Stats }{Statistic for each META data}
#'
#' @author ~~Benjamin Laenen~~
#' @references
#' @keywords ~utilities
#' @examples
#'
#' opt <- parse_args(OptionParser(option_list=option_list))
#' opt$plot <- TRUE
#' stats_META("my.vcf", vcf_file="Plot_metadata", opt)
#'
#' @export
stats_META <- function(vcf, vcf_file="vcf", ...){
myDots <- list(...)
if (!is.null(myDots$opt)){
opt <- myDots$opt
}else{
opt <- parse_args(OptionParser(option_list=initialise_option()))
}
if(nrow(vcf) == 0){
message("\nNo site left after filtering!!")
return(list())
}
#Export some stats adapted for GATK,
#it can also output from other caller
#but not tested.
META <- grep("INFO", queryMETA(vcf), value = TRUE)
META <- gsub(".+=(.+)$", "\\1", META)
META <- grep("AC|AF|AN", META, value = TRUE, invert =TRUE)
Description_META <- lapply(META, function(x) paste(x, grep("Description", queryMETA(vcf, element = x)[[1]], value = TRUE), sep = ":"))
INFO_per_snp <- lapply(META, function(x) extract.info(vcf, element = x, as.numeric = TRUE))
names(INFO_per_snp) <- names(Description_META) <- META
mat_INFO_per_snp <- as.data.frame(matrix(unlist(INFO_per_snp), ncol=length(INFO_per_snp)))
index_col <- colSums(mat_INFO_per_snp, na.rm=TRUE) != 0
mat_INFO_per_snp <- mat_INFO_per_snp[,index_col]
mat_INFO_per_snp <- mat_INFO_per_snp[complete.cases(mat_INFO_per_snp),]
colnames(mat_INFO_per_snp) <- META[index_col]
if(opt$verbose) message(sprintf("Computing stats for INFO :\n%s", paste(META[index_col], collapse = " ")), appendLF=TRUE)
CI_Stats <- apply(mat_INFO_per_snp,2, quantile, probs = c(0.025, 0.5, 0.975))
if(opt$plot){
pdf(paste0(gsub(".vcf|.vcf.gz", "",vcf_file), "_", "filter_stat_overview.pdf"))
#pdf()
par(mfrow=c(2, 2), cex.main = 0.5)
Hist_out <- suppressWarnings(invisible(lapply(META, function(x) try(hist(INFO_per_snp[[x]], 20, xlab = x, main=Description_META[[x]], col = "aquamarine2"),silent=TRUE))))
pc <- try(prcomp(scale(mat_INFO_per_snp)))
par(mfrow=c(1, 1), cex.main = 0.6)
try(biplot(pc, choices =1:2, cex=.6, pc.biplot = TRUE))
try(biplot(pc, choices =3:4, cex=.6, pc.biplot = TRUE))
graphics.off()
}
message("Done...", appendLF=TRUE)
return(list(META=META, Description_META=Description_META, INFO_per_snp=INFO_per_snp, CI_Stats=CI_Stats))
}
#' Save large output from a filterVCF
#'
#' This function is used in the pipeline filterVCF.R to save output from a run
#' of filtering. The filterVCF object contains information about the sites that
#' were filtered, the invairant, the fixed, the bedfile imputed,etc. This
#' function creates a directory with the resulting filtered vcf file, invariant
#' and fixed bedfile and all the filterts that have been applied as bedfiles.
#'
#'
#' @param RES a filterVCF object
#' @param output_dir output directory
#' @param bed_outputdir output directory to save filters
#' @return NULL
#'
#' @author ~~Benjamin Laenen~~
#' @seealso objects to See Also as \code{\link{help}},
#' @references
#' @keywords ~utilities
#' @examples
#'
#' opt <- GetOpt(filterVCF_object)
#' save_output(filterVCF_object, output_dir = "./", bed_outputdir = "filters", opt)
#'
#' @export
save_output <- function(RES, output_dir = "./", bed_outputdir, ...){
myDots <- list(...)
if (!is.null(myDots$opt)){
opt <- myDots$opt
}else{
opt <- parse_args(OptionParser(option_list=initialise_option()))
}
if(!is.na(opt$output_file)){
vcf_file_short <- paste0(basename(gsub(".vcf$|.vcf.gz$", "", opt$output_file)))
}else{
vcf_file_short <- paste0(basename(gsub(".vcf$|.vcf.gz$", "", opt$vcf_file)))
}
bed_window_filter_dir <- paste0(bed_outputdir, "/window_filters")
if(!dir.exists(bed_window_filter_dir)){
dir.create(bed_window_filter_dir)
}
if(!is.na(opt$bed_file[1])){
Grange2bed(BEDFilter(RES), "Filter by bed file", bed_window_filter_dir, paste0(vcf_file_short, "_filtered_by_", "bed_GRange_merged", ".bed"))
}
if(isTRUE(opt$filter_repeats_by_windows)){
Grange2bed(Filter_WindowsProp(RES), "windows with half repeats (or other bedfile specified in --repeats)", bed_window_filter_dir, paste0(vcf_file_short, "_filtered_by_", "windows_with_half_repeats_to_remove", ".bed"))
}
if(isTRUE(opt$filter_fix_het)){
Grange2bed(FixHet_RefPop(RES), "Fixed contiguous heterozygous sites in 50bp windows in the population specified in --filter_fix_het_contiguous_in_pop", bed_window_filter_dir, paste0(vcf_file_short, "_filtered_by_", "fix_het_pop", ".bed"))
}
if(isTRUE(!is.na(opt$filter_high_DP_standardized))){
names(Normalized_DP_Filter(RES)[[1]]) <- names(Normalized_DP_Filter(RES)[[2]]) <- paste0("median_normDP_", parse_filter_high_DP_standardized(opt$filter_high_DP_standardized)$threshold)
for(x in names(Normalized_DP_Filter(RES))){
for(y in names(Normalized_DP_Filter(RES)[[x]])){
Grange2bed(Normalized_DP_Filter(RES)[[x]][[y]], sprintf("%s with normalized depth higher than the threshold %s", x, y), bed_window_filter_dir, paste0(vcf_file_short, "_filtered_by_", "standardized_DP_", y, ifelse(x=="total_high_DP_windows", "_per_sample", "_across_sample"), ".bed") )
}
}
}
#Save bed file for sites removed by diffrent filters to intersect with vcf_bed_Grange
bed_single_filter_dir <- paste0(bed_outputdir, "/individual_filters")
filter2save <- c(
"QD",
"SOR",
"MQRankSum",
"FS",
"MQ",
"ReadPosRankSum",
"InbreedingCoeff",
"fix_het",
"all_het",
"bi_allelic",
"missing",
"indel")
vcf_bed_GRange <- vcf2Grange(vcfRaw(RES))
if(!dir.exists(bed_single_filter_dir)){
dir.create(bed_single_filter_dir)
}
void <- lapply(filter2save, function(x) filter2bed(Filters(RES)[[x]], x, vcf_bed_GRange, bed_single_filter_dir, paste0(vcf_file_short, "_filtered_by_", x, ".bed")))
#save removed sites
master_filter_Grange <- removed_sites(RES)
if(sum(width(invariantRaw(RES))) != 0){
master_filter_Grange <- c(master_filter_Grange, removed_sites_inv(RES))
}
Grange2bed(master_filter_Grange, "Removed sites", bed_outputdir, paste0(vcf_file_short, "_removed_sites", ".bed"))
#save fixed invariant
if(isTRUE(length(fixed(RES)) != 0) ){
Grange2bed(fixed(RES), "Fixed homozygous ALT sites , keep that filter if a repolarization is done later", bed_outputdir, paste0(vcf_file_short, "_ALT_fixed", ".bed"))
}
#save invariant
if(isTRUE(length(invariant(RES)) != 0)){
Grange2bed(invariant(RES), "Invariable sites including REF/REF and missing", bed_outputdir, paste0(vcf_file_short, "_invariable", ".bed"))
}
list_bed_files <- list.files(path=outputdir, pattern=".bed$", recursive = TRUE, full.names = TRUE)
void <- lapply(list_bed_files, gzip, overwrite=TRUE)
#gzip all bed file
#write vcf file
if(opt$verbose) message(sprintf("Save filtered vcf to %s: ", paste0(vcf_file_short, "_filtered", ".vcf.gz")))
write.vcf(vcf(RES), file =paste0(vcf_file_short, "_filtered", ".vcf.gz"))
}
#' Main wrapper to filter a VCF file
#'
#' This function is the main core of the pipeline filterVCF.R. It will perform
#' a series of filtering steps depending of the options provided in a opt list
#' that can be initiated with initialise_option(). This function is not expected
#' to be used directly by the user which should first run the pipeline
#' filterVCF.R on a complete non filtered VCF (including invariant).
#'
#'
#' see the help of filterVCF.R for more detail
#' Rscript filterVCF.R --help
#'
#'
#' @param vcf_file path to vcf file
#' @param ... options from initialise_option(). This parameter regulates all the filtering steps that will be applied to the data.
#' @return a filterVCF object.
#'
#' @author ~~Benjamin Laenen~~
#' @references
#' @keywords ~main
#' @examples
#'
#' opt <- parse_args(OptionParser(option_list=option_list))
#' main_filter_VCF("my.vcf", opt)
#'
#' @export
main_filter_VCF <- function(vcf_file, ...){
.libPaths("/proj/uppstore2018024/private/Rpackages/")
suppressPackageStartupMessages(suppressMessages(try(library(filterVCF))))
myDots <- list(...)
if (!is.null(myDots$opt)){
opt <- myDots$opt
}else{
opt <- parse_args(OptionParser(option_list=initialise_option()))
}
vcf <- read.vcfR(vcf_file, limit = 1e+08, verbose = opt$verbose, convertNA = FALSE)
vcf@fix[,"ID"] <- NA
if(!is.na(opt$keep_sites)){
sites2keep <- bed2Grange(opt$keep_sites)
vcf_bed_GRange <- vcf2Grange(vcf)
index_sites2keep <- findOverlaps(vcf_bed_GRange, sites2keep, minoverlap=1, ignore.strand=TRUE)
vcf <- vcf[from(index_sites2keep)]
if(opt$verbose) message(sprintf("Keeping only sites interesecting with %s\nResults in %s sites before filtering", opt$keep_sites, nrow(vcf)), appendLF=TRUE)
}
#select only some chromosome
#return a empty RES if the chr i not present in the vcf
if(!is.na(opt$chr[1])){
vcf <- vcf[getCHROM(vcf) %in% opt$chr]
if(nrow(vcf) == 0 ){
return(as.filterVCF(list(
vcf=vcf,
vcf_filtered=vcf,
vcf_inv=GRanges(),
vcf_inv_filtered=GRanges(),
fixed_inv_Grange=GRanges(),
reference=new("DNAStringSet"),
master_filter=logical(0),
master_filter_inv=logical(0),
filters=list(logical(0)),
filters_inv=list(logical(0)),
removed_sites=GRanges(),
removed_sites_inv=GRanges(),
bed_GRange_merged=GRanges(),
windows_with_half_repeats_to_remove=GRanges(),
fix_het_pop=GRanges(),
filter_DP_repeats=list(high_DP_windows_means_across_sample=GRanges(), total_high_DP_windows=GRanges()),
stats_vcf_filtered=NA_character_,
opt = opt)))
}
}
if(!is.na(opt$sample)){
opt$sample_list <- parse_opt_sample(opt$sample)
vcf <- vcf[,c("FORMAT", opt$sample_list)]
warnings("\nInformation in the INFO field will not be recalculated, use GATK -T SelectVariants on the output vcf to recreate them.\n")
}
# extract_genotype
gt <- extract_gt_ff(vcf)
#remove sample with too much missing data
if(!is.na(opt$missing_per_individual)){
passing_missing_ind <- missing_per_individual(gt, opt$missing_per_individual)
vcf <- vcf[,c("FORMAT", passing_missing_ind)]
gt <- gt[passing_missing_ind]
}
#Start by filtering the invariant out and keep the vcf for further saving
filter_invariant <- create_invariant_filter(vcf, gt=gt, opt)
gc(verbose=FALSE)
vcf_inv <- vcf[filter_invariant]
vcf <- vcf[!filter_invariant]
if(opt$verbose) message("Done...", appendLF=TRUE)
#explore the filtering option in the metadata and plot some reports. If the vcf is too big start by subsampling 100000
if(!is.na(opt$split) & isTRUE(opt$split != 1)){
#dont calculate stats on splitted file
#it will be done later
# note that is a subsample is taken the info will not match for the sample
# it is better to use SelectVariants first
if(nrow(vcf) > 1e5){
if(opt$verbose) message(sprintf("The VCF has %s variants, taking a subsample of 100000 to draw stats plot", nrow(vcf)), appendLF=TRUE)
vcf_for_stats <- vcf[sample(1:nrow(vcf), 1e5),]
}else{
vcf_for_stats <- vcf
}
#get some stats
stats_vcf <- suppressWarnings(stats_META(vcf_for_stats, vcf_file=vcf_file, opt))
}
# extract_genotype
gt <- gt[as.ff(!filter_invariant),]
#filter DP per individual, if used filter missing should be recalculated
#as we will replace the genotype by NA is DP is < threshold > .
# vcf_bak <- vcf
# vcf <- vcf_bak
if(!is.na(opt$filter_depth)){
opt$filter_depth_parsed <- parse_filter_depth(opt$filter_depth)
vcf <- change_gt_by_DP_filter(vcf, min_DP=opt$filter_depth_parsed$min_DP, max_DP=opt$filter_depth_parsed$max_DP, opt)
#We need to re extract the genotype
rm(gt)
gt <- extract_gt_ff(vcf)
#After changing the genotyoe to NA some sites can become invariant
#create a new filter for invariant and
#adding the new inv to the old while filtering the snp
#it works even if no sites is filtered
filter_invariant_dp <- create_invariant_filter(vcf, gt=gt, opt)
vcf_inv <- rbind2(vcf_inv, vcf[filter_invariant_dp])
vcf <- vcf[!filter_invariant_dp]
gt <- gt[as.ff(!filter_invariant_dp),]
#re add the invariant
}
if(!is.na(opt$filter_genotype_quality)){
opt$filter_genotype_quality <- parse_filter_GQ(opt$filter_genotype_quality)
vcf <- change_gt_by_GQ_filter(vcf, invariable = FALSE, GQ_threshold=opt$filter_genotype_quality$GQ, opt)
#We need to re extract the genotype
rm(gt)
gt <- extract_gt_ff(vcf)
#After changing the genotyoe to NA some sites can become invariant
#create a new filter for invariant and
#adding the new inv to the old while filtering the snp
#it works even if no sites is filtered
filter_invariant_dp <- create_invariant_filter(vcf, gt=gt, opt)
vcf_inv <- rbind2(vcf_inv, vcf[filter_invariant_dp])
vcf <- vcf[!filter_invariant_dp]
gt <- gt[as.ff(!filter_invariant_dp),]
#re add the invariant
}
#save the inv to temp file to retrieve after to sve memory usage
tmp_vcf_inv <- tempfile()
saveRDS(vcf_inv, file = tmp_vcf_inv)
rm(vcf_inv)
filters <- list()
# Filter the variant
# GATK recommendation modified after exploring the data. It is a bit more stringent
# and try to remove excess of hets due to wrong call
# "QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" \
# "QD < 5.0 || FS > 60.0 || MQ < 50.0 || MQRankSum < -5 || ReadPosRankSum < -4.0 || SOR > 3 || InbreedingCoeff < -0.2" \
if(!is.null(opt$filter_GATK_info)){
filter_GATK_info <- parse_GATK_filter_option(opt$filter_GATK_info)
for(i in names(filter_GATK_info)){
filters[[i]] <- create_filter_META(vcf, i, filter_GATK_info[[i]])
}
}
# filter for fix het
if(opt$filter_fix_het){
filters[["fix_het"]] <- create_filter_fix_het(gt, opt)
}
# filter for fix het
if(opt$filter_all_het){
filters[["all_het"]] <- create_filter_all_het(gt, opt)
}
#filter for bi allelic
if(opt$biallelic){
if(opt$verbose) message("Apply filter to keep bi-allelic sites", appendLF=TRUE)
filters[["bi_allelic"]] <- !is.biallelic(vcf)
}
#filter for missing
if(!is.na(opt$missing)){
filters[["missing"]] <- create_filter_missing(gt, opt$missing)
}
# filter snps only remove indels
if(opt$filter_indel){
filters[["indel"]] <- create_indel_filter(vcf, opt)
}
# Reading all bedfiles into Grange object
if(!is.na(opt$bed_file[1])){
if(opt$verbose) message("Apply filter from bedfiles", appendLF=TRUE)
bed_GRange <- lapply(opt$bed_file, bed2Grange)
# Merging Grange object into one to intersect with the vcf
bed_GRange_merged <- suppressWarnings(do.call("c", bed_GRange))
# Converting vcf to Grange
vcf_bed_GRange <- vcf2Grange(vcf)
#finding the overlap between the vcf and the bed file (bedtools subtract)
filters[["filter_bed"]] <- !is.na(GenomicRanges::findOverlaps(vcf_bed_GRange, bed_GRange_merged , select ="first", ignore.strand=TRUE))
}else{
bed_GRange_merged <- NULL
}
if(isTRUE(opt$filter_repeats_by_windows)){
#repeats windows with more than 50%
windows_with_half_repeats_to_remove <- suppressWarnings(create_filter_repeats_in_windows(opt$reference, opt$repeats, vcf, 20000, 0.5))
filters[["filter_repeats_by_windows"]] <- !is.na(GenomicRanges::findOverlaps(vcf_bed_GRange, windows_with_half_repeats_to_remove , select ="first", ignore.strand=TRUE))
}else{
windows_with_half_repeats_to_remove <- NULL
}
if(isTRUE(!is.na(opt$filter_high_DP_standardized))){
#custom filter on DP per windows
opt$filter_high_DP_standardized <- parse_filter_high_DP_standardized(opt$filter_high_DP_standardized)
filter_DP_repeats <- create_filter_high_DP_standardized(opt$reference, vcf, threshold = opt$filter_high_DP_standardized$threshold, threshold_CI = opt$filter_high_DP_standardized$threshold_CI, windows_size= opt$filter_high_DP_standardized$windows_size, overlapping= opt$filter_high_DP_standardized$slidding, percent_passing_filter = opt$filter_high_DP_standardized$percent_passing_filter, opt)
gc()
filters[["filter_DP_repeats"]] <- !is.na(GenomicRanges::findOverlaps(vcf_bed_GRange, filter_DP_repeats[[2]][[1]] , select ="first", ignore.strand=TRUE))
}else{
filter_DP_repeats <- NULL
}
if(!is.na(opt$filter_fix_het_contiguous_in_pop)){
#filter cluster of fix het in the swedish pop
fix_het_pop <- create_filter_fix_het_contiguous_in_pop(gt, vcf, opt$filter_fix_het_contiguous_in_pop, windows_size = 50, max_nb_contigous_hets = 1)
vcf_bed_GRange <- vcf2Grange(vcf)
filters[["filter_pop_het_contiguous"]] <- !is.na(GenomicRanges::findOverlaps(vcf_bed_GRange, fix_het_pop , select ="first", ignore.strand=TRUE))
}else{
fix_het_pop <- NULL
}
# Sum up all filters to create a master filter
master_filter <- Reduce("+", filters) > 0
if(length(master_filter) == 0) master_filter <- rep(FALSE, nrow(vcf))
# #information on the number of snps remove by each filters
# snp_remove_by_filter <- lapply(filters, sum, na.rm = TRUE)
#write a report of the snps that were filtered
#Remove the fixed sites in the vcf and it to the invariant vcf.
#Save a bedfile with the fixed sites
filters[["inv_fix_sites"]] <- create_filter_fixed_sites(gt)
filters[["inv_fix_sites"]] <- !master_filter & filters[["inv_fix_sites"]]
fixed_inv_Grange <- vcf2Grange(vcf[filters[["inv_fix_sites"]],])
rm(gt)
gc()
fix_vcf <- vcf[filters[["inv_fix_sites"]],]
#join the invariant with the fixed in a vcf
vcf_inv <- rbind2(readRDS(tmp_vcf_inv), fix_vcf)
#filter the vcf file with all the filter at once.
#single filter will be outputed to a folder
#in order to create removed sites bedfile or
#refiltered the original vcf using only some
#filters with bedtools subtract.
vcf_filtered <- vcf[!master_filter & !filters[["inv_fix_sites"]],]
stats_vcf_filtered <- stats_META(vcf_filtered, vcf_file=paste0(vcf_file, "_filtered"), opt)
# ==========================================================================
# apply some filters to vcf_inv
# ==========================================================================
if(!is.na(opt$filter_depth)){
vcf_inv <- change_gt_by_DP_filter(vcf_inv, min_DP=opt$filter_depth_parsed$min_DP, max_DP=opt$filter_depth_parsed$max_DP, opt)
}
#filter on Ref Genotype Quality
if(!is.na(opt$filter_genotype_quality)){
vcf_inv <- change_gt_by_GQ_filter(vcf_inv, invariable = TRUE, GQ_threshold=opt$filter_genotype_quality$RGQ, opt)
}
filters_inv <- list()
#filter for bi allelic
if(opt$biallelic){
filters_inv[["bi_allelic"]] <- !is.biallelic(vcf_inv)
}
#filter for missing
if(!is.na(opt$missing)){
filters_inv[["missing"]] <- create_filter_missing(vcf_inv, opt$missing)
}
# filter snps only remove indels
if(opt$filter_indel){
filters_inv[["indel"]] <- create_indel_filter(vcf_inv, invariable=TRUE)
}
# Converting vcf to Grange
vcf_inv_bed_GRange <- vcf2Grange(vcf_inv)
#finding the overlap between the vcf and the bed file (bedtools subtract)
if(!is.na(opt$bed_file[1])){
#finding the overlap between the vcf and the bed file (bedtools subtract)
filters_inv[["filter_bed"]] <- !is.na(GenomicRanges::findOverlaps(vcf_inv_bed_GRange, bed_GRange_merged , select ="first", ignore.strand=TRUE))
}
#repeats windows with more than 50%
if(opt$filter_repeats_by_windows){
filters_inv[["filter_repeats_by_windows"]] <- !is.na(GenomicRanges::findOverlaps(vcf_inv_bed_GRange, windows_with_half_repeats_to_remove , select ="first", ignore.strand=TRUE))
}
if(isTRUE(!is.na(opt$filter_high_DP_standardized))){
#custom filter on DP per windows
vcf_inv_bed_GRange <- vcf2Grange(vcf_inv)
filter_DP_repeats_inv <- create_filter_high_DP_standardized(opt$reference, vcf_inv, threshold = opt$filter_high_DP_standardized$threshold, threshold_CI = opt$filter_high_DP_standardized$threshold_CI, windows_size= opt$filter_high_DP_standardized$windows_size, overlapping= opt$filter_high_DP_standardized$slidding, opt)
gc()
filters_inv[["filter_DP_repeats"]] <- !is.na(GenomicRanges::findOverlaps(vcf_inv_bed_GRange, filter_DP_repeats_inv[[1]][[1]] , select ="first", ignore.strand=TRUE))
}else{
filter_DP_repeats_inv <- NULL
}
# Sum up all filters to create a master filter
master_filter_inv <- Reduce("+", filters_inv) > 0
#information on the number of snps remove by each filters
#inv_remove_by_filter <- lapply(filters_inv, sum, na.rm = TRUE)
vcf_inv_filtered <- vcf2Grange(vcf_inv[!master_filter_inv], metadata = "present")
###############
vcf_bed_GRange <- vcf2Grange(vcf)
removed_sites <- reduce(vcf_bed_GRange[master_filter])
removed_sites_inv <- reduce(vcf_inv_bed_GRange[master_filter_inv])
RES <- as.filterVCF(list(
vcf=vcf,
vcf_filtered=vcf_filtered,
vcf_inv=vcf2Grange(vcf_inv),
vcf_inv_filtered=vcf_inv_filtered,
fixed_inv_Grange=fixed_inv_Grange,
reference=readDNAStringSet(opt$reference),
master_filter=master_filter,
master_filter_inv=master_filter_inv,
filters=filters,
filters_inv=filters_inv,
removed_sites=removed_sites,
removed_sites_inv=removed_sites_inv,
bed_GRange_merged=bed_GRange_merged,
windows_with_half_repeats_to_remove=windows_with_half_repeats_to_remove,
fix_het_pop=fix_het_pop,
filter_DP_repeats=filter_DP_repeats,
stats_vcf_filtered=NA,
opt=opt))
#stats_vcf_filtered=stats_vcf_filtered$CI_Stats))
rm(vcf_inv)
rm(vcf)
gc()
if(isTRUE(opt$debug)){
if(!is.na(opt$output_file)){
saveRDS(RES, paste0(getwd(), "/", basename(paste0(gsub(".vcf$|.vcf.gz$", "", opt$output_file), ".rds"))))
}else{
saveRDS(RES, paste0(getwd(), "/", basename(gsub(".vcf$|.vcf.gz$", ".rds", vcf_file))))
}
}
if(opt$verbose) Sys.procmem()
#remove vcf_inv and inv_filtered to save some disk space?
return(RES)
}
|
35eff1361771115dcf16a02ca3d225b023997aeb | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SyNet/examples/mayflynz.Rd.R | d2cd966dcd6b74c4c2337bca14d6b6b4ba8b4473 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 275 | r | mayflynz.Rd.R | library(SyNet)
### Name: mayflynz
### Title: Mayfly Fauna of New Zealand
### Aliases: mayflynz
### Keywords: datasets
### ** Examples
data(mayflynz)
plot(mayflynz[[2]][,2:3], main = "Mayfly Fauna of New Zealand", xlab = "Latitude",
ylab = "Longitude", asp = 1.3)
|
1022ed71bea01ff7628fa066dbf06cc51e152fdc | 5069665a0ff64671c1ffe92fc46fa4cc68c29f61 | /CTEC_tfidf.R | e5360e0c8daee22779acc58822abafb087a244a8 | [] | no_license | nicholson2208/AlgosAndSociety | 06121530160fcdfc0b6b89fca5ccbcb37a555044 | d8cf9c57179f01a994d931990dac2c82beb3bda2 | refs/heads/master | 2020-04-09T01:17:08.135641 | 2018-12-12T20:15:04 | 2018-12-12T20:15:04 | 159,898,408 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,841 | r | CTEC_tfidf.R | library(tidyverse)
library(tidytext)
# load sentiment dictionary
nrc_lex <- get_sentiments("nrc") # many sentiments
all_stop_words <- stop_words %>% select(-lexicon) # long list of stop words
# read in json from CaesaParser.py, and flatten
dat <- jsonlite::fromJSON('data.json', flatten = TRUE)[[1]]
dat.df <- dat %>%
bind_rows() %>%
mutate(department = rep(names(dat), map_dbl(dat, nrow)))
# filter only CTECs with a gender and select a few columns
gender.comments.dept.df <- dat.df %>%
filter(instructor_gender == "M" | instructor_gender == "F") %>%
select(department, instructor_gender, comments) %>%
mutate(dept_gender = paste(department, instructor_gender, sep="-"))
# one word per row
comment.words <- gender.comments.dept.df %>%
unnest %>%
unnest_tokens(ngram, comments, token="ngrams", n=2)
# bigrams
comment.bigrams <- gender.comments.dept.df %>%
unnest %>%
unnest_tokens(ngram, comments, token="ngrams", n=5)
# filter only words that have a sentiment score
# skip when n > 1
comment.words.interesting <- semi_join(comment.words, nrc_lex)
# create total word counts
gender.word.count <- comment.words.interesting %>%
count(instructor_gender, word, sort=TRUE) %>%
# count(dept_gender, department, instructor_gender, word, sort=TRUE) %>%
ungroup()
# bigrams
gender.bigram.count <- comment.bigrams %>%
count(instructor_gender, ngram, sort=TRUE) %>%
ungroup()
gender.total.bigrams <- gender.bigram.count %>%
group_by(instructor_gender) %>%
summarize(total = sum(n))
gender.bigrams <- left_join(gender.bigram.count, gender.total.bigrams)
gender.bigrams <- gender.bigrams %>%
bind_tf_idf(ngram, instructor_gender, n)
gender.bigrams %>%
# filter(department == 'BME' | department == 'EECS') %>%
arrange(desc(tf_idf)) %>%
mutate(ngram = factor(ngram, levels=rev(unique(ngram)))) %>%
group_by(instructor_gender) %>%
top_n(10) %>%
ungroup %>%
ggplot(aes(ngram, tf_idf, fill=instructor_gender)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~instructor_gender, ncol=2, scales = "free") +
coord_flip()
# group word counts
gender.total.words <- gender.word.count %>%
group_by(instructor_gender) %>%
summarize(total = sum(n))
gender.words <- left_join(gender.word.count, gender.total.words)
# perform tf-idf
gender.words <- gender.words %>%
bind_tf_idf(word, instructor_gender, n)
########
# visualize tf-idf
gender.words %>%
# filter(department == 'BME' | department == 'EECS') %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels=rev(unique(word)))) %>%
group_by(instructor_gender) %>%
top_n(10) %>%
ungroup %>%
ggplot(aes(word, tf_idf, fill=instructor_gender)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~instructor_gender, ncol=2, scales = "free") +
coord_flip()
|
d5c71021f206725793e26224cf30e5b01a95b3bb | 74ef16d6169e660d445bfa1e359d7f08d6bf48aa | /데이터기반 통계분석 시스템구축/GraphicsTool.R | 2e041c009bf8b391ebfb69b9b7d16ae98171837d | [] | no_license | BenHeo/SNU | c739828a19c25c8f11e7dec3a9100951eba97cde | e842377e68a42185f7ece118dc883f0c8e33bb13 | refs/heads/master | 2020-03-21T05:14:37.774269 | 2018-12-17T02:30:57 | 2018-12-17T02:30:57 | 138,151,468 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,403 | r | GraphicsTool.R | data(mtcars)
# View(mtcars)
str(mtcars)
names(mtcars)
plot(mpg~disp, mtcars) # ~는 formula y = ax 에서 y가 앞에 있고 x가 뒤에 있게 하는 것 같은 원리
a = "mpg~disp"
a_f = as.formula(a); class(a_f)
plot(a_f, mtcars)
?plot
plot(hp~disp, mtcars) # hp = B0 + (B1 * disp) + error(평균이 0)
# 회귀분석은 등분산 가정을 지켜야 하는데 이분산 가정이 되는 경우 예측이 힘들겠다고 생각하면 됨
# 에러텀이 0 인 경우를 생각해서 잇는 것과 상위 10% 혹은 5%를 생각해서 잇는 경우가 기울기가 다르다면 등분산성이 어긋난다
set.seed(1)
x = rnorm(100)
y = 2 + 2*x + rnorm(100)
plot(y~x, main = "y=2x+2") # or plot(x,y)
# plot types : p(point), l(line), b(both point and line), s(step), n(no plot)
x = seq(-2, 2, length.out = 10)
y = x^2
plot(x, y, type = 'p')
plot(x, y, type = 'l')
plot(x, y, type = 'b')
plot(x, y, type = 's')
plot(x, y, type = 'n')
plot(x, y, type = 'b', lty = 3) # different line type
plot(x, y, type = 'b', pch = 2) # different shape
plot(x=1:25, y=rep(0,25), pch=1:25) # kyakyakyakya
head(colors()) # colors in r pallete
plot(x,y, type="b", xlab="xx", ylab="yy", main="y=x^2", col="lightblue")
plot(x,y, type="b", xlim= c(-1,1))
# draw multiple plots at once
plot(~mpg+disp+drat, mtcars, main="Simple Scatterplot Matrix", col = "orange", pch = 19)
plot(x,y, pch =20, main="scatter plot")
abline(a=1, b=2, col="red") # a + bx
abline(v=1, col="blue") # vertical line
abline(h=1, col="green") # horizontal line
plot(x=1,y=1, type='n', xlim=c(0,10), ylim=c(0,5), xlab = 'time', ylab = '# of visiting')
x = 0:10
set.seed(1)
y=rpois(length(x), lambda=1)
points(x,y,col="blue", type="s")
points(x,y,col="red", type="l", lty = 3)
plot(0,0, type='n', xlim=c(-2,2), ylim=c(-2,2))
x = c(-2,1,0,1,0)
y = c(0,-1,2,-2,1)
lines(x,y) # please draw by order :(
# NA is used for disconnect line
plot(0,0, type='n', xlim=c(-2,2), ylim=c(-2,2))
x = c(-2,1,NA,1,0)
y = c(0,-1,NA,-2,1)
lines(x,y) # still not good
# use group or order
plot(0,0, type='n', xlim=c(1,5), ylim=c(0,2))
x = seq(1,5,1)
abline(v=x, lty=1:length(x))
z = sort(rnorm(100))
y1 = 2+ z + rnorm(100)
plot(z, y1, col="blue", pch=3)
points(z, y1/2, col="red", pch=19)
legend("topright", c("pch_3", "pch_19"), col=c("blue", "red"), pch = c(3,19))
### Visualization of KNN
set.seed(1)
x <- sort(rnorm(100))
y <- 3 + x^2 + rnorm(100)
plot(x, y, pch = 20)
fit = lm(y~x)
str(fit)
coef <- fit$coefficients
coef[1]
coef[2]
abline(coef[1], coef[2], col='red') # model bias ==> evaluated by least square ===> enlarger model space
# y_hat(x) = 1/k * sum(index set of xi k-nearest to x * yi)
# KNN is non-parametric regression which means KNN doesn't assume model space
library(FNN)
k10zero <- knnx.index(x, 0, k=10)
x[47]
x[46]
idx <- k10zero[1,]
points(x[idx], y[idx], pch = 19, col = 'green' )
abline(v=0, lty = 3)
k10mean0 <- mean(y[idx])
abline(h=k10mean0, col = 'blue')
eval.n = 100
eval.point = seq(-3,3,length.out = 100)
plot(x,y,pch=20)
idx.mat <- knnx.index(x, eval.point, k=10)
yhat <- rep(0, eval.n)
for (i in 1:eval.n){
yhat[i] <- mean(y[idx.mat[i,]])
}
lines(eval.point, yhat, type = 'l', col = 'red')
a = matrix(1:25, 5, 5)
image(a)
a
z <- 2*volcano
dim(z)
x <- 10*(1:nrow(z))
y <- 10*(1:ncol(z))
z[30,4]
x[30]
y[4]
persp(x,y,z, theta = 135, # 산 모양
ltheta = 20, col = "green3")
contour(x,y,z) # 등고선
|
fa9ffa66974b6e7ec2ae8c61c04594e9349d50c3 | a3eb888d081e824d3081412ea1da47a67d40cc92 | /Getting and Cleaning Data/12_reading_from_the_web.R | 96dc352187f76cf19a0a769d0721e9291b19a7eb | [] | no_license | Ads99/Coursera | b1bc38ba6c61b84abd3694c2eaa1a158a5fc1a94 | f775ac73361098746d38db3bf4a004100109e4c6 | refs/heads/master | 2020-04-25T06:47:01.003825 | 2016-09-05T15:08:47 | 2016-09-05T15:08:47 | 25,043,486 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,684 | r | 12_reading_from_the_web.R | setwd('C://Users//ABaker//Documents//GitHub//Coursera//Getting and Cleaning Data')
if (!file.exists("data")) {
dir.create("data")
}
# Getting data off webpages - readLines()
con = url("http://scholar.google.com/citations?user=HI-I6C0AAAAJ&hl=en")
htmlCode = readLines(con)
close(con)
htmlCode
# Parsing with XML
# The above generated a lot of unstructured html stuff so we can use the
# XML package to parse this data
library(XML)
url <- "http://scholar.google.com/citations?user=HI-I6C0AAAAJ&hl=en"
html <- htmlTreeParse(url, useInternalNodes=T)
xpathSApply(html, "//title", xmlValue)
# [1] "Jeff Leek - Google Scholar Citations"
xpathSApply(html, "//td[@id='col-citedby']", xmlValue)
# Another approach - GET from the httr package
library(httr); html2 = GET(url)
content2 = content(html2,as="text")
parsedHtml = htmlParse(content2,asText=TRUE)
xpathSApply(parsedHtml, "//title", xmlValue)
# More complicated - Accessing websites with passwords
pg1 = GET("http://httpbin.org/basic-auth/user/passwd")
#Response [http://httpbin.org/basic-auth/user/passwd]
#Date: 2014-10-16 17:50
#Status: 401
#Content-type: <unknown>
# <EMPTY BODY>
# So the httr package allows us to authenticate with user name and password
pg2 = GET("http://httpbin.org/basic-auth/user/passwd",
authenticate("user","passwd"))
pg2
#Response [http://httpbin.org/basic-auth/user/passwd]
#Date: 2014-10-16 17:52
#Status: 200
#Content-type: application/json
#Size: 46 B
#{
# "authenticated": true,
# "user": "user"
#}
names(pg2)
# Using handles (won't need to repeatedly authenticate)
google = handle("http://google.com")
pg1 = GET(handle=google,path="/")
pg2 = GET(handle=google,path="search")
|
d4347af2d55d9caff08f000318c12fcd55e21d7a | 60a091a68b32cdceb2a02f5e85783a05b2230e8a | /useTau.R | bcc29faa43809b28babe86ebffc1d140f535eb92 | [] | no_license | pingqingsheng/mbeta | b57e8c69c0244033b008787ecbc2311e4e76f735 | f83b2bfdfde438920d3c7a993fba955585c882cf | refs/heads/master | 2021-01-11T22:35:57.652426 | 2017-03-09T03:52:05 | 2017-03-09T03:52:05 | 78,995,547 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,427 | r | useTau.R |
# Simulate some data
library(MASS)
set.seed(234)
fixed <- mvrnorm(1,m=rep(5,5), Sigma=diag(rep(1,5)))
D <- outer(rep(sqrt(3),5), rep(sqrt(3),5))
diag(D) <- 9
random <- as.matrix(mvrnorm(100, m=rep(0,5), Sigma=D))
random <- matrix(rep(random, each=100),ncol=5, byrow=FALSE)
phi <- 5
X <- matrix(0,10000,4)
for(i in 1:100){
X[((i-1)*100+1):(i*100),] <- mvrnorm(100, m=rep(i/100,4), Sigma=diag(rep(1,4)))
}
X <- cbind(1,X)
Z <- X
group <- rep(seq_len(100), each=100)
Dat <- as.data.frame(cbind(X,Z))
Dat <- cbind(group, Dat)
# Let X=Z at present time, but of course we could sim different Z
eta <- X %*% fixed + apply(Z*random, 1, sum)
mu <- as.vector(pmax(1/(1+exp(eta)^(-1)), .Machine$double.eps))
y <- rbeta(length(group), shape1 = mu*phi, shape2 = (1-mu)*phi)
names(Dat) <- c("group",paste0("x",seq_len(5)),paste0("z",seq_len(5)))
Dat <- cbind(y=y, Dat)
# Estimation with Laplace Approximation
# initialization
# Use mini batch (possiblly find a extra package do SGD to accelerate or rewrite with C)
terminate <- FALSE
tolerance_1 <- 1
tolerance_2 <- 1
tolerance_3 <- 5
# gamma_new <- gamma_0
gamma_new <- unique(random)
beta_new_sgd <- fixed
# D_new <- matrix(15,5,5)
# diag(D_new) <- 20
D_new <- D
phi_new_sgd <- 5
rate <- 0.01
cond_1 <- FALSE
cond_2 <- FALSE
cond_3 <- FALSE
marker <- 1
verbose <- TRUE
# SGD then Newtown-Rapson iteratively
while(!terminate){
beta_old <- beta_new_sgd
phi_old <- phi_new_sgd
D_old <- D_new
tau_new_sgd <- self_solve(D_old)
gamma_old <- matrix(rep(gamma_new, each=100), ncol=5, byrow = FALSE)
gamma_temp <- aggregate(gamma_old, by=list(Dat$group), unique, simplify=TRUE)
gamma_temp <- as.matrix(gamma_temp[,-1])
# Use ADAM updating scheme
b_1 <- 0.9
b_2 <- 0.9
m_beta <- 0
nu_beta <- 0
m_D <- 0
nu_D <- 0
m_phi <- 0
nu_phi <- 0
# m_beta_old <- 0
# nu_beta_old <- 0
# m_D_old <- 0
# nu_D_old <- 0
# Give gamma_i (gamma_old), use SGD to optimize beta and D
for (i in 1:max(as.integer(Dat$group))){
# reset parameter and moment
beta_old_sgd <- beta_new_sgd
tau_old_sgd <- tau_new_sgd
phi_old_sgd <- phi_new_sgd
X <- as.matrix(Dat[group==i, 3:12])
Y <- as.matrix(Dat[group==i, 1])
# ------------------------ ADAM SGD ---------------------------------
# Updating 1st and 2nd moment
score_beta <- g_h(X, Y, gamma_temp[i,], beta_old_sgd)$g_beta - 1/2*g_d_h_2(X, Y, gamma_temp[i,], beta_old_sgd, tau_old_sgd)$g_beta
m_beta <- b_1*m_beta + (1-b_1)*score_beta
nu_beta <- b_2*nu_beta + (1-b_2)*score_beta^2
steps_1 <- rate/sqrt(nu_beta/(1-b_1))*m_beta/(1-b_1)
beta_new_sgd <- beta_old_sgd + steps_1
score_phi <- g_h(X, Y, gamma_temp[i,], beta_old_sgd)$g_phi - 1/2*g_d_h_2(X, Y, gamma_temp[i,], beta_old_sgd, tau_old_sgd)$g_phi
m_phi <- b_1*m_phi + (1-b_1)*score_phi
nu_phi <- b_2*nu_phi + (1-b_2)*score_phi^2
steps_2 <- rate/sqrt(nu_phi/(1-b_1))*m_phi/(1-b_1)
phi_new_sgd <- phi_old_sgd + steps_2
score_tau <- g_h(X, Y, gamma_temp[i,], beta_old_sgd)$g_tau - 1/2*t(self_solve(d_h_2(X,Y,gamma_temp[i,],beta_old_sgd,tau_old_sgd))) + 1/2*D_old
m_D <- b_1*m_D + (1-b_1)*score_tau
nu_D <- b_2*nu_D + (1-b_2)*score_tau^2
steps_3 <- rate*(1/sqrt(nu_D/(1-b_2)))*m_D/(1-b_2)
tau_new_sgd <- tau_old_sgd + steps_3
tau_new_sgd <- eigen(tau_new_sgd)$vectors %*% diag(pmax(eigen(tau_new_sgd)$values,0)) %*% t(eigen(tau_new_sgd)$vectors)
#
# ----------------------------- Momentum SGD -----------------------------------
# nu_beta_new <- 0.9*nu_beta_old + rate*score_beta
# nu_D_new <- 0.9*nu_D_old + rate*score_psi
# beta_new_sgd <- beta_old_sgd + nu_beta_new
# D_new_sgd <- D_old_sgd + nu_D_new
#
if(any(is.nan(beta_new_sgd)==TRUE)) stop("NaN appears again !!!!!!!!!!!")
cond1 <- all(abs(beta_old_sgd - beta_new_sgd) < 0.00001 )
cond2 <- all(abs(phi_old_sgd - phi_new_sgd) < 0.00001 )
cond3 <- all(abs(tau_old_sgd - tau_new_sgd) < 0.1 )
if(cond1 & cond2 & cond3) {cat("SGD converge","\n"); break}
if(verbose){
cat(i,"th group descent (beta|phi)", paste0(round(beta_new_sgd,digit=4), collapse=" "), " | ", phi_new_sgd, "\n")
# cat("biggest tau_ij", max(tau_new_sgd),"\n")
}
}
# Give up iteratively reweighted algorithm.....
# I need some time to really understand IRWLS
# Use Newtown Rapson instead
marker_inner <- 1
converge <- FALSE
gamma_old <- matrix(rep(gamma_temp,each=100), ncol=5, byrow=FALSE)
gamma_new <- aggregate(gamma_old, by=list(Dat$group), unique, simplify=TRUE)
gamma_new <- as.matrix(gamma_new[,-1])
while(!converge){
gamma_old <- gamma_new
working_value <- working_vec(Dat[,3:12], Dat[,1], gamma_old, beta_new_sgd, tau_new_sgd)
# if(marker_inner > 100) {
# factor <- marker_inner
# }else{
# factor <- 101
# }
# working_value <- working_vec(Dat[,3:12], Dat[,1], gamma_old, beta_new_sgd, tau_new_sgd)
# All these loops seems stupid
for (i in 1:length(unique(Dat$group))){
gamma_new[i,] <- gamma_old[i,] + self_solve(working_value$H[[i]]) %*% working_value$d_h[[i]]
}
if(all(abs(gamma_new - gamma_old) < 2)) {converge <- TRUE}
if(any(is.nan(gamma_new)==TRUE)) {stop("NaN appears again !!!!!!!!!!")}
marker_inner <- marker_inner + 1
cat(paste0(marker_inner, "th iteration: Newtown's Way ", max(abs(gamma_new - gamma_old))), "\n")
cat("Difference between D and D_hat(frobenious): ", sum((D_old-var(gamma_new))^2), "\n")
# if(marker_inner > 19) {cat("Fail to converge","\n"); break}
}
# check convergence
beta_new <- beta_new_sgd
phi_new <- phi_new_sgd
D_new <- self_solve(tau_new_sgd)
cond_1 <- (all(abs(beta_new - beta_old) < tolerance_1))
cond_2 <- (all(abs(phi_new - phi_old) < tolerance_2))
cond_3 <- (sum((D_new-D_old)^2) < tolerance_3)
if ( cond_1 & cond_2 & cond_3) {
terminate <- TRUE
cat("Estimation Finished","\n")
(beta_hat <- beta_new)
(D_hat <- D_new)
(phi_hat <- phi_new)
}
marker <- marker + 1
if(marker > 30) {
warning("Fail to converge, estimation is not optimal")
terminate <- TRUE
(beta_hat <- as.numeric(beta_new))
(D_hat <- D_new)
(phi_hat <- phi_new)
}
cat("--------",paste0(marker, "th iteration"),"--------","\n")
}
#
gamma_hat <- matrix(rep(gamma_new,100), ncol=5, byrow=FALSE)
eta_hat <- as.matrix(Dat[,3:7]) %*% beta_hat + apply(as.matrix(Dat[,8:12])*gamma_hat, 1, sum)
mu_hat <- 1/(1+exp(eta_hat)^(-1))
y[y > 1-.Machine$double.eps] <- 1-.Machine$double.eps
y[y < .Machine$double.eps] <- .Machine$double.eps
mu_real <- y
RMSE <- sqrt(sum((mu_hat - mu_real)^2))
dev.cur()
png("trial .png", width=600, height=400, res=100, units="px")
plot(density(y), type="l", lwd=2)
lines(density(mu_hat, bw=density(y)$bw), lwd=2, lty=2, main="Line: Real vs Fitted", col="red")
legend("topright", legend=c("Fitted","Real"), lty=c(2,1), lwd=2, col=c(2,1))
dev.off()
|
ba51873d4c5e5aad6fec0ed7905d90276679509f | c9cae6b31d52f7ce5720308a3a8b1193cb5aabcd | /01-barcelona-incidents-complaints-suggestions/functions/data_reader.R | c966a2625059aaa9f8a809efd978c2622806f397 | [] | no_license | marcfresquet/opendata-rshiny | f306baa1c185344a25ad8135d503a8c24292bc85 | 95f4f02424f1359b41a41eb46bfda400fbbb72a1 | refs/heads/master | 2020-03-22T04:29:26.191908 | 2018-10-26T17:26:25 | 2018-10-26T17:26:25 | 139,502,019 | 0 | 0 | null | 2018-10-21T20:54:41 | 2018-07-02T22:43:06 | R | UTF-8 | R | false | false | 640 | r | data_reader.R | data_reader <- function(csv_path) {
# Read data from a csv file
df <- read_csv(csv_path)
# Filter by target columns
df <- df %>%
select(target_columns)
# Filter rows depending on year
df <- df %>%
filter(ANY_DATA_ALTA >= min_year)
# Transform some columns to numeric values
for (num_col in numeric_columns) {
df[[num_col]] <- as.numeric(df[[num_col]])
}
# Format date
df$DATA_ALTA <- ymd(paste(df$ANY_DATA_ALTA, df$MES_DATA_ALTA, df$DIA_DATA_ALTA, sep="-"))
df$DATA_TANCAMENT <- ymd(paste(df$ANY_DATA_TANCAMENT, df$MES_DATA_TANCAMENT, df$DIA_DATA_TANCAMENT, sep="-"))
# Return DataFrame
return(df)
}
|
c3904994c8e6a6a15a901293b3791fb076731fce | 12f3c27dfa6fda0a241c3974a7721acc4327fb09 | /scripts/03_bed_movement_correction.R | bca48718435411c1932503ae79ef95d3d758592c | [
"MIT"
] | permissive | sbhattacharyay/nims | 2793d7ff8cb3f1644347a029689cfbf573322b87 | cf93aaa2fc1814c9dba1dad3768377456d1c637c | refs/heads/master | 2023-06-24T14:54:12.391823 | 2023-06-20T09:27:45 | 2023-06-20T09:27:45 | 236,843,680 | 3 | 0 | MIT | 2021-04-14T14:46:29 | 2020-01-28T21:17:29 | R | UTF-8 | R | false | false | 4,160 | r | 03_bed_movement_correction.R | #### Master Script 3: Bed Motion Correction and Collection of Multiple Imputations ####
#
# Shubhayu Bhattacharyay, Matthew Wang, Eshan Joshi
# University of Cambridge
# Johns Hopkins University
# email address: sb2406@cam.ac.uk
#
### Contents:
# I. Initialization
# II. Load imputed motion feature data and collect into one object
# III. Determine feature space thresholds to correct bed motion
# IV. Correct bed motion by identifying time points and patient indices during which SMA exceeds a literature-reviewed threshold
### I. Initialization
# Denote number of imputations
m <- 9
# Call requisite libraries
library(tidyverse)
library(readxl)
# Define feature label names
feature.labels <- c("BPW","FDE","HLF_h","HLF_l","MFR","SMA","WVL")
### II. Load imputed motion feature data and collect into one object
compiledImputations <- vector(mode = "list")
for (i in 1:m){
print(paste('Imputation no.',i,'started'))
currPattern <- paste0('*',i,'.csv')
currFileList <- list.files('../features/01_imputed_features/',pattern = currPattern)
imputation.df <- data.frame(matrix(ncol = 12, nrow = 0))
for (j in 1:length(feature.labels)){
currFilePath <- file.path('../features/01_imputed_features',currFileList[j])
curr.df <- read.csv(currFilePath) %>%
mutate(Feature = feature.labels[j]) %>%
relocate(Feature, .after = TimeOfDay)
imputation.df <- rbind(imputation.df,curr.df)
print(paste('Feature no.',j,'complete'))
}
compiledImputations[[i]] <- imputation.df %>% arrange(UPI,RecordingIdx,Feature)
print(paste('Imputation no.',i,'complete'))
}
### III. Determine feature space thresholds to correct bed motion
# Based on an SMA threshold for dynamic vs. static activity (https://doi.org/10.1016/j.medengphy.2013.06.005),
# find corresponding thresholds for the other feature spaces.
SMA.thresh <- .135
# Note we only use the first imputation to do so since, when testing, we found that the thresholds are the same for all imputations
source('./functions/find_thresholds.R')
feature.thresholds <- find_thresholds(compiledImputations[[1]],SMA.thresh)
featRanges <-
list(
BPW.nm.range = c(0, feature.thresholds[1]),
FDE.nm.range = c(feature.thresholds[2], 1.707),
HLF_l.nm.range = c(0, feature.thresholds[3]),
HLF_h.nm.range = c(0, feature.thresholds[4]),
MFR.nm.range = c(feature.thresholds[5], 3.2),
SMA.nm.range = c(0, feature.thresholds[6]),
WVL.nm.range = c(0, feature.thresholds[7])
)
### IV. Correct bed motion by identifying time points and patient indices during which SMA exceeds a literature-reviewed threshold:
for (i in 1:length(compiledImputations)){
print(paste("Imputation No.",i,"Started"))
curr.df <- compiledImputations[[i]]
curr.SMA.df <-curr.df %>% filter(Feature == "SMA")
bed.SMA.rows <- which(curr.SMA.df$Bed > SMA.thresh)
for (j in 1:length(feature.labels)){
a <- featRanges[[j]][1]
b <- featRanges[[j]][2]
print(paste("Feature No.",j,"Started"))
curr.feat.rows <- which(curr.df$Feature == feature.labels[j])
currFeat.df <- curr.df %>% filter(Feature == feature.labels[j])
currFeatChange.df <- currFeat.df[bed.SMA.rows,]
if (feature.labels[j] %in% c("FDE","MFR")) {
temp.mat <- currFeatChange.df[,c('LA','LE','LW','RA','RE','RW')] + currFeatChange.df[,'Bed']
temp.mat[temp.mat > b] <- runif(sum(temp.mat > b),a+(b-a)/2,b)
} else {
temp.mat <- currFeatChange.df[,c('LA','LE','LW','RA','RE','RW')] - currFeatChange.df[,'Bed']
temp.mat[temp.mat < 0] <- runif(sum(temp.mat < 0),0,b/2)
}
currFeatChange.df[,c('LA','LE','LW','RA','RE','RW')] <- temp.mat
currFeat.df[bed.SMA.rows,] <- currFeatChange.df
curr.df[curr.feat.rows,] <- currFeat.df
print(paste("Feature No.",j,"Complete"))
}
curr.df <- curr.df %>%
mutate(ImputationNo = i) %>%
relocate(ImputationNo,
UPI,
RecordingIdx,
HoursFromICUAdmission,
TimeOfDay,
Feature)
write.csv(currImp,paste0('../features/02_bed_corrected_imputed_features/bed_corrected_imputation_',i,'.csv'),row.names = F)
print(paste("Imputation No.",i,"Complete"))
}
|
8954532b5865f5038f1ba3d609604f5bd06e173b | bb191ee8e08341188e48c62f1fc30d1ce322b470 | /cachematrix.R | c08c23a9342e7a698f233d1d8a05fdd5932a25aa | [] | no_license | jvassy/ProgrammingAssignment2 | 75005b8f2cc512ca681b08a485a5bc886f1d02fa | 8839b84ac0b4a33a809e11260c163beeb46e1bea | refs/heads/master | 2021-01-18T10:32:48.056734 | 2014-07-20T21:36:36 | 2014-07-20T21:36:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,231 | r | cachematrix.R |
## The combination of makeCacheMatrix and cacheSolve takes a matrix 'x' and reports its inverse 's', first determining whether 's' is already cached. If it is, it report 's' without calculating it again.
## makeCacheMatrix creates a list of 4 functions that can be read by cacheSolve to determine whether the inverse of a matrix 'x' has already been calculated and cached ('s')
makeCacheMatrix <- function(x = matrix()) {
s <- NULL
set <- function(y) {
x <<- y
s <<- NULL
}
get <- function() x
setSolve <- function(solve) s <<- solve
getSolve <- function() s
list(set = set, get = get,
setSolve = setSolve,
getSolve = getSolve)
}
cacheSolve computes the inverse of the special "matrix" returned by makeCacheMatrix above. If the inverse has already been calculated ('s'), then cacheSolve retrieves it from the cache. If not, it calculates and reports the inverse.
cacheSolve <- function(y, ...) {
s <- y$getSolve()
if(!is.null(s)) {
message("getting cached data")
return(s)
}
data <- y$get()
s <- solve(x)
y$setSolve(s)
s
} |
8563335a3c52843abd52657bdda0b56aa1ef84cc | af074201b063ceacbf57f1dd8da0fa95f8513e6f | /mayer1pre.R | 439c1d3f94074fe6e0a4281b28ff2f8f3879438b | [] | no_license | cswasey16/thesis | d3244172460f5f10f3191196b09c92b3f6c01f55 | 4193e5fae5e1ab9816a202a981d4a0726945d711 | refs/heads/master | 2021-01-10T13:16:05.677161 | 2016-04-20T02:02:34 | 2016-04-20T02:02:34 | 53,007,128 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 867 | r | mayer1pre.R | #mayer table 2
library(stargazer)
#percents across just D
anes_prevoter <- subset(anes_2008, anes_2008$prevoter==TRUE)
dummiespre <- dummy(anes_prevoter$subbucketsfac, sep="", fun= as.numeric, drop=FALSE)
weightspre <- anes_prevoter$V080102
countspre <- colSums(dummiespre)
dumweightpre <- sapply(1:ncol(dummiespre),function(x) dummiespre[,x] * weightspre )
sumspre <- colSums(dumweightpre)
totalpre <- sum(sumspre)
range <- c("-100:-91", "-90:-81", "-80:-71", " -70:-61", "-60:-51", "-50:-41", "-40:-3","-30:-21", "-20:-16", "15:-11", "-10:-6", "-5:-1", "0", "1:5", "6:10", "11:15" ,"16:20", "21:30", "31:40", "41:50", "51:60", "61:70", "71:80","81:90", "91:100", "NA")
wpctpre <- (sumspre/totalpre)*100
percentpre <- round(wpctpre, digits=2)
names(percent) <- range
mayer1pre <- cbind(range, countspre, percentpre)
stargazer(mayer1pre, rownames=FALSE)
|
fe785c57420c930a1371ace9c9f16174dbb7bd7a | e3bb23c8c3be4c7edcb8dd5a3571c8f0dd567490 | /Anomoly_Detection_R.R | 02a3cf6a35e663ff09f738d1abc6c273036affd8 | [] | no_license | snersuai/ML_Stack | 4b3e8bfa896dca4042c2574d7202618f3ca3ce15 | 820d1e4eba1c53ad1ee4719a2262a73904fb2aa1 | refs/heads/master | 2021-10-16T15:49:35.949645 | 2018-12-28T15:45:19 | 2018-12-28T15:45:19 | 163,424,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,927 | r | Anomoly_Detection_R.R | install.packages("devtools")
devtools::install_github("twitter/AnomalyDetection")
library(AnomalyDetection)
help(AnomalyDetectionTs)
library(AnomalyDetection)
??AnomalyDetectionTs
help(AnomalyDetectionVec)
AnomalyDetectionVec(raw_data[,2], max_anoms=0.02, period=1440, direction='both', only_last=FALSE, plot=TRUE)
raw_data <- read.csv(file="C:/Users/Administrator/Documents/ML_Analysis/heart_activity_fitbit1.csv",
header=TRUE, sep=",",stringsAsFactors = FALSE)
head(raw_data,100)
raw_data$caloriesOut_OR = as.numeric(raw_data$caloriesOut_OR)
raw_data$caloriesOut_FB = as.numeric(raw_data$caloriesOut_FB)
raw_data$caloriesOut_Cardio = as.numeric(raw_data$caloriesOut_Cardio)
raw_data$caloriesOut_Peak = as.numeric(raw_data$caloriesOut_Peak)
raw_data$dateTime = as.POSIXct(raw_data$dateTime)
raw_data[is.na(raw_data)] <- 0
AnomalyDetectionTs(raw_data[c("dateTime","caloriesOut_Peak")], xlabel = "Heart in Peak Mode" ,
ylabel = "Calories" , na.rm=T, max_anoms=0.1, direction='both', plot=TRUE)$plot
AnomalyDetectionTs(raw_data[c("dateTime","caloriesOut_FB")], xlabel = "Heart in Fat burn Mode" , ylabel = "Calories",
max_anoms=0.1, direction='both', plot=TRUE)$plot
AnomalyDetectionTs(raw_data[c("dateTime","caloriesOut_Cardio")], xlabel = "Heart in Cardio Mode", ylabel = "Calories",
max_anoms=0.1, direction='both', plot=TRUE)$plot
AnomalyDetectionTs(raw_data[c("dateTime","caloriesOut_OR")], "Heart in Out of Range Mode", ylabel = "Calories",
max_anoms=0.1, direction='both', plot=TRUE)$plot
res$plot
df <- read.csv(url("https://raw.githubusercontent.com/ieatbaozi/R-Practicing/master/example.csv"),header = TRUE,stringsAsFactors = FALSE)
df$DateTime <- as.POSIXct(df$DateTime)
library(AnomalyDetection)
ADtest <- AnomalyDetectionTs(df, max_anoms=0.1, direction='both', plot=TRUE)
ADtest$plot
|
6345c34cf9ac1d51d1f06a3f36b3f53be826a9ef | 1f94d932cd3526dadff7e47eeb32c14f92aeba41 | /R/altadata.R | 5288b1d286b9cf1504f8e74bbe65e18e64714272 | [] | no_license | altabering/altadata-r | 8218afae2367c3c681c864a6aded698b61b66df5 | d057bddbb0b52ce15a672e5722bd663f1d013bf7 | refs/heads/master | 2023-02-09T07:33:13.638349 | 2020-12-29T14:50:17 | 2020-12-29T14:50:17 | 313,133,338 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,161 | r | altadata.R | #' Initialize retrieve data process
#'
#' @param product_code data product code
#' @param limit number of rows you want to retrieve
#'
#' @return Nothing just set the initial parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.get_data("co_10_jhucs_03", limit = 50)
#' }
altadata.get_data <- function(product_code, limit) {
altadata.check_parameters(product_code, "product_code", parameter_type = "character")
data_api_url <- paste(
getOption("aldatata.data_api_base_url"),
product_code,
"/?format=json",
"&api_key=",
getOption("aldatata.api_key"),
sep =""
)
options(aldatata.data_api_url = data_api_url)
options(aldatata.condition_text = "")
if(missing(limit)){
options(aldatata.data_limit = NULL)
} else {
altadata.check_parameters(limit, "limit", parameter_type = "numeric")
options(aldatata.data_limit = limit)
}
}
#' Fetch data with configurations given before
#'
#' @return dataframe object
#' @export
#'
#' @examples
#' \dontrun{
#' aldatata.api_key('YOUR_API_KEY')
#' altadata.get_data("co_10_jhucs_03", limit = 50)
#' altadata.load()
#' }
altadata.load <- function() {
data <- c()
page <- 1
total_size <- 0
response_length <- 1
data_limit <- getOption("aldatata.data_limit")
while (response_length > 0) {
request_url <- altadata.query_builder(page)
tryCatch({
df <- altadata.request(request_url)
}, error = function(e){
print(e)
})
data = rbind(data, df)
response_length <- length(df)
total_size <- length(data)
if(!is.null(data_limit)){
if(total_size > data_limit){
break
}
}
page <- page + 1
}
if(!is.null(data_limit)){
data <- utils::head(data, data_limit)
}
return(data)
}
#' Get customer subscription info
#'
#' @return dataframe object
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.list_subscription()
#' }
altadata.list_subscription <- function() {
subscription_api_url <- getOption("aldatata.subscription_api_url")
subscription_info <- altadata.request(subscription_api_url)
return(subscription_info)
}
#' Get data header as a vector
#'
#' @param product_code data product code
#'
#' @return vector object
#' @export
#'
#' @examples
#' \dontrun{
#' aldatata.api_key('YOUR_API_KEY')
#' altadata.get_header("co_10_jhucs_03")
#' }
altadata.get_header <- function(product_code) {
altadata.check_parameters(product_code, "product_code", parameter_type = "character")
request_url <- paste(
getOption("aldatata.data_api_base_url"),
product_code,
"/?format=json",
"&api_key=",
getOption("aldatata.api_key"),
"&page=1",
sep =""
)
json_response <- altadata.request(request_url)
header_info <- names(json_response)
return(header_info)
}
#' Select specific columns in the retrieve data process
#'
#' @param selected_columns list of columns to select
#'
#' @return Nothing just set the select parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.select(c("reported_date", "province_state", "mortality_rate"))
#' }
altadata.select <- function(selected_columns) {
altadata.check_parameters(selected_columns, "selected_columns", parameter_type = "vector")
selected_columns_text <- paste(selected_columns, collapse=",")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&columns=",
selected_columns_text,
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' Sort data by given column and method in the retrieve data process
#'
#' @param order_column column to which the order is applied
#' @param order_method sorting method. Possible values: asc or desc
#'
#' @return Nothing just set the sort parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.sort("province_state", order_method = "desc")
#' }
altadata.sort <- function(order_column, order_method = "asc") {
altadata.check_parameters(order_column, "order_column", parameter_type = "character")
altadata.check_parameters(order_method, "order_method", parameter_type = "character")
if(!(order_method %in% c("asc", "desc"))){
stop("order_method parameter must be 'asc' or 'desc'")
}
condition_text <- paste(
getOption("aldatata.condition_text"),
"&order_by=",
order_column,
"_",
toString(order_method),
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' Equal condition by given column and value in the retrieve data process
#'
#' @param condition_column column to which the condition will be applied
#' @param condition_value value to use with condition
#'
#' @return Nothing just set the equal condition parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.equal("province_state", "Alabama")
#' }
altadata.equal <- function(condition_column, condition_value) {
altadata.check_parameters(condition_column, "condition_column", parameter_type = "character")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&",
condition_column,
"_eq=",
toString(condition_value),
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' Not equal condition by given column and value
#'
#' @param condition_column column to which the condition will be applied
#' @param condition_value value to use with condition
#'
#' @return Nothing just set the not equal condition parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.not_equal("province_state", "Utah")
#' }
altadata.not_equal <- function(condition_column, condition_value) {
altadata.check_parameters(condition_column, "condition_column", parameter_type = "character")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&",
condition_column,
"_neq=",
toString(condition_value),
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' Greater than condition by given column and value
#'
#' @param condition_column column to which the condition will be applied
#' @param condition_value value to use with condition
#'
#' @return Nothing just set the greater than condition parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.greater_than("mortality_rate", 2)
#' }
altadata.greater_than <- function(condition_column, condition_value) {
altadata.check_parameters(condition_column, "condition_column", parameter_type = "character")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&",
condition_column,
"_gt=",
toString(condition_value),
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' Greater than equal condition by given column and value
#'
#' @param condition_column column to which the condition will be applied
#' @param condition_value value to use with condition
#'
#' @return Nothing just set the greater than equal condition parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.greater_than_equal("mortality_rate", 3)
#' }
altadata.greater_than_equal <- function(condition_column, condition_value) {
altadata.check_parameters(condition_column, "condition_column", parameter_type = "character")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&",
condition_column,
"_gte=",
toString(condition_value),
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' Less than condition by given column and value
#'
#' @param condition_column column to which the condition will be applied
#' @param condition_value value to use with condition
#'
#' @return Nothing just set the less than condition parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.less_than("mortality_rate", 2)
#' }
altadata.less_than <- function(condition_column, condition_value) {
altadata.check_parameters(condition_column, "condition_column", parameter_type = "character")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&",
condition_column,
"_lt=",
toString(condition_value),
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' Less than equal condition by given column and value
#'
#' @param condition_column column to which the condition will be applied
#' @param condition_value value to use with condition
#'
#' @return Nothing just set the less than equal condition parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.less_than_equal("mortality_rate", 3)
#' }
altadata.less_than_equal <- function(condition_column, condition_value) {
altadata.check_parameters(condition_column, "condition_column", parameter_type = "character")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&",
condition_column,
"_lte=",
toString(condition_value),
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' In condition by given column and value list
#'
#' @param condition_column column to which the condition will be applied
#' @param condition_value value to use with condition
#'
#' @return Nothing just set the in condition parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.condition_in("province_state", c("Utah", "Alabama"))
#' }
altadata.condition_in <- function(condition_column, condition_value) {
altadata.check_parameters(condition_column, "condition_column", parameter_type = "character")
altadata.check_parameters(condition_value, "condition_value", parameter_type = "vector")
condition_value_text <- paste(condition_value, collapse=",")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&",
condition_column,
"_in=",
condition_value_text,
sep = ""
)
options(aldatata.condition_text = condition_text)
}
#' Not in condition by given column and value list
#'
#' @param condition_column column to which the condition will be applied
#' @param condition_value value to use with condition
#'
#' @return Nothing just set the not in condition parameters
#' @export
#'
#' @examples
#' \dontrun{
#' altadata.condition_not_in("province_state", c("Utah", "Alabama"))
#' }
altadata.condition_not_in <- function(condition_column, condition_value) {
altadata.check_parameters(condition_column, "condition_column", parameter_type = "character")
altadata.check_parameters(condition_value, "condition_value", parameter_type = "vector")
condition_value_text <- paste(condition_value, collapse=",")
condition_text <- paste(
getOption("aldatata.condition_text"),
"&",
condition_column,
"_notin=",
condition_value_text,
sep = ""
)
options(aldatata.condition_text = condition_text)
}
## Helper functions
# Check parameter types by given inputs
altadata.check_parameters <- function(paramater, paramater_name, parameter_type) {
if (parameter_type == "vector") {
if (class(paramater) != "character") {
stop(paste(paramater_name, " parameter must be ", parameter_type, sep=""))
}
else if (length(paramater) < 1) {
stop(paste(paramater_name, " parameter must contain at least one value", sep=""))
}
}
else if (parameter_type == "character") {
if (class(paramater) != "character") {
stop(paste(paramater_name, " parameter must be ", parameter_type, sep=""))
}
} else if (parameter_type == "numeric") {
if (class(paramater) != "numeric") {
stop(paste(paramater_name, " parameter must be ", parameter_type, sep=""))
}
}
}
# Request API and parse the result
altadata.request <- function(request_url) {
response <- httr::GET(request_url)
if(httr::status_code(response) != 200){
stop(httr::content(response, as = "text"), call. = FALSE)
}
json_response <- jsonlite::fromJSON(rawToChar(response$content))
return(json_response)
}
# Create API request url
altadata.query_builder <- function(page) {
condition_text <- getOption("aldatata.condition_text")
if(condition_text == ""){
request_url <- paste(
getOption("aldatata.data_api_url"),
"&page=",
toString(page),
sep =""
)
}
else {
request_url <- paste(
getOption("aldatata.data_api_url"),
condition_text,
"&page=",
toString(page),
sep =""
)
}
return(request_url)
}
|
953c6a8fd2ff4b85a3e8ad59d8775418381d9f03 | d922758e6c9ac51cdbcfe25ff26a114d1635250c | /man/update.Rd | 038cfb049024280cd37be8632f717035e3b551e2 | [] | no_license | jeffreyhanson/raptr | c7fa50617b080a72f8fe97c9534a664cc02d91b9 | 059a1abc9c2e2f071ce2a7d3596bfd1189441d92 | refs/heads/master | 2023-08-31T22:13:12.446845 | 2023-03-14T03:26:50 | 2023-03-14T03:26:50 | 44,244,406 | 5 | 0 | null | 2023-08-21T23:56:38 | 2015-10-14T11:51:44 | R | UTF-8 | R | false | true | 6,325 | rd | update.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/GurobiOpts.R, R/ManualOpts.R,
% R/RapData.R, R/RapReliableOpts.R, R/RapUnreliableOpts.R, R/RapSolved.R
\name{update}
\alias{update}
\alias{update.GurobiOpts}
\alias{update.ManualOpts}
\alias{update.RapData}
\alias{update.RapReliableOpts}
\alias{update.RapUnreliableOpts}
\alias{update.RapUnsolOrSol}
\title{Update object}
\usage{
\method{update}{GurobiOpts}(
object,
Threads = NULL,
MIPGap = NULL,
Method = NULL,
Presolve = NULL,
TimeLimit = NULL,
NumberSolutions = NULL,
MultipleSolutionsMethod = NULL,
NumericFocus = NULL,
...
)
\method{update}{ManualOpts}(object, NumberSolutions = NULL, ...)
\method{update}{RapData}(
object,
species = NULL,
space = NULL,
name = NULL,
amount.target = NULL,
space.target = NULL,
pu = NULL,
cost = NULL,
status = NULL,
...
)
\method{update}{RapReliableOpts}(object, BLM = NULL, failure.multiplier = NULL, max.r.level = NULL, ...)
\method{update}{RapUnreliableOpts}(object, BLM = NULL, ...)
\method{update}{RapUnsolOrSol}(object, ..., formulation = NULL, solve = TRUE)
}
\arguments{
\item{object}{\code{\link[=GurobiOpts]{GurobiOpts()}}, \code{\link[=RapUnreliableOpts]{RapUnreliableOpts()}},
\code{\link[=RapReliableOpts]{RapReliableOpts()}}, \code{\link[=RapData]{RapData()}},
\code{\link[=RapUnsolved]{RapUnsolved()}}, or \code{\link[=RapSolved]{RapSolved()}} object.}
\item{Threads}{\code{integer} number of cores to use for processing.}
\item{MIPGap}{\code{numeric} MIP gap specifying minimum solution quality.}
\item{Method}{\code{integer} Algorithm to use for solving model.}
\item{Presolve}{\code{integer} code for level of computation in presolve.}
\item{TimeLimit}{\code{integer} number of seconds to allow for solving.}
\item{NumberSolutions}{\code{integer} number of solutions to generate.}
\item{MultipleSolutionsMethod}{\code{integer} name of method to obtain
multiple solutions (used when \code{NumberSolutions} is greater than one).
Available options are \code{"benders.cuts"}, \code{"solution.pool.0"},
\code{"solution.pool.1"}, and \code{"solution.pool.2"}. The
\code{"benders.cuts"} method produces a set of distinct solutions that
are all within the optimality gap. The \code{"solution.pool.0"}
method returns all solutions identified whilst trying to find
a solution that is within the specified optimality gap. The
\code{"solution.pool.1"} method finds one solution within the optimality
gap and a number of additional solutions that are of any level of quality
(such that the total number of solutions is equal to
\code{number_solutions}). The \code{"solution.pool.2"} finds a
specified number of solutions that are nearest to optimality. The
search pool methods correspond to the parameters used by the Gurobi
software suite (see \url{https://www.gurobi.com/documentation/8.0/refman/poolsearchmode.html#parameter:PoolSearchMode}).
Defaults to \code{"benders.cuts"}.}
\item{NumericFocus}{\code{integer} how much effort should Gurobi focus on
addressing numerical issues? Defaults to \code{0L} such that minimal effort
is spent to reduce run time.}
\item{...}{parameters passed to \code{\link[=update.RapReliableOpts]{update.RapReliableOpts()}},
\code{\link[=update.RapUnreliableOpts]{update.RapUnreliableOpts()}}, or \code{\link[=update.RapData]{update.RapData()}}.}
\item{species}{\code{integer} or \code{character} denoting species for which
targets or name should be updated.}
\item{space}{\code{integer} denoting space for which targets should be
updated.}
\item{name}{\code{character} to rename species.}
\item{amount.target}{\code{numeric} vector for new area targets (\%) for the
specified species.}
\item{space.target}{\code{numeric} vector for new attribute space targets
(\%) for the specified species and attribute spaces.}
\item{pu}{\code{integer} planning unit indices that need to be updated.}
\item{cost}{\code{numeric} new costs for specified planning units.}
\item{status}{\code{integer} new statuses for specified planning units.}
\item{BLM}{\code{numeric} boundary length modifier.}
\item{failure.multiplier}{\code{numeric} multiplier for failure planning
unit.}
\item{max.r.level}{\code{numeric} maximum R failure level for approximation.}
\item{formulation}{\code{character} indicating new problem formulation to
use. This can be either "unreliable" or "reliable". The default is
\code{NULL} so that formulation in \code{object} is used.}
\item{solve}{\code{logical} should the problem be solved? This argument is
only valid for \code{\link[=RapUnsolved]{RapUnsolved()}} and \code{\link[=RapSolved]{RapSolved()}}
objects. Defaults to \code{TRUE}.}
}
\value{
\linkS4class{GurobiOpts},
\linkS4class{RapUnreliableOpts},
\linkS4class{RapReliableOpts}, \linkS4class{RapData},
\linkS4class{RapUnsolved}, or \linkS4class{RapSolved} object
depending on argument to \code{x}.
}
\description{
This function updates parameters or data stored in an existing
\code{\link[=GurobiOpts]{GurobiOpts()}}, \code{\link[=RapUnreliableOpts]{RapUnreliableOpts()}},
\code{\link[=RapReliableOpts]{RapReliableOpts()}}, \code{\link[=RapData]{RapData()}},
\code{\link[=RapUnsolved]{RapUnsolved()}}, or \code{\link[=RapSolved]{RapSolved()}} object.
}
\examples{
\dontrun{
# load data
data(sim_ru, sim_rs)
# GurobiOpts
x <- GurobiOpts(MIPGap = 0.7)
y <- update(x, MIPGap = 0.1)
print(x)
print(y)
# RapUnreliableOpts
x <- RapUnreliableOpts(BLM = 10)
y <- update(x, BLM = 2)
print(x)
print(y)
# RapReliableOpts
x <- RapReliableOpts(failure.multiplier = 2)
y <- update(x, failure.multiplier = 4)
print(x)
print(y)
# RapData
x <- sim_ru@data
y <- update(x, space.target = c(0.4, 0.7, 0.1))
print(space.target(x))
print(space.target(y))
## RapUnsolved
x <- sim_ru
y <- update(x, amount.target = c(0.1, 0.2, 0.3), BLM = 3, solve = FALSE)
print(x@opts@BLM); print(amount.target(x))
print(y@opts@BLM); print(space.target(y))
## RapSolved
x <- sim_rs
y <- update(x, space.target = c(0.4, 0.6, 0.9), BLM = 100, Presolve = 1L,
solve = FALSE)
print(x@opts@BLM); print(amount.target(x))
print(y@opts@BLM); print(space.target(y))
}
}
\seealso{
\linkS4class{GurobiOpts},
\linkS4class{RapUnreliableOpts},
\linkS4class{RapReliableOpts}, \linkS4class{RapData},
\linkS4class{RapUnsolved}, \linkS4class{RapSolved}.
}
|
14db5f8d1387f872ff224de2948d5449c2b42ee4 | b77b91dd5ee0f13a73c6225fabc7e588b953842b | /ev_prep_scripts/ds_dn_3_paml_parse.R | c175f91ffccad01cf3c2266197dcac794a0c2b13 | [
"MIT"
] | permissive | ksamuk/gene_flow_linkage | a1264979e28b61f09808f864d5fa6c75568147b0 | 6182c3d591a362407e624b3ba87403a307315f2d | refs/heads/master | 2021-01-18T09:18:02.904770 | 2017-04-02T16:51:40 | 2017-04-02T16:51:40 | 47,041,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,353 | r | ds_dn_3_paml_parse.R | ########parse paml file to data frame
########new approach (uses raw paml files to avoid shell script shenanigans)
library(ape)
library(biomaRt)
library(dplyr)
library(magrittr)
home.dir<-"~/Documents/Science/Projects/Ph.D./Genome Meta Analysis/ev_prep_scripts/paml_analysis"
#home.dir<-"~/review/analysis/gma/ev_prep_scripts/paml_analysis"
#the output directory (window evs)
out.dir<-"~/Documents/Science/Projects/Ph.D./Genome Meta Analysis/evs/window"
#contains pre-processed paml output (the dn and ds tree lines+first file of paml file)
paml.output.dir<-file.path(home.dir,"alignments_all")
setwd(paml.output.dir)
#dat file list
file.list<-list.files()
#prep output data frame
gene.id<-vector(mode="character",length=length(file.list))
ds<-vector(mode="numeric",length=length(file.list))
dn<-vector(mode="numeric",length=length(file.list))
num.sites<-vector(mode="numeric",length=length(file.list))
gacu.dnds<-data.frame(gene.id,ds,dn,num.sites,stringsAsFactors=FALSE)
#loop through files and extract ds info
for (i in 1:length(file.list)){
#read in file
file.con<-file(file.list[i])
file.lines<-readLines(file.con)
closeAllConnections()
#find the "ds tree" line
ds.tree.line<-grep("dS tree",file.lines)
#find the "After deleting gaps. " line (for filtering bad alignments)
gaps.line<-grep("After deleting gaps. ",file.lines)
num.sites<-as.numeric(unlist(strsplit(file.lines[gaps.line],split=" "))[4])
#edge case where there are no alignment gaps
if(length(num.sites)==0){
num.sites<-as.numeric(unlist(strsplit(file.lines[1],split=" ")))
num.sites<-num.sites[length(num.sites)]
}
gacu.dnds$num.sites[i]<-num.sites
#find gene name from the file name
gene.name<-sapply(strsplit(file.list[i],split=".cml"),function(x)x[1])
if(gene.name==""){
print(file.list[i]," has no gene name?!")
}
gacu.dnds$gene.id[i]<-gene.name
#if no ds tree or target gene, skip file
#uncomment for commentary on the quality of your data files
if(length(ds.tree.line)==0){
#print(paste(file.list[i],"is missing dS tree."))
gacu.dnds$ds[i]<-NA
gacu.dnds$dn[i]<-NA
}else if(length(grep(paste(gene.name,":",sep=""),file.lines))==0){
#print(paste(file.list[i],"has a dS tree, but is missing the target gene."))
gacu.dnds$ds[i]<-NA
gacu.dnds$dn[i]<-NA
}else{
#make the tree(s)
ds.tree<-read.tree(text=file.lines[ds.tree.line+1])
dn.tree<-read.tree(text=file.lines[ds.tree.line+3])
#if there is no dn or ds value, assign NA, otherwise grab value from tree
if(is.null(ds.tree$edge.length[which.edge(ds.tree,gene.name)])){
gacu.dnds$ds[i]<-NA
}else{
gacu.dnds$ds[i]<-ds.tree$edge.length[which.edge(ds.tree,gene.name)]
}
if(is.null(dn.tree$edge.length[which.edge(dn.tree,gene.name)])){
gacu.dnds$dn[i]<-NA
}else{
gacu.dnds$dn[i]<-dn.tree$edge.length[which.edge(dn.tree,gene.name)]
}
#progress bar
if (i%%1000==0){
cat(i,"...",sep="")
}
}
}
#remove lines containing NAs
gacu.dnds<-gacu.dnds[complete.cases(gacu.dnds),]
##match gene.ids to genomic coordinates
#initialize gacu ensembl
ensembl <- useMart("ensembl",dataset="gaculeatus_gene_ensembl")
#the attributes of interest
attributes.feat<-c("ensembl_gene_id",
"ensembl_peptide_id",
"start_position",
"end_position",
"chromosome_name")
#query ensembl for coordinates
coords<-getBM(attributes=attributes.feat,values=gacu.dnds$gene.id,filters=c("ensembl_peptide_id"),mart = ensembl)
#for easier viewing
coords<-arrange(coords,ensembl_peptide_id)
#match in ds/dn values (could be a left_join, whateves)
ds.out<-gacu.dnds$ds[match(coords$ensembl_peptide_id,gacu.dnds$gene.id)]
dn.out<-gacu.dnds$dn[match(coords$ensembl_peptide_id,gacu.dnds$gene.id)]
#build prelim output file
gacu.out<-data.frame(gene.id=coords$ensembl_gene_id,
peptide.id=coords$ensembl_peptide_id,
lg=coords$chromosome_name,
pos1=coords$start_position,
pos2=coords$end_position,
ds=ds.out,
dn=dn.out,
sites=num.sites)
#if there are multiple peptides from a single gene (~30% of data), take the mean of the ds values
out.means<-gacu.out%>%
group_by(gene.id)%>%
summarise(mean(ds),mean(dn))
out.means<-data.frame(out.means)
names(out.means)<-c("gene.id","ds.mean","dn.mean")
#join in means with rest of data
gacu.out.2<-left_join(gacu.out,out.means)
#remove scaffolds
gacu.out.2<-gacu.out[grep("group",gacu.out$lg),]
#convert lg to numeric
gacu.out.2$lg<-as.numeric(as.roman(sapply(strsplit(as.character(gacu.out.2$lg),"group"),function(x)x[2])))
#arrange
gacu.out.2<-arrange(gacu.out.2,lg)
#FILTERING: no dn/dsvalues above 2 (from literature), no genes with <=100 sites
gacu.out.2<-gacu.out.2[gacu.out.2$sites>=100,]
gacu.out.2<-gacu.out.2[gacu.out.2$dn<=2,]
gacu.out.2<-gacu.out.2[gacu.out.2$ds<=2,]
#prep output files (strip duplicates)
gacu.out.ds<-gacu.out.2[,3:6]
gacu.out.ds<-unique(gacu.out.ds)
gacu.out.dn<-gacu.out.2[,c(3:5,7)]
gacu.out.dn<-unique(gacu.out.dn)
#write to file
setwd(out.dir)
write.table(gacu.out.ds,file="ds.txt",row.names=FALSE)
write.table(gacu.out.dn,file="dn.txt",row.names=FALSE)
|
8fe7ae1f9f946dbb648b5dd968a5d3de1ee1fc60 | 6f10f8643a261334c1e1b2b534c6e433b1100a53 | /plot4.R | 90122a6694a948da002203ae16f87b4f34f9fe5d | [] | no_license | JustinAbbottcs/ExData_Plotting1 | 772d4dcf449eb516f10ffcd174f6ce8107d599b0 | fe1672482773d7b62e78d1839b65998b4e8620ec | refs/heads/master | 2020-09-15T16:25:34.019867 | 2019-11-24T00:39:26 | 2019-11-24T00:39:26 | 223,502,775 | 0 | 0 | null | 2019-11-22T23:27:30 | 2019-11-22T23:27:29 | null | UTF-8 | R | false | false | 1,677 | r | plot4.R | library(tidyverse)
##Read in data from local file, limiting rows read to preserve memory
hpc_data <- read.table("../household_power_consumption.txt", header = TRUE, sep = ";", nrows = 500000)
##Data cleaning
hpc_data <- hpc_data %>%
mutate(Date=as.Date(hpc_data$Date, "%d/%m/%Y")) %>%
mutate(Time=as.POSIXct(strptime(paste(Date, Time), format = "%Y-%m-%d %H:%M:%S"))) %>%
filter(Date == "2007-02-01" | Date == "2007-02-02")
hpc_data[3:9] <- sapply(hpc_data[3:9], function(x) as.numeric(as.character(x)))
#Initializes 4 graphs and subsequently adds to each to write to pdf
png("plot4.png")
par(mfcol = c(2, 2))
with(hpc_data, {
#1
plot(y = hpc_data$Global_active_power,
x = hpc_data$Time,
xlab = "",
ylab = "Global Active Power",
type = "l")
#2
with(hpc_data, plot(Time, Sub_metering_1,
ylim = c(0,40),
type = "l",
xlab = "",
ylab = "Energy sub metering"))
with(hpc_data, lines(Time, Sub_metering_2, col = "red"))
with(hpc_data, lines(Time, Sub_metering_3, col = "blue"))
with(hpc_data, legend("topright",
col = c("black", "red", "blue"),
lty = c(1, 1, 1),
legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")))
#3
plot(y = hpc_data$Voltage,
x = hpc_data$Time,
xlab = "datetime",
ylab = "Voltage",
type = "l")
#4
plot(y = hpc_data$Global_reactive_power,
x = hpc_data$Time,
xlab = "datetime",
ylab = "Global_reactive_power",
type = "l")
})
#Write to and close png file
dev.off()
|
c4e1458347eaf44cbf3d260a64cabeeef2a4fec8 | 410813638cfe015b49c2396634207545445ff35b | /str/BB Calibrado/SimPiPS_BB_v1 .r | aab77504f9186756d0389d016a28f27695517887 | [] | no_license | stalynGuerrero/InferenciaBootstrapBayesiana | dda9335299d910b0f94f4139cc93f6fde1086c4f | ed208199546820edc7df7b0c0481aab89dbd09a4 | refs/heads/master | 2021-01-19T04:58:01.793901 | 2017-08-04T13:56:26 | 2017-08-04T13:56:26 | 60,229,312 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,368 | r | SimPiPS_BB_v1 .r | #############################################################################
rm(list = ls())
options(digits = 10)
dirPath<-"/home/sguerrero/Documentos/Dropbox/articulos/Artículo Proporción bayesiana/InferenciaBootstrapBayesiana/Algoritmo prueba/InferenciaBootstrapBayesiana"
setwd(dirPath)
#############################################################################
require(TeachingSampling)
require(hdrcde)
require(cubature)
source("str/Funciones Comunes/SimMASGamma.r")
source("str/Funciones Comunes/VeroBootPiPS.r")
source("str/Funciones Comunes/Medida.Calidad.r")
#############################################################################
# Definir función para la simulación
SimHT <- function(Pob, n, apriori = "unif", k) {
ty<-sum(Pob[,"Y"])
sel<-S.piPS(n,Pob[,"X"])
pik<-sel[,"Pik.s"]
sel<-sel[,"samp"]
ys<-Pob[sel,"Y"]
Vero <- VeroBootPiPS(ys,pik,ty)
if (apriori == "gamma") {
Aprio = dgamma(Vero$x, ty ^ 2 / (k), ty / (k))
nombre<-paste0("Vero,",k,apriori)
}
if (apriori == "unif") {
Aprio = dunif(Vero$x, min = 0, max = 10 ^ 10)
nombre<-paste0("Vero,",apriori)
}
if (apriori == "normal") {
Aprio = dnorm(Vero$x, mean = ty, sd = k)
nombre<-paste0("Vero,",k,apriori)
}
# par(mfrow=c(1,2))
# plot(Vero,type = "l",main = nombre)
# abline(v=ty)
post = Vero$y * Aprio
if(sum(post)==0){post=dunif(Vero$x, min = min(Vero$x), max = max(Vero$x))}
Vero$x <- Vero$x[post>0]
Vero$y <- Vero$y[post>0]
post <- post[post>0]
Fxpost <- approxfun(Vero$x,post)
q0.001<-as.numeric(quantile(Vero$x,probs = 0.05))
q0.999<-as.numeric(quantile(Vero$x,probs = 0.95))
rejilla<-seq(q0.001,q0.999,by=1)
muesb = sample(rejilla, 1000, prob =Fxpost(rejilla), replace = T)
# plot(x = rejilla,y=Fxpost(rejilla),type = "l",main = "Pos")
# abline(v=ty)
# points(x =muesb,Fxpost(muesb),pch=20)
# hist(muesb)
CV = 1000 * sd(muesb) / mean(muesb)
IC = hdr(muesb, 95)$hdr # intervalo de credibilidad
c(cont = ifelse(ty > IC[1] & ty < IC[2], 1, 0),
Longitud = IC[2] - IC[1], # Longitud del intervalo
hat.ty = mean(muesb), # Estimador de calibration
Sesgo = mean(muesb) - ty, # Sesgo de la estimaci'on
CV = CV ) # Coeficiente de variaci'on estimado
}
#############################################################################
# Inicializar las variables
N=20000
shape=4
rate=1
#############################################################################
# Crear escenario
n=c(50,400,1000)
sigma=c(74,13.7,3.6)
Escenarios<-expand.grid(n=n,sigma=sigma)
RsultMAS1<- data.frame(Coverage.100=NA,
Longitud.Relative.1000=NA,
Sesgo.Relative.1000=NA,
CV.1000=NA)
RsultMAS2<-RsultMAS1
RsultMAS3<-RsultMAS1
RsultMAS4<-RsultMAS1
RsultMAS5<-RsultMAS1
#############################################################################
n=50
sigma=74
for (i in 1:9) {
set.seed(1)
Pob<-SimMASGamma(N,shape,rate,Escenarios[i,"sigma"])
ResulSimU <-t(replicate(1000,SimHT(Pob,Escenarios[i,"n"])))
ResulSimN_N <-t(replicate(1000,SimHT(Pob,Escenarios[i,"n"],apriori = "normal",k=1000)))
ResulSimG_N <-t(replicate(1000,SimHT(Pob,Escenarios[i,"n"],apriori = "gamma",k=10000)))
ResulSimN <-t(replicate(1000,SimHT(Pob,Escenarios[i,"n"],apriori = "normal",k=10)))
ResulSimG <-t(replicate(1000,SimHT(Pob,Escenarios[i,"n"],apriori = "gamma",k=100)))
ty<-sum(Pob[,"Y"])
print(RsultMAS1[i,]<-Medida.Calidad(ResulSimU,ty))
print(RsultMAS2[i,]<-Medida.Calidad(ResulSimN_N,ty))
print(RsultMAS3[i,]<-Medida.Calidad(ResulSimG_N,ty))
print(RsultMAS4[i,]<-Medida.Calidad(ResulSimN,ty))
print(RsultMAS5[i,]<-Medida.Calidad(ResulSimG,ty))
}
RsultUNF<-cbind(Escenarios,RsultMAS1)
RsultNOR_N<-cbind(Escenarios,RsultMAS2)
RsultGAM_N<-cbind(Escenarios,RsultMAS3)
RsultNOR<-cbind(Escenarios,RsultMAS4)
RsultGAM<-cbind(Escenarios,RsultMAS5)
write.table(RsultUNF,"output/RsultUNFPiPS.txt",sep = "\t",dec = ".",row.names = FALSE)
write.table(RsultNOR_N,"output/RsultNOR_NPiPS.txt",sep = "\t",dec = ".",row.names = FALSE)
write.table(RsultGAM_N,"output/RsultGAM_NPiPS.txt",sep = "\t",dec = ".",row.names = FALSE)
write.table(RsultNOR,"output/RsultNORPiPS.txt",sep = "\t",dec = ".",row.names = FALSE)
write.table(RsultGAM,"output/RsultGAMPiPS.txt",sep = "\t",dec = ".",row.names = FALSE) |
2849c098065165d8784f179fc141bde63307396a | b229d2315a23155329ba1db5b87ea095b8beeeb3 | /man/get_closest_sim.Rd | 7892010faf3ea44979a1fa25ab0781031e12ed0a | [] | no_license | sbpdata/compost | 89cc14930181cdefb1f8f4e71d5c79d20baa9c32 | ab17b57aaaf1c433de60898e8ead836e737a0255 | refs/heads/master | 2020-08-05T17:11:49.400999 | 2019-11-03T12:28:57 | 2019-11-03T12:28:57 | 212,628,595 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 711 | rd | get_closest_sim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_closest_sim.R
\name{get_closest_sim}
\alias{get_closest_sim}
\title{Find highest similarity value in region}
\usage{
get_closest_sim(rca_mat, sim_mat)
}
\arguments{
\item{rca_mat}{binary rca matrix with region rows, units in columns}
\item{sim_mat}{similarity matrix with units on both dimensions.}
}
\value{
matrix with regions in rows, units in columns. The elements are
the highest similarity-value in the region to the given unit (column).
}
\description{
In the rca matrix, each region (row) has a number of
units (col) with the value 1. From these units, the function
finds the highest value of similarity to each unit.
}
|
96ba86221a982982a4d2681713b440b9fb080650 | 2dbd351ce22af3ca6889442138c1d2b7b82e2fda | /plot4.R | a8f7d569425fb499436085cbcd3ff2a0a499004f | [] | no_license | yashikabadaya/ExData_Plotting1 | 27bfb19cc322db47b9a0f75d37806447c67b7096 | bb54723b1d72b319d6729b8a495c5b4b52181c5c | refs/heads/master | 2020-04-27T21:55:01.482163 | 2019-03-27T11:27:54 | 2019-03-27T11:27:54 | 174,716,266 | 0 | 0 | null | 2019-03-09T16:11:33 | 2019-03-09T16:11:32 | null | UTF-8 | R | false | false | 1,138 | r | plot4.R | data <- read.table("household_power_consumption.txt", sep = ";" , header = TRUE, stringsAsFactors = FALSE , dec = ".", na.strings="?")
data$V1 <- as.Date(data$Date, format = "%d/%m/%Y")
data <- data[data$Date %in% c("1/2/2007","2/2/2007") , ]
date <- strptime(paste(data$Date, data$Time ,sep= " "), "%d/%m/%Y %H:%M:%S")
globalActivePower <- as.numeric(data$Global_active_power)
png("plot4.png", width = 480 , height = 480)
par(mfcol = c(2,2))
plot(date, globalActivePower, ylab = "Global Active Power (kilowatts)", type = "l", xlab = " " )
plot(date , data$Sub_metering_1, type = "l", ylab = "Energy sub metering", xlab = " ")
lines(date, data$Sub_metering_2 , type = "l", col = "red")
lines(date, data$Sub_metering_3 , type = "l" , col = "blue")
#lyt is for type of line and lwd defines the width of line
legend("topright", legend = c("Sub_metering_1" , "Sub_metering_2", "Sub_metering_3"), col = c("Black", "Red" , "Blue"), lty=1, lwd=1)
plot(date, data$Voltage, ylab = "Voltage", type = "l", xlab = "datetime")
plot(date, data$Global_reactive_power, ylab = "Global_reactive_power", type = "l", xlab = "datetime" )
dev.off() |
247e62e83d4596d1ed52222fdeea7c7cf822d09c | 6acadfa1d7455c004b5f395b767161375808a0c8 | /Scripts/complete.R | e7d4d434c09c044c37abb1b5d3f1b93ab0ffed22 | [] | no_license | guilhermeCoutinho/R-Programming | 9750b16c45c3318b87e92a5749e207ca96089428 | 75f61fb4ce9861a4ffc21159cd98bbd665073d6f | refs/heads/master | 2016-09-05T19:49:18.166687 | 2014-07-01T11:24:24 | 2014-07-01T11:24:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 504 | r | complete.R | complete = function (directory , id) {
ret = data.frame()
for (i in id) {
data = !is.na(read.csv( paste (directory, "\\" , dir(directory)[i] , sep="" )))
count = 0
for (j in 1:nrow(data)) {
if (data[j,2] == T & data[j,3] == T) {
count = count + 1
}
}
ret = rbind(ret , data.frame(i,count) )
}
names(ret) = c("id" , "nobs")
ret
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.