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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4e223b1f5b7326d4d81346a3b24954f23fbffeae
|
72d9009d19e92b721d5cc0e8f8045e1145921130
|
/terra/man/time.Rd
|
fcad8722c54b0cb64ad785a636049f2f895dcf04
|
[] |
no_license
|
akhikolla/TestedPackages-NoIssues
|
be46c49c0836b3f0cf60e247087089868adf7a62
|
eb8d498cc132def615c090941bc172e17fdce267
|
refs/heads/master
| 2023-03-01T09:10:17.227119
| 2021-01-25T19:44:44
| 2021-01-25T19:44:44
| 332,027,727
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 712
|
rd
|
time.Rd
|
\name{time}
\alias{time}
\alias{time<-}
\alias{time,SpatRaster-method}
\alias{time<-,SpatRaster-method}
\title{time of SpatRaster layers}
\description{
Get or set the time of the layers of a SpatRaster. Experimental. Currently only Date's allowed.
}
\usage{
\S4method{time}{SpatRaster}(x, ...)
\S4method{time}{SpatRaster}(x)<-value
}
\seealso{\code{\link{depth}}}
\arguments{
\item{x}{SpatRaster}
\item{value}{"Date", "POSIXt", or numeric}
\item{...}{additional arguments. None implemented}
}
\value{
Date
}
\examples{
s <- rast(system.file("ex/logo.tif", package="terra"))
time(s) <- as.Date("2001-05-04") + 0:2
time(s)
}
\keyword{spatial}
|
bd2737d4a78e583d69aa2dbb0d1c93f3a252ca15
|
adb6cc1648bb33b06e73be9dd35112d6f639b2db
|
/plot2.R
|
3d002f73d99ec4961c1f390aade368ba6024e315
|
[] |
no_license
|
ohjho/ExData_Plotting1
|
f5ccdaed99d27a4e86e9ced7a92a58735503472f
|
5a730c71728c644b9f1451ca5333f1c81e47b1a8
|
refs/heads/master
| 2021-05-31T13:22:23.532957
| 2016-04-18T09:52:20
| 2016-04-18T09:52:20
| 56,209,764
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 824
|
r
|
plot2.R
|
# Loading and cleaning Data
fname <- "household_power_consumption.txt"
if (!file.exists(fname)){
message(fname, " not found. Exiting script...")
stop("See information on the dataset from README.md or download here: https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip")
}
raw <- read.table(fname, header=TRUE,sep=";", na.string="?")
raw$Date <- as.Date(raw$Date, format="%d/%m/%Y")
energy <- subset(raw, Date >= "2007-02-01" & Date <= "2007-02-02")
rm(raw)
# Image Creation
message("Data loaded. Created Image...")
png(file="plot2.png",width=480, height=480)
with(energy,plot(as.POSIXct(paste(Date,Time)),Global_active_power,
ylab="Global Active Power (kilowatts)",
xlab="",
type="l")
)
dev.off()
rm(energy)
message("Successful.")
|
a83dec7bbc49c79dd3644db244321f3c33d1732c
|
63f78bd8589218f2b0317158acbeeec3c3c43340
|
/R/TIMEDEC/TIMEDEC.R
|
565de35c977e334152d5b84de08e12a3ac3685c0
|
[] |
no_license
|
Decision-Neuroscience-Lab/boPro
|
f462d94e9800ff6fb2df3ae3e73a55731c943b24
|
8e0f9dca1ea6999d9e6ca9cdacf2fbd45db0a53a
|
refs/heads/master
| 2020-05-27T21:15:41.627997
| 2017-03-02T06:51:03
| 2017-03-02T06:51:03
| 83,645,323
| 2
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,175
|
r
|
TIMEDEC.R
|
# TIMEDEC
## Bowen J Fung, 2015
# Repeated measures for feedback manipulation
feedMan <- read.csv("~/Documents/R/TIMEDEC/feedbackMan.csv")
# Subset to only ECG data
feedMan <- subset(feedMan, id < 121)
require(PMCMR)
h <- kruskal.test(k-k2 ~ as.factor(condition), data = feedMan)
posthoc.kruskal.nemenyi.test(k-k2 ~ as.factor(condition), data = feedMan, g = as.factor(condition), method="Tukey") # Find mean ranks in MATLAB
pairwise.wilcox.test((feedMan$k-feedMan$k2), g = as.factor(feedMan$condition), p.adj="none", exact=T)
# Test if 'overestimate' < 'underestimate' for increase (more negative) in k
s <- wilcox.test(feedMan$k[feedMan$condition == 2]-feedMan$k2[feedMan$condition == 2],
feedMan$k[feedMan$condition == 3]-feedMan$k2[feedMan$condition == 3])
# Test if 'control' < 'underestimate' for increase (more negative) in k
wilcox.test(feedMan$k[feedMan$condition == 4]-feedMan$k2[feedMan$condition == 4],
feedMan$k[feedMan$condition == 3]-feedMan$k2[feedMan$condition == 3])
# Test if 'control' > 'overestimate' for increase (more negative) in k
wilcox.test(feedMan$k[feedMan$condition == 4]-feedMan$k2[feedMan$condition == 4],
feedMan$k[feedMan$condition == 2]-feedMan$k2[feedMan$condition == 2])
# Correlations and multiple comparison corrections
data <- read.table("~/Documents/R/TIMEDEC/data.csv")
edr <- read.csv("/Users/Bowen/Documents/R/TIMEDEC/timedecKubios.csv")
names(edr)[1] <- "id"
edr <- edr[,c("id","edr")]
data <- merge(data,edr,by = "id", all.x = T, all.y = F)
# Physiological censoring
data$meanHR[data$meanHR == 0] <- NA
data$sdnn[data$sdnn == 0] = NA
data$sdnni[data$sdnni == 0] = NA
data$meanHR[data$meanHR > 140] = NA
data$sdnn[data$sdnn < 20] = NA
data$sdnni[data$sdnni < 20] = NA
data$hfpowfft[data$hfpowfft > 4000] = NA
data$lfpowfft[data$lfpowfft > 4000] = NA
data <- subset(data, filter == 1 & id < 121) # Subset to clean data
attach(data)
require(Hmisc)
require(corrgram)
require(ggplot2)
require(xtable)
require(ppcor)
source("/Users/Bowen/Documents/R/misc functions/corrstars.R")
source("/Users/Bowen/Documents/R/misc functions/xtable.decimal.R")
# DR and HR
vars <- c("meanDiff","cvReproduction","stevens1","stevens2","meanHR","sdnn","hfpowfft","lfpowfft")
temp <- data[vars]
corrgram(temp,order=TRUE,lower.panel = panel.ellipse, upper.panel = panel.pts, cor.method = "spearman")
corr <- rcorr(as.matrix(temp), type = "spearman")
fdrCorr <- apply(corr$P, 2, p.adjust, method = "holm") # Methods are bonferroni, holm, hochberg, hommel, BH, or BY
corrstars(as.matrix(temp), type = "spearman", method = "none")
xtable(corrstars(as.matrix(temp)))
# Control for respiration (by including heart rate)
require(ppcor)
vars <- c("stevens2","hfpowfft","meanHR")
temp <- data[vars]
temp <- temp[complete.cases(temp),]
pcor(temp, method = "spearman")
spcor(temp, method = "spearman")
# Control for respiration (by including peak HF)
vars <- c("stevens2","hfpowfft","hfpeakfft")
temp <- data[vars]
temp <- temp[complete.cases(temp),]
pcor(temp, method = "spearman")
spcor(temp, method = "spearman")
# Control for respiration (by including edr)
vars <- c("stevens2","hfpowfft","edr")
temp <- data[vars]
temp <- temp[complete.cases(temp),]
pcor(temp, method = "spearman")
spcor(temp, method = "spearman")
# Plots
ggplot(data, aes(x = stevens1, y = lfpowfft)) + geom_point(aes(color = stevens1)) + geom_smooth(method = "lm", se = TRUE)
write.table(round(corr$r, digits = 2), file = "~/Documents/R/TIMEDEC/DRHRcorrR.csv")
write.table(round(corr$P, digits = 3), file = "~/Documents/R/TIMEDEC/DRHRcorrP.csv")
write.table(round(fdrCorr,digits = 3), file = "~/Documents/R/TIMEDEC/DRHRcorrFDR.csv")
# TD and HR
vars <- c("bayesLogK","meanHR","sdnn","hfpowfft","lfpowfft")
temp2 <- data[vars]
corrgram(temp2,order=TRUE,lower.panel=panel.ellipse,cor.method = "spearman")
corr <- rcorr(as.matrix(temp2), type = "spearman")
fdrCorr <- apply(corr$P, 1, p.adjust, method = "BH") # Methods are bonferroni, holm, hochberg, hommel, BH, or BY
corrstars(as.matrix(temp2), type = "spearman", method = "none")
xtable(corrstars(as.matrix(temp2)))
write.table(round(corr$r, digits = 2), file = "~/Documents/R/TIMEDEC/TDHRcorrR.csv")
write.table(round(corr$P, digits = 3), file = "~/Documents/R/TIMEDEC/TDHRcorrP.csv")
write.table(round(fdrCorr,digits = 3), file = "~/Documents/R/TIMEDEC/TDHRcorrFDR.csv")
# Questionnaire and HR
vars <- c("meanHR","sdnn","sdnni","hfpowfft","lfpowfft","ipipN","ipipE","ipipO","ipipA","ipipC","BIS","BASdrive","BASfun","BASreward","zaubAUC")
temp3 <- data[vars]
corrgram(temp3,order=TRUE,lower.panel=panel.ellipse,cor.method = "spearman")
corr <- rcorr(as.matrix(temp3), type = "spearman")
fdrCorr <- apply(corr$P, 1, p.adjust, method = "BH") # Methods are bonferroni, holm, hochberg, hommel, BH, or BY
corrstars(as.matrix(temp3), type = "spearman", method = "none")
xtable.decimal(corrstars(as.matrix(temp3), type = "spearman", method = "none"))
write.table(round(corr$r, digits = 2), file = "~/Documents/R/TIMEDEC/QHRcorrR.csv")
write.table(round(corr$P, digits = 3), file = "~/Documents/R/TIMEDEC/QHRcorrP.csv")
write.table(round(fdrCorr,digits = 3), file = "~/Documents/R/TIMEDEC/QHRcorrFDR.csv")
# TD and AUC
vars <- c("k3","meanHR","zaubAUC")
temp4 <- data[vars]
corrgram(temp4,order=TRUE,lower.panel=panel.ellipse,cor.method = "spearman")
corr <- rcorr(as.matrix(temp4), type = "spearman")
fdrCorr <- apply(corr$P, 1, p.adjust, method = "BH") # Methods are bonferroni, holm, hochberg, hommel, BH, or BY
corrstars(as.matrix(temp4), type = "spearman", method = "none")
xtable(corrstars(as.matrix(temp4)))
# Questionnaire, TD, DR
vars <- c("k3","meanDiff","cvReproduction","stevens1","stevens2") #"bayesLogK","magEffect","zaubAUC")
temp5 <- data[vars]
corrgram(temp5,order=TRUE,lower.panel=panel.ellipse,cor.method = "spearman")
corr <- rcorr(as.matrix(temp5), type = "spearman")
fdrCorr <- apply(corr$P, 1, p.adjust, method = "BH") # Methods are bonferroni, holm, hochberg, hommel, BH, or BY
corrstars(as.matrix(temp5), type = "spearman", method = "none")
xtable.decimal(corrstars(as.matrix(temp5), type = "spearman", method = "none"))
## Correlation between carry-over effects and HR data
coefs <- read.csv("~/Documents/R/TIMEDEC/COcoefs.csv")
coefs <- subset(coefs,id %in% data$id) # Subset to clean data
data <- cbind(data,coefs)
vars <- c("sample","meanHR","sdnn","hfpowfft","lfpowfft")
temp6 <- data[vars]
corrgram(temp6,order=TRUE,lower.panel=panel.ellipse,cor.method = "spearman")
corr <- rcorr(as.matrix(temp6), type = "spearman")
fdrCorr <- apply(corr$P, 1, p.adjust, method = "BH") # Methods are bonferroni, holm, hochberg, hommel, BH, or BY
corrstars(as.matrix(temp6), type = "spearman", method = "none")
xtable.decimal(corrstars(as.matrix(temp6), type = "spearman", method = "none"))
# Partial correlations (control for respiration)
vars <- c("k3","stevens1","stevens2","meanDiff","meanHR","stevens1","stevens2","sdnn","hfpowfft","lfpowfft")
temp <- data[vars]
temp <- temp[complete.cases(temp),]
x <- temp$stevens2
y <- temp$hfpowfft
z <- temp$meanHR
pcor.test(x, y, z, method = "spearman")
spcor.test(x, y, z, method = "spearman")
detach(data)
# Check physiological data between feedback conditions
require(PMCMR)
kruskal.test(data$meanHR~ as.factor(condition), data = data)
kruskal.test(data$sdnn~ as.factor(condition), data = data)
kruskal.test(data$hfpowfft~ as.factor(condition), data = data)
kruskal.test(data$lfpowfft~ as.factor(condition), data = data)
posthoc.kruskal.nemenyi.test(data$meanHR ~ as.factor(condition), data = data, g = as.factor(condition), method="Tukey") # Find mean ranks in MATLAB
posthoc.kruskal.nemenyi.test(data$sdnn ~ as.factor(condition), data = data, g = as.factor(condition), method="Tukey") # Find mean ranks in MATLAB
posthoc.kruskal.nemenyi.test(data$hfpowfft ~ as.factor(condition), data = data, g = as.factor(condition), method="Tukey") # Find mean ranks in MATLAB
posthoc.kruskal.nemenyi.test(data$lfpowfft ~ as.factor(condition), data = data, g = as.factor(condition), method="Tukey") # Find mean ranks in MATLAB
pairwise.wilcox.test(data$meanHR, data$condition, p.adjust.method = "none", na.rm = T)
wilcox.test(data[condition == 3,"meanHR"], data[condition == 4,"meanHR"], p.adjust.method = "none", na.rm = T)
pairwise.wilcox.test(data$sdnn, data$condition, p.adjust.method = "none", na.rm = T)
pairwise.wilcox.test(data$hfpowfft, data$condition, p.adjust.method = "none", na.rm = T)
pairwise.wilcox.test(data$lfpowfft, data$condition, p.adjust.method = "none", na.rm = T)
wilcox.test(data[condition == 2,"lfpowfft"], data[condition == 3,"lfpowfft"], p.adjust.method = "none", na.rm = T)
# Control for feedback conditions
## Partial correlations
vars <- c("stevens1","lfpowfft","condition")
temp <- data[vars]
temp <- temp[complete.cases(temp),]
x <- temp$stevens1
y <- temp$lfpowfft
z <- temp$condition
pcor.test(x, y, z, method = "spearman")
rcorr(x,y, type = "spearman")
## Quantile regression
require("quantreg")
qs = 1:9/10
# Mean HR and discount rate
fit1 <- rq(k3 ~ meanHR, data = data, tau = qs)
summary(fit1, se = "nid")
plot(fit1)
fit2 <- rq(k3 ~ meanHR + as.factor(condition), data = data, tau = qs)
summary(fit2, se = "nid")
plot(fit2)
# HF and exponent
fit1 <- rq(stevens2 ~ hfpowfft, data = data, tau = 0.5)
summary(fit1, se = "nid")
fit2 <- rq(stevens2 ~ hfpowfft + as.factor(condition), data = data, tau = 0.5)
summary(fit2, se = "nid")
# LF and exponent
fit1 <- rq(stevens2 ~ lfpowfft, data = data, tau = 0.5)
summary(fit1, se = "nid")
fit2 <- rq(stevens2 ~ lfpowfft + as.factor(condition), data = data, tau = 0.5)
summary(fit2, se = "nid")
# LF and scale
fit1 <- rq(stevens1 ~ lfpowfft, data = data, tau = 0.6)
summary(fit1, se = "nid")
fit2 <- rq(stevens1 ~ lfpowfft + as.factor(condition), data = data, tau = 0.6)
summary(fit2, se = "nid")
# Figures
## Duration reproduction
timeSeries <- read.csv("~/Documents/R/TIMEDEC/timeSeries.csv", header = T)
intervals <- read.csv("~/Documents/R/TIMEDEC/intervals.csv", header = T)
kruskal.test(meanReproduction ~ as.factor(sampleInterval), data = intervals)
kruskal.test(meanDiff ~ as.factor(sampleInterval), data = intervals)
kruskal.test(stdReproduction ~ as.factor(sampleInterval), data = intervals)
kruskal.test(cvReproduction ~ as.factor(sampleInterval), data = intervals)
require("quantreg")
qs = 1:9/10
fit1 <- rq(diff ~ sample, data = timeSeries, tau = qs)
summary(fit1, se = "nid")
plot(fit1)
fit2 <- rq(k3 ~ meanHR + as.factor(condition), data = data, tau = qs)
summary(fit2, se = "nid")
plot(fit2)
attach(timeSeries)
require(ggplot2)
require(ggExtra)
plot_center = ggplot(timeSeries, aes(x=sample,y=reproduction)) + geom_point(aes(colour = factor(sample))) + stat_smooth(method = "lm", formula = y ~ poly(x,2), size = 1)
ggMarginal(plot_center, type="density", margins = "y")
## Regression
summary(lm(stevens2 ~ hfpowfft + lfpowfft, data = data))
# Grossman and Kollai (1993) suggestion (PNS activity only indexed by HF-HRV if HR taken into account)
summary(lm(stevens2 ~ hfpowfft, data = data))
summary(lm(stevens2 ~ hfpowfft+ meanHR, data = data))
# Effect sizes for some tests
cohensD(stevens2-1)
t.test(stevens2-1)
|
97793633733242c523763bb1762471f8f016a425
|
f2d61b91feef89fa7523e2fedd8a3a0461ef0cba
|
/R/wp/wp_film.R
|
fcbade88e2c08661a9dad6973df7c6edd26431d6
|
[] |
no_license
|
MarcinKosinski/trigeR5
|
c94f49476ecdf09da8277d5e81e7b2966b5642ce
|
1711e9b43a13d2b0073bd6e2cfbe967671dba427
|
refs/heads/master
| 2021-01-19T21:11:38.640114
| 2017-05-13T10:12:03
| 2017-05-13T10:12:03
| 88,619,642
| 4
| 11
| null | 2017-05-12T21:34:36
| 2017-04-18T11:56:39
|
JavaScript
|
UTF-8
|
R
| false
| false
| 1,941
|
r
|
wp_film.R
|
db <- dbConnect(drv = SQLite(), dbname = "data/wp.db")
#### FILM ####
adress <- "http://film.wp.pl/"
adresses <- adress %>%
read_html() %>%
html_nodes(css = "._1lXcfrU") %>%
html_text %>%
tolower() %>%
gsub("[[:space:][:punct:]]", "", .) %>%
chartr("ąćęłńóśźż", "acelnoszz", .) %>%
paste0(adress, .)
adresses <- adresses[-6]
links <- pblapply(adresses, function(one_ad) {
ad_html <- read_html(one_ad)
ad_nodes <- html_nodes(ad_html, css = "script")
ad_text <- ad_nodes %>%
grep("film.wp.pl", .) %>%
ad_nodes[.] %>%
as.character()
biggest_list <- ad_text %>%
stri_count_regex("film.wp.pl") %>%
which.max()
big_links <- biggest_list %>%
ad_text[.] %>%
stri_extract_all_regex("[[:alnum:]]+-[[:alnum:]-]+[[:digit:]]+[ag]")
big_links
}) %>%
unlist() %>%
unique %>%
paste0(adress, .)
# art_links <- links %>%
# grep("a$", ., value = TRUE)
# gal_links <- links %>%
# grep("g$", ., value = TRUE)
# Filter out galleries
links <- links %>%
grep("a$", ., value = TRUE)
# db <- dbConnect(drv = SQLite(), dbname = "../data/wp.db")
db_links <- dbGetQuery(db, "SELECT links FROM wp_film")
links <- setdiff(links, db_links$links)
bodies <- pblapply(links, function(link) {
tryCatch(link %>%
read_html() %>%
html_nodes(css = "p , ._1HGmjUl , ._1xAmRvR") %>%
html_text() %>%
paste0(collapse = " "),
error = function(e) {
"Hmm... Nie ma takiej strony."
})
}) %>%
unlist() %>%
gsub("'", "''", .)
wp_film <- data_frame(links = links, bodies = bodies) %>%
filter(bodies != "Hmm... Nie ma takiej strony.")
db_next <- "', '"
for (i in 1:nrow(wp_film)) {
dbGetQuery(db,
paste0("INSERT INTO wp_film (links, bodies) VALUES ('",
wp_film$links[i], db_next,
wp_film$bodies[i], "')"))
}
dbDisconnect(db)
update_csv('wp_film')
|
3f3b27619ecbb722e4389d242d16a05076c1c851
|
4f8a077dc78236d66b3b81569990f7eddb3d45c3
|
/h2o-r/tests/testdir_demos/runit_demo_glrm_walking_gait.R
|
3cfdf82c41cbddf73bca102ee978f3e3c6d02166
|
[
"Apache-2.0"
] |
permissive
|
konor/h2o-3
|
22b8e7c0e64597d18693f34a06079f242826cd92
|
77b27109c84c4739f9f1b7a3078f8992beefc813
|
refs/heads/master
| 2021-01-14T11:20:29.772798
| 2015-10-11T01:56:36
| 2015-10-11T01:56:36
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,001
|
r
|
runit_demo_glrm_walking_gait.R
|
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../h2o-runit.R')
# Connect to a cluster
# Set this to True if you want to fetch the data directly from S3.
# This is useful if your cluster is running in EC2.
data_source_is_s3 = F
locate_source <- function(s) {
if (data_source_is_s3)
myPath <- paste0("s3n://h2o-public-test-data/", s)
else
myPath <- locate(s)
}
test.walking_gait.demo <- function(conn) {
Log.info("Import and parse walking gait data...")
gait.hex <- h2o.importFile(locate("smalldata/glrm_test/subject01_walk1.csv"), destination_frame = "gait.hex")
print(summary(gait.hex))
Log.info("Basic GLRM using quadratic loss and no regularization (PCA)")
gait.glrm <- h2o.glrm(training_frame = gait.hex, x = 2:ncol(gait.hex), k = 5, init = "PlusPlus", loss = "Quadratic",
regularization_x = "None", regularization_y = "None", max_iterations = 1000)
print(gait.glrm)
Log.info("Archetype to feature mapping (Y):")
gait.y <- gait.glrm@model$archetypes
print(gait.y)
Log.info("Plot first archetype on z-coordinate features")
feat_cols <- seq(3, ncol(gait.y), by = 3)
plot(1:length(feat_cols), gait.y[1,feat_cols], xlab = "Feature", ylab = "Archetypal Weight", main = "First Archetype's Z-Coordinate Feature Weights", col = "blue", pch = 19, lty = "solid")
text(1:length(feat_cols), gait.y[1,feat_cols], labels = colnames(gait.y[1,feat_cols]), cex = 0.7, pos = 3)
abline(0, 0, lty = "dashed")
Log.info("Projection into archetype space (X):")
gait.x <- h2o.getFrame(gait.glrm@model$loading_key$name)
print(head(gait.x))
time.df <- as.data.frame(gait.hex$Time[1:150])[,1]
gait.x.df <- as.data.frame(gait.x[1:150,])
Log.info(paste0("Plot archetypes over time range [", time.df[1], ",", time.df[2], "]"))
matplot(time.df, gait.x.df, xlab = "Time", ylab = "Archetypal Projection", main = "Archetypes over Time", type = "l", lty = 1, col = 1:5)
legend("topright", legend = colnames(gait.x.df), col = 1:5, pch = 1)
# Log.info("Reconstruct data from matrix product XY")
# gait.pred <- predict(gait.glrm, gait.hex)
# print(head(gait.pred))
#
# Log.info(paste0("Plot original and reconstructed L.Acromium.X over time range [", time.df[1], ",", time.df[2], "]"))
# lacro.df <- as.data.frame(gait.hex$L.Acromium.X[1:150])
# lacro.pred.df <- as.data.frame(gait.pred$reconstr_L.Acromium.X[1:150])
# matplot(time.df, cbind(lacro.df, lacro.pred.df), xlab = "Time", ylab = "X-Coordinate of Left Acromium", main = "Position of Left Acromium over Time", type = "l", lty = 1, col = 1:2)
# legend("topright", legend = c("Original", "Reconstructed"), col = 1:2, pch = 1)
Log.info("Import and parse walking gait data with missing values...")
gait.miss <- h2o.importFile(locate("smalldata/glrm_test/subject01_walk1_miss15.csv"), destination_frame = "gait.miss")
print(summary(gait.miss))
Log.info("Basic GLRM using quadratic loss and no regularization (PCA)")
gait.glrm2 <- h2o.glrm(training_frame = gait.miss, validation_frame = gait.hex, x = 2:ncol(gait.miss), k = 15, init = "PlusPlus",
loss = "Quadratic", regularization_x = "None", regularization_y = "None", max_iterations = 500, min_step_size = 1e-7)
print(gait.glrm2)
Log.info("Impute missing data from X and Y")
gait.pred2 <- predict(gait.glrm2, gait.miss)
print(head(gait.pred2))
Log.info(paste0("Plot original and imputed L.Acromium.X over time range [", time.df[1], ",", time.df[2], "]"))
lacro.df2 <- as.data.frame(gait.hex$L.Acromium.X[1:150])
lacro.pred.df2 <- as.data.frame(gait.pred2$reconstr_L.Acromium.X[1:150])
matplot(time.df, cbind(lacro.df2, lacro.pred.df2), xlab = "Time", ylab = "X-Coordinate of Left Acromium", main = "Position of Left Acromium over Time", type = "l", lty = 1, col = 1:2)
legend("topright", legend = c("Original", "Imputed"), col = 1:2, pch = 1)
}
doTest("Test out Walking Gait Demo", test.walking_gait.demo)
|
920434b2ed55d90f8bdb9a65a29f2de6e5a0d3b0
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/LOGICOIL/examples/LOGICOILfit.Rd.R
|
0d9f51f439ea4959a0c4a08ad8653c8ad1d58216
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 272
|
r
|
LOGICOILfit.Rd.R
|
library(LOGICOIL)
### Name: LOGICOILfit
### Title: Fit of the multinomial log-linear model obtained from the
### LOGICOIL training dataset.
### Aliases: LOGICOILfit
### Keywords: datasets
### ** Examples
data(LOGICOILfit)
names(LOGICOILfit)
LOGICOILfit$coefnames
|
4aa0c34af98e037f8aa3f71b30cb3e7c8511af40
|
e09d229dd1ad18879fb051e4cb7d97c1475f49aa
|
/R/trace_backwards.R
|
bf1e8c2586a0df22a1815e13dbeaf1e4efc35061
|
[
"MIT"
] |
permissive
|
hamishgibbs/rtrackr
|
15bc922c8f8dfb765ee5b5da80df66b84eb16b16
|
2a353b73f8507e96c71c32c1ea557cfc04f9c0b2
|
refs/heads/master
| 2022-11-11T17:35:52.513669
| 2020-06-20T12:19:33
| 2020-06-20T12:19:33
| 271,510,902
| 1
| 0
|
NOASSERTION
| 2020-06-12T14:45:06
| 2020-06-11T09:54:51
|
R
|
UTF-8
|
R
| false
| false
| 756
|
r
|
trace_backwards.R
|
# trace_backwards
#
# @description recursively traverse a log file tree to identify parent nodes of a given trackr_id
#
# @param target_id string, a trackr_id
#
# @return list, the trackr_ids of parent record(s)
trace_backwards <- function(target_id){
parent_id <- target_id
parents <- list()
i = 1
while(length(parent_id) == 1){
prev_id <- parent_id
parent_id <- get_parent_backwards(parent_id)
parents[[i]] <- list(name = prev_id, children = parent_id, type = 'node')
i = i + 1
}
if(is.null(parents[[i - 1]]$children)){
parents[[i - 1]]$type = 'root'
parents <- parents[1:(i - 1)]
}
if(length(parents[[i - 1]]$children) > 1){
parents[[i - 1]]$type = 'break_point'
}
return(parents)
}
|
93e8075f55939bbee2fdec4ec001999ac8560c58
|
150ddbd54cf97ddf83f614e956f9f7133e9778c0
|
/R/avg.R
|
9d445f48f1bf7469c5aabf1231792268eb849743
|
[
"CC-BY-4.0"
] |
permissive
|
debruine/webmorphR
|
1119fd3bdca5be4049e8793075b409b7caa61aad
|
f46a9c8e1f1b5ecd89e8ca68bb6378f83f2e41cb
|
refs/heads/master
| 2023-04-14T22:37:58.281172
| 2022-08-14T12:26:57
| 2022-08-14T12:26:57
| 357,819,230
| 6
| 4
|
CC-BY-4.0
| 2023-02-23T04:56:01
| 2021-04-14T07:47:17
|
R
|
UTF-8
|
R
| false
| false
| 3,361
|
r
|
avg.R
|
#' Average Images
#'
#' Create an average from a list of delineated stimuli.
#'
#' @details
#'
#' ### Normalisation options
#'
#' * none: averages will have all coordinates as the mathematical average of the coordinates in the component templates
#' * twopoint: all images are first aligned to the 2 alignment points designated in `normpoint`. Their position is set to their position in the first image in stimuli
#' * rigid: procrustes aligns all images to the position of the first image in stimuli
#'
#' ### Texture
#'
#' This applies a representative texture to the average, resulting in composite images with more realistic texture instead of the very smooth, bland texture most other averaging programs create. See the papers below for methodological details.
#'
#' B. Tiddeman, M. Stirrat and D. Perrett (2005). Towards realism in facial prototyping: results of a wavelet MRF method. Theory and Practice of Computer Graphics.
#'
#' B. Tiddeman, D.M. Burt and D. Perrett (2001). Computer Graphics in Facial Perception Research. IEEE Computer Graphics and Applications, 21(5), 42-50.
#'
#' @param stimuli list of stimuli to average
#' @param texture logical; whether textured should be averaged
#' @param norm how to normalise; see Details
#' @param normpoint points for twopoint normalisation
#'
#' @return list of stimuli with the average image and template
#' @export
#' @family webmorph
#'
#' @examples
#' \donttest{
#' if (webmorph_up()) {
#' demo_stim() |> avg()
#' }
#' }
avg <- function(stimuli,
texture = TRUE,
norm = c("none", "twopoint", "rigid"),
normpoint = 0:1) {
stimuli <- require_tems(stimuli, TRUE)
if (length(stimuli) > 100) {
stop("We can't average more than 100 images at a time. You can create sub-averages with equal numbers of faces and average those together.")
}
if (!webmorph_up()) {
stop("Webmorph.org can't be reached. Check if you are connected to the internet.")
}
norm <- match.arg(norm)
format <- "jpg" #match.arg(format)
# save images locally
tdir <- tempfile()
files <- write_stim(stimuli, tdir, format = "jpg") |> unlist()
upload <- lapply(files, httr::upload_file)
names(upload) <- sprintf("upload[%d]", 0:(length(upload)-1))
settings <- list(
texture = ifelse(isTRUE(as.logical(texture)), "true", "false"),
norm = norm,
normPoint0 = normpoint[[1]],
normPoint1 = normpoint[[2]],
format = format
)
# send request to webmorph and handle zip file
ziptmp <- paste0(tdir, "/avg.zip")
httr::timeout(30 + 10*length(stimuli))
httr::set_config( httr::config( ssl_verifypeer = 0L ) )
url <- paste0(wm_opts("server"), "/scripts/webmorphR_avg")
r <- httr::POST(url, body = c(upload, settings) ,
httr::write_disk(ziptmp, TRUE))
utils::unzip(ziptmp, exdir = paste0(tdir, "/avg"))
#resp <- httr::content(r)
avg <- paste0(tdir, "/avg") |>
read_stim() |>
rename_stim("avg")
unlink(tdir, recursive = TRUE) # clean up temp directory
avg
}
#' Check if webmorph.org is available
#'
#' @export
#' @family webmorph
#' @examples
#' webmorph_up()
webmorph_up <- function() {
tryCatch({
paste0(wm_opts("server"), "/scripts/status") |>
httr::HEAD() |> httr::status_code() |> identical(200L)
}, error = function(e) {
return(FALSE)
})
}
|
22317d7d5f468b9f01d76e6704ee1951d1cae712
|
8327aedc9fca9c1d5f11c160d440ecc082fb915d
|
/man/per.Rd
|
5da25bae9fca76698a7ee2c4106a0dca850b47d7
|
[] |
no_license
|
SESjo/SES
|
f741a26e9e819eca8f37fab71c095a4310f14ed3
|
e0eb9a13f1846832db58fe246c45f107743dff49
|
refs/heads/master
| 2020-05-17T14:41:01.774764
| 2014-04-17T09:48:14
| 2014-04-17T09:48:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 953
|
rd
|
per.Rd
|
\name{per}
\alias{per}
\title{Decompose an atomic vector to its successive values and their length.}
\usage{
per(x, idx = FALSE)
}
\arguments{
\item{x}{The atomic vector to examine.}
\item{idx}{Should the indexes (start and end) of
homogeneous sequences be returned as well ?}
}
\value{
A data frame with values and lengths of the homogeneous
sequences of x. The class of the column 'value' is copied
from the input.
}
\description{
The reverse of 'base::rep()' function: decompose an atomic
vector to its successive values and their length.
}
\examples{
(x <- rep(LETTERS[1:10], 10:1))
(y <- per(x))
# 'per()' is the reverse of 'rep()'
# identical(rep(y$value, y$length), x) # TRUE
# Because characters are not converted to factors
# inherits(y$value, class(x)) # TRUE
}
\seealso{
Other generalUtils: \code{\link{SESname}};
\code{\link{convertTime}}; \code{\link{replaceMissing}};
\code{\link{sunPosition}}; \code{\link{sunPos}}
}
|
cd19b39a4fba632862748005ab8d96566406ab65
|
532d0fe3ec396c2898574944a66e3e43d750b4b9
|
/IslandOfLostScripts/cm_v1.R
|
5ff985c3469167341077bb883d39186d1d36d3fa
|
[] |
no_license
|
myellen/MF850_Computational_Finance_Final
|
6d91653abe90d48a84df5eca08ed178bab2870fe
|
7410931178707089e286867f9af49d7aee7f6434
|
refs/heads/master
| 2020-06-11T13:02:40.224997
| 2016-12-19T10:37:40
| 2016-12-19T10:37:40
| 75,659,108
| 0
| 1
| null | 2016-12-19T09:52:48
| 2016-12-05T19:32:11
|
R
|
UTF-8
|
R
| false
| false
| 4,576
|
r
|
cm_v1.R
|
<<<<<<< HEAD
## Load the data
mydata<-read.csv(file="mf850-finalproject-data.csv", header=TRUE, sep=",")
=======
library(glmnet)
##Read Test Data
mytestdata<-read.csv(file="/Users/leighm888/Desktop/Test_set_v2.csv", header=TRUE, sep=",")
#turn categorical variables into factors
date<-mytestdata[,1]
RETMONTH_SPX<-mytestdata[,2]
compid<-mytestdata[,3]
Close<-mytestdata[,4]
Adj_Close<-mytestdata[,5]
Volume<-mytestdata[,6]
Adj_Volume<-mytestdata[,7]
Industry<-mytestdata[,8]
RETMONTH<-mytestdata[,9]
MARKETCAP<-mytestdata[,10]
REVENUEUSD<-mytestdata[,11]
COR<-mytestdata[,12]
GP<-mytestdata[,13]
RND<-mytestdata[,14]
SGNA<-mytestdata[,15]
OPEX<-mytestdata[,16]
OPINC<-mytestdata[,17]
EBITUSD<-mytestdata[,18]
INTEXP<-mytestdata[,19]
TAXEXP<-mytestdata[,20]
CONSOLINC<-mytestdata[,21]
NETINCNCI<-mytestdata[,22]
NETINC<-mytestdata[,23]
PREFDIVIS<-mytestdata[,24]
NETINCCMNUSD<-mytestdata[,25]
EPSUSD<-mytestdata[,26]
SHARESWA<-mytestdata[,27]
DPS<-mytestdata[,28]
DEPAMOR<-mytestdata[,29]
SBCOMP<-mytestdata[,30]
CAPEX<-mytestdata[,31]
NCF<-mytestdata[,32]
ASSETS<-mytestdata[,33]
CASHNEQUSD<-mytestdata[,34]
INVENTORY<-mytestdata[,35]
LIABILITIES<-mytestdata[,36]
DEBTUSD<-mytestdata[,37]
INVESTMENTS<-mytestdata[,38]
EQUITY<-mytestdata[,39]
BM<-mytestdata[,40]
SHARESBAS<-mytestdata[,41]
SHAREFACTOR<-mytestdata[,42]
TAXASSETS<-mytestdata[,43]
TAXLIABILITIES<-mytestdata[,44]
DIVYIELD<-mytestdata[,45]
EBITDAUSD<-mytestdata[,46]
EBITDAMARGIN<-mytestdata[,47]
DE<-mytestdata[,48]
EVEBIT<-mytestdata[,49]
EVEBITDA<-mytestdata[,50]
FCFPS<-mytestdata[,51]
GROSSMARGIN<-mytestdata[,52]
NETMARGIN<-mytestdata[,53]
PE<-mytestdata[,54]
PS<-mytestdata[,55]
PB<-mytestdata[,56]
ROIC<-mytestdata[,57]
SPS<-mytestdata[,58]
PAYOUTRATIO<-mytestdata[,59]
ROA<-mytestdata[,60]
ROE<-mytestdata[,61]
ROS<-mytestdata[,62]
mytestdata$Industry=as.factor(mytestdata$Industry)
contrasts(mytestdata$Industry) = contr.treatment(213)
mytestdata$industry.f[1:213]
linear_v1<-lm(RETMONTH ~ RETMONTH_SPX + Close + Adj_Close + Volume +
Adj_Volume + Industry + MARKETCAP + REVENUEUSD + COR +GP + RND+SGNA + OPEX + OPINC +
EBITUSD + INTEXP + TAXEXP + CONSOLINC + NETINCNCI + NETINC + PREFDIVIS+ NETINCCMNUSD +
EPSUSD + SHARESWA + DPS + DEPAMOR + SBCOMP + CAPEX + NCF + ASSETS + CASHNEQUSD +
INVENTORY + LIABILITIES + DEBTUSD + INVESTMENTS + EQUITY + BM + SHARESBAS + SHAREFACTOR +
TAXASSETS + TAXLIABILITIES + DIVYIELD + EBITDAUSD + EBITDAMARGIN + DE + EVEBIT + EVEBITDA +
FCFPS + GROSSMARGIN + NETMARGIN + PE +PS + PB +ROIC + SPS +PAYOUTRATIO + ROA + ROE + ROS)
summary(linear_v1)
##Close, Adj_Close, Volume, Adj_Volume, Industry 48, 83,138,139,149,EPSUSD,
##SHARESWA,SHARESBAS,DIVYIELD,DE,NETMARGIN,PB,ROIC,SPS,ROA
linear_sig<-lm(RETMONTH ~ RETMONTH_SPX + Close + Adj_Close + Volume + Adj_Volume + Industry + EPSUSD +
SHARESWA + SHARESBAS + DIVYIELD + DE + NETMARGIN + PB + ROIC +
SPS + ROA)
#define matrix of explanatory variables
exp_var<-mytestdata[,c(2,4:8,10:62)]
fit<-glmnet(exp_var,RETMONTH)
testdata_matrix<-data.matrix(exp_var)
fit=glmnet(testdata_matrix,RETMONTH)
x = model.matrix(RETMONTH~ RETMONTH_SPX + Close + Adj_Close + Volume +
Adj_Volume + Industry + MARKETCAP + REVENUEUSD + COR +GP + RND+SGNA + OPEX + OPINC +
EBITUSD + INTEXP + TAXEXP + CONSOLINC + NETINCNCI + NETINC + PREFDIVIS+ NETINCCMNUSD +
EPSUSD + SHARESWA + DPS + DEPAMOR + SBCOMP + CAPEX + NCF + ASSETS + CASHNEQUSD +
INVENTORY + LIABILITIES + DEBTUSD + INVESTMENTS + EQUITY + BM + SHARESBAS + SHAREFACTOR +
TAXASSETS + TAXLIABILITIES + DIVYIELD + EBITDAUSD + EBITDAMARGIN + DE + EVEBIT + EVEBITDA +
FCFPS + GROSSMARGIN + NETMARGIN + PE + PS + PB +ROIC + SPS +PAYOUTRATIO + ROA + ROE + ROS,
data = mytestdata)
lasso<-glmnet(x, RETMONTH,alpha=1)
ridge<-glmnet(x, RETMONTH,alpha=0)
##Industry, Close, Volume, Industry,opinc, netincnci,epsusd,ncf,bm,sharesbas,divyield,
## ebitdamargin, de, roic, sps, payoutratio, roa
linear_lasso<-lm(RETMONTH~Close + Volume + Industry + OPINC + NETINCNCI + EPSUSD
+ NCF + BM + SHARESBAS + DIVYIELD + EBITDAMARGIN + DE + ROIC +
SPS + PAYOUTRATIO + ROA)
test_set_cat<-read.csv(file="/Users/leighm888/Desktop/Test_set_cat.csv", header=TRUE, sep=",")
test_set_cat$Industry=as.factor(test_set_cat$Industry)
contrasts(test_set_cat$Industry) = contr.treatment(213)
y<-test_set_cat$RETMONTHCAT
linear_cat<-glm(y~.,data = test_set_cat)
>>>>>>> master
|
e190f56c61f4acf90546e56ed6023e1ffdf188b6
|
521fa790f4faa0d25d617bc40604a6bcbfa7e324
|
/code/define_couples_mutate.R
|
fe46761676b4ad69f7265e4c98f9e5e896dc0e93
|
[] |
no_license
|
CedricBezy/stat_sante_git
|
deaced07b3c21e6161611b2165ef432d66076120
|
7177aab8f1106fae54472de5779f631d419af2f5
|
refs/heads/master
| 2021-05-15T04:56:03.235445
| 2018-02-02T16:16:35
| 2018-02-02T16:16:35
| 118,431,016
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,227
|
r
|
define_couples_mutate.R
|
#---------------------------------------------------
# Cedric Bezy
# 25 / 01 / 2018
# Projet Stat Sante
#---------------------------------------------------
rm(list = ls())
library(dplyr)
library(tibble)
load('stat_sante_copy/data/couples_init.RData')
##==================================================
# Functions
##==================================================
contains_values <- function(text, vect){
any(sapply(vect, grepl, x = text))
}
count_na <- function(x){
sum(is.na(x))
}
na_barplot <- function(df){
nb_na <- sapply(df, count_na)
nb_na <- nb_na[which(nb_na != 0)]
if(length(nb_na)){
barplot(nb_na, main="Number of NA")
}
return(nb_na)
}
strfind <- function(x, vect, xsep = ";"){
return(any(strsplit(x, split = xsep)[[1]] %in% vect))
}
##================================================================
# Valeurs manquantes
##================================================================
##-----------------------------------.
# Bar_Plot
##-----------------------------------.
na_barplot(couples_init)
##-----------------------------------.
# Filter
##-----------------------------------.
couples <- couples_init %>%
dplyr::filter(
!is.na(bmi_h) & between(bmi_h, 15, 45),
!is.na(diplome_h),
!is.na(age_f)
)
# barplot
na_barplot(couples)
##================================================================.
# Creation de variables
##================================================================.
##-----------------------------------.
# Difference age
##-----------------------------------.
diff_age <- couples$age_h - couples$age_f
couples <- couples %>%
tibble::add_column(diff_age, .after = "fecondite")
##-----------------------------------.
# Duree Infertilite
##-----------------------------------.
duree_infertilite_class <- with(couples, {
cut(duree_infertilite,
breaks = c(0, 24, max(duree_infertilite, na.rm = TRUE) + 1),
labels = c("inf_24", "24_sup"),
include.lowest = FALSE,
right = TRUE,
ordered_result = FALSE
)
})
couples <- couples %>% add_column(duree_infertilite_class, .after = "duree_infertilite")
##-----------------------------------.
# BMI
##-----------------------------------.
# <16 : Anorexie ;
# 16 < Maigreur < 18,5 ;
# 18,5< normal < 25 ;
# 25< surpoids < 30 ;
# 30 < obese < 40 ;
# >40 massive
bmi_h_class_6 <- with(couples, {
cut(bmi_h,
breaks = c(10, 16, 18.5, 25, 30, 40, 60),
labels = c("Anorexie", "Maigreur", "Normal", "Surpoids", "Obese", "Massive"),
include.lowest = FALSE,
right = TRUE,
ordered_result = FALSE
)
})
bmi_h_class_2 <- with(couples, {
cut(bmi_h,
breaks = c(16, 25, 60),
labels = c("Normal", "Surpoids"),
include.lowest = FALSE,
right = TRUE,
ordered_result = FALSE
)
})
couples <- couples %>%
tibble::add_column(bmi_h_class_6, bmi_h_class_2, .after = "bmi_h")
##-----------------------------------.
# Pathologie Homme
##-----------------------------------.
patho_h <- couples$patho_h
patho_h <- gsub(" *, *", ",", patho_h)
patho_h <- gsub(" +", "_", patho_h)
patho_h <- gsub(",", ";", patho_h)
all_patho_h <- table(unlist(strsplit(patho_h, ";")))
all_patho_h
# [1] "non" "chimiotherapie"
# [3] "autre" "pathologies_respiratoire_chroniques"
# [5] "hodgkin" "radiotherapie"
# [7] "sinusites_chroniques" "diabete"
# [9] "cancer_testis" "sarcome"
# [11] "neurologique"
table(couples_init$patho_h)
# autre
# 227
# cancer testis , chimiotherapie
# 2
# chimiotherapie
# 5
# chimiotherapie , radiotherapie
# 2
# diabete
# 7
# hodgkin , chimiotherapie , radiotherapie
# 1
# neurologique
# 1
# non
# 842
# pathologies respiratoire chroniques
# 9
# sarcome , chimiotherapie
# 1
# sinusites chroniques
# 33
# sinusites chroniques , pathologies respiratoire chroniques
# 1
# Chimio
v_chimio <- c("chimiotherapie",
"cancer_testis",
"radiotherapie",
"hodgkin",
"sarcome")
v_chronic <- c("pathologies_respiratoire_chroniques",
"sinusites_chroniques",
"diabete")
v_autre <- setdiff(all_patho_h, c("non", v_chimio, v_chronic))
patho_h_bin <- factor(patho_h == 'non',
levels = c(FALSE, TRUE),
labels = c(0, 1))
x <- patho_h[832]
vect <- v_chimio
# Chimiotherapie
patho_h_regroup = factor(
ifelse(
patho_h == "non",
"non",
ifelse(
sapply(patho_h, strfind, vect = v_chimio, xsep = ";"),
"chimio",
ifelse(
sapply(patho_h, strfind, vect = v_chronic, xsep = ";"),
"chronic",
"autre"
)
)
),
levels = c("non", "chimio", "chronic", "autre")
)
table(patho_h_regroup)
couples <- couples %>%
dplyr::mutate(
patho_h = patho_h
) %>%
tibble::add_column(
patho_h_regroup,
patho_h_bin,
.after = "patho_h"
)
##-----------------------------------.
# Pathologie Femme
##-----------------------------------.
patho_f <- couples$patho_f
patho_f <- gsub(" *, *", ",", patho_f)
patho_f <- gsub(" +", "_", patho_f)
patho_f <- gsub(",", ", ", patho_f)
all_patho_f <- unique(unlist(strsplit(patho_f, ", ")))
all_patho_f
table(patho_f)
# autre endometriose hydrosalpinx
# 18 17 2
# non pb tubaire bilateral pb tubaire unilateral
# 647 14 65
patho_f_bin <- factor(
(patho_f == 'non'),
levels = c(FALSE, TRUE),
labels = c(0, 1)
)
patho_f_regroup <- factor(
ifelse(
test = is.na(patho_f),
yes = NA,
no = ifelse(
test = (patho_f %in% c("non", "endometriose")),
yes = patho_f,
no = ifelse(
test = grepl("tubaire", patho_f),
yes = "tubaire",
no = "autre"
)
)
),
levels = c("non", "endometriose", "tubaire", "autre")
)
summary(patho_f_regroup)
couples <- couples %>%
dplyr::mutate(
patho_f = patho_f
) %>%
tibble::add_column(
patho_f_regroup,
patho_f_bin,
.after = "patho_f"
)
##-----------------------------------.
# Bilan Femme
##-----------------------------------.
df_bilan <- couples %>%
dplyr::select(id, enfant, bh_f, ct_f, patho_f)
nb_na_bilan_f <- with(couples, is.na(bh_f) + is.na(ct_f) + is.na(patho_f))
complet_f = (nb_na_bilan_f == 0)
bilan_f <- with(couples, {
factor(
ifelse(
test = (nb_na_bilan_f == 3),
yes = NA,
no = ifelse(
test = (is.na(bh_f) | bh_f == "normal") &
(is.na(ct_f) | ct_f == "ovulation") &
(is.na(patho_f) | patho_f == "non"),
yes = 0,
no = 1
)
),
levels = c(1, 0),
labels = c("dysfonc", "normal")
)
})
## ADD TO couples
couples <- couples %>%
tibble::add_column(
bilan_f = bilan_f,
complet_f = complet_f,
.before = "bh_f"
)
##================================================================.
# remake couples
##================================================================.
couples <- droplevels(couples) %>%
tibble::add_column(
delta = with(couples, {
ifelse(!is.na(dconception),
dconception - dconsultation,
ddn - dconsultation)
}),
.after = "ddn"
)
##================================================================.
# Save
##================================================================.
save(couples, file = 'stat_sante_copy/data/couples.RData')
if(readline("Update Github data (y/n): ")%in% c("y", "1")){
save(couples, file = 'stat_sante_git/data/couples.RData')
message("Substitution of data : done")
}else{
message("No substitution of data")
}
|
e6889f727ba31e1f51f915a237ad0f85e7df5c6f
|
fd2bf6d71e00c84e16814fa8fc41c35d52e0752b
|
/plot-fdcs-w-err-bar-Function.R
|
bce8e78f76a615eb66cbe63b91f22aef6cb5fc3f
|
[] |
no_license
|
BTDangelo/Function-Archive
|
b9a6a5538e2dd56043b60d0ad7fe58286fb5ec9a
|
20cf00f19a5999ac4c6fab01f4cf68288fccd1b4
|
refs/heads/master
| 2020-12-02T18:04:59.106707
| 2017-08-09T20:05:06
| 2017-08-09T20:05:06
| 96,469,222
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,202
|
r
|
plot-fdcs-w-err-bar-Function.R
|
## BTD - Function to create flow duration curve with error bars
flow.d.c <- function(chr.dir.main, chr.dir.flow.data, chr.dir.figures, chr.eb) {
## chr.dir.main - path to main workspace
## chr.dir.flow.data - path to flow data
## chr.dir.figures - path to write the figures
## chr.eb - path to error bar data
library(ggplot2)
library(scales) ## BTD - Generic plot scaling nethods
library(tidyr) ## BTD - Makes it easy to "tidy" your data. Tidy data is data that's easy to work with.
library(Hmisc) ## BTD - Contains functions useful for data analysis, high-level graphics, etc.
library(plyr) ## BTD - Tools for splitting, applying, and combining data
library(dplyr) ## BTD - A fast, consistent tool for working with data frame like objects, both in and out of memory
## BTD - Read the error bar data into R
eb <- read.csv(file = chr.eb )
## BTD - Create unique combination of a set of vectors pertaining to each station number, each station number is a character vector
chr.stn.num <- as.character(unique(eb$station))
## BTD - Create a for loop for each station
for(j in 1:length(chr.stn.num)) {
## plot fdc for Upper Yaquina River Bacteria TMDL report document
options(stringsAsFactors = FALSE)
## flow data file
chr.file.flow.data <- paste0("flow_stn",chr.stn.num[j], ".txt")
## name of output figure file
chr.file.figure <- paste0("fdc-stn-", chr.stn.num[j], ".png")
## creat empty data frome for flow data
df.data.flow <- data.frame(stn = character(0), date = character(0),
flow_cfs = character(0))
## path and file name for flow data file
tmp.flow.file <- paste0(chr.dir.flow.data, "/", chr.file.flow.data)
## read the flow data file into R
tmp.data <-
read.table(file=tmp.flow.file,sep="\t",
header=TRUE, stringsAsFactors=FALSE, colClasses="character")
## populate the flow data frame with the flow data
df.data.flow <- data.frame(stn = tmp.data$stn,
date = as.POSIXct(strptime(tmp.data$date,
format = "%m-%d-%Y")),
flow_cfs = as.numeric(tmp.data$flow_cfs))
## replace NAs in flow data with nearest previous non-NA value
tmp.flow.no.na <- (df.data.flow %>% fill(flow_cfs))
df.data.flow <- cbind(df.data.flow,
value = tmp.flow.no.na$flow_cfs)
## create a column for the flow exceedance in the flow data frame
df.data.flow <- cbind(df.data.flow, flow.exceed = -1)
## create a function to calculate the flow exceedance
flow.exceed <- function(v.flow) {
tmp.rank <- rank(v.flow, ties.method = "average")
tmp.exceed <- tmp.rank / length(v.flow)
tmp.exceed <- 100 * (1 - tmp.exceed)
return(tmp.exceed)
}
## calculate the flow exceedance for the flow data
df.data.flow$flow.exceed <- flow.exceed(df.data.flow$value)
## make plots of fdc
## dynamically calculate the breaks for the numbers on the y-axis
tmp.breaks <- 10^seq(from = floor(log10(min(df.data.flow$value,
na.rm = TRUE))),
to = ceiling(log10(max(df.data.flow$value,
na.rm = TRUE))),
length.out = 5)
fancy_scientific <- function(l) {
## function taken from stackoverflow.com post
## http://stackoverflow.com/questions/11610377/how-do-i-change-the-formatting-of-numbers-on-an-axis-with-ggplot/24241954
# turn in to character string in scientific notation
x <- format(l, scientific = TRUE)
# quote the part before the exponent to keep all the digits
#y <- gsub("^(.*)e", "'\\1'e", x)
# turn the 'e+' into plotmath format
z <- gsub("^.*e", "10^", x)
# return this as an expression
parse(text=z)
}
## add the fdc to the plot and set how the axes will appear
p1 <- ggplot(data = df.data.flow) +
geom_line(aes(x = flow.exceed, y = value),
color = "blue", size = 1.5) +
scale_y_log10(limits = range(tmp.breaks), breaks = tmp.breaks,
minor_breaks = c(sapply(tmp.breaks, function(x) seq(0, x, x/10))),
labels = fancy_scientific) +
scale_x_continuous(limits = c(0, 100), expand = c(0,0)) +
labs(
x = "Flow Exceedance (%)",
y = "Average Daily Flow (cfs)"
)
## set the appeareance of the grid lines and the text in the figure
p2 <- p1 +
theme(
axis.title = element_text(size = 10, color = "black"),
axis.text = element_text(size = 8, color = "black"),
panel.grid.major = element_line(colour = "grey60"),
panel.grid.minor = element_line(colour = "grey60"),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(colour = "black", fill=NA, size=1)
)
## now add information dynamically for the flowzone boundaires
## flowzone descriptions
chr.flz.desc <- c("High Flows", "Transitional Flows", "Typical Flows",
"Dry Flows", "Low Flows")
## flowzone for exceedance boundaries
num.flow.exceed.bnd <- c(10,40,60,90)
## add lines for flowzone boundaries
p3 <- p2 +
geom_vline(xintercept = num.flow.exceed.bnd, size = 1.5,
linetype = "dashed")
## adding text for flowzone boundaires
## get the mid-pojnts of the x-values of the flowzones
junk <- c(0, num.flow.exceed.bnd, 100)
junk.mid <- c()
for(ii in 2:length(junk)) {
junk.mid <- c(junk.mid, junk[ii-1] + (junk[ii] - junk[ii-1]) / 2)
}
num.flow.exceed.bnd.mids <- junk.mid
rm(junk, junk.mid)
## create a data frame that has the x and y values along with the labels for the flowzone boundaries
df.fz.lables <- data.frame(x = num.flow.exceed.bnd.mids,
y = 10^ (rep(1, length(num.flow.exceed.bnd.mids))* min(p3$scales$scales[[1]]$limits) +
0.015 * (p3$scales$scales[[1]]$limits[2] - p3$scales$scales[[1]]$limits[1])),
chr.label = chr.flz.desc)
## add flowzone text to the fdc plot
p4 <- p3 + geom_text(data = df.fz.lables,
aes(x = x, y = y, label = chr.label),
size = 6 * 0.352777778, fontface = "bold")
## set the width of the plot in inches
p.width = 6
## write the plot to a graphics file
png(filename = paste0(chr.dir.figures, "/", chr.file.figure), width = p.width,
height = round(p.width / 1.61803398875, 1), units = "in",
res = 1200)
plot(p4)
dev.off()
## BTD - Add Error Bars
## BTD - Take all data from error bar dataset and filter or return rows according to each station number
df.err.bar <- eb %>% filter(station == chr.stn.num[j])
## BTD - Turn flow.exceed column fron a decimal into a percentage
df.err.bar$flow.exceed <- df.err.bar$flow.exceed * 100
## BTD - Add ggplot for error bars to the fdc plot, make error bars red
p5 <- p4 +
geom_point(data = df.err.bar, aes(x = flow.exceed, y = value), color = "red") +
geom_errorbar(data = df.err.bar,
aes(x=flow.exceed, ymin = err.lower.limit ,
ymax = err.upper.limit), color = "red")
## BTD - Name of the fdc plot with the added error bars
png(filename = paste0(chr.dir.figures, "/",
paste0('fdc-stn-', chr.stn.num[j], '-w-err-bar.png')),
width = p.width, height = round(p.width / 1.61803398875, 1), units = "in",
res = 1200)
plot(p5)
dev.off()
}
}
flow.d.c(chr.dir.main = "M:/Models/Bacteria/LDC/Bernadette-workspace",
chr.dir.flow.data = "M:/Models/Bacteria/LDC/Bernadette-workspace/data" ,
chr.dir.figures = "M:/Models/Bacteria/LDC/Bernadette-workspace/figures",
chr.eb = "//deqhq1/TMDL/TMDL_WR/MidCoast/Models/Bacteria/LDC/Bernadette-workspace/data/fdc-err-bars.csv")
|
2e3f36a0154a3a1e3bfdb82fd57e387901848f06
|
c542082c439cf134c109d1b12605aedabe2ca082
|
/R/playground/old_attempts/predict_2_kernels.R
|
352660db0e77206d43255c8fe66b7495a449ade5
|
[] |
no_license
|
NathanWycoff/GPArcLength
|
f811702d0bcb3bd3e447c8c9ab8611d1830936f7
|
4b84fb390dd21cf3487f53b5709ef9a9c8b1eadd
|
refs/heads/master
| 2021-05-06T04:14:31.562516
| 2018-01-27T17:08:44
| 2018-01-27T17:08:44
| 114,920,616
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,840
|
r
|
predict_2_kernels.R
|
#!/usr/bin/Rscript
# predict_2_kernels.r Author "Nathan Wycoff <nathanbrwycoff@gmail.com>" Date 01.08.2018
require(mds.methods)
source('../../lib/some_gp_funcs.R')
## Seeing what predictions looked like on the nonsmooth multiscale GP
######### Generate some data with specified weirdness
set.seed(1234)
n <- 50
p <- 1
X <- matrix(runif(n*p), ncol = p)
#X <- matrix(seq(0,1,length.out=n), ncol = p)
## Denote the top s which have the largest inner product with the p-vector of 1's as being in the different kernel space
sp <- 0.25#proportion of things in different kernel space
s <- ceiling(sp * n)
r <- rank(-X %*% rep(1,p))
##Xn -- x normal, obeys the kernel for most of the space
##Xd -- x different, obeys a kernel with a tenth of the lengthscale
Xn <- as.matrix(X[r > s,], ncol = 1)
Xd <- as.matrix(X[r <= s,], ncol = 1)
#Create the kernels and the response
kernn <- kernel_factory(lengthscale=0.1)
kernd <- kernel_factory(lengthscale=0.01, covariance = 10)
nugget <- 0.01
yn <- gen_gp(Xn, kernn, nugget)
yd <- gen_gp(Xd, kernd, nugget)
#Store them in one vector
y <- rep(NA, n)
y[r > s] <- yn
y[r <= s] <- yd
#Only works in 1D so far.
funky_kern <- function(x, y) {
cp1 <- max(Xn)
cp2 <- min(Xd)
if (x <= cp1) {
return(kernn(x, y))
} else if (x >= cp2) {
return(kernd(x, y))
} else {
l <- conn_line_seg(c(cp1, kernn(x, y)), c(cp2, kernd(x, y)))
return(l(x - cp1)[2])
}
}
##For 1D only, plot the points as well as the normal GP fit.
if (p == 1) {
quartz()
cols <- c('red', 'blue')
plot(X, y, lwd=0)
text(X, y, 1:n, col = cols[(r > s) + 1])
XX <- as.matrix(seq(0,1,length.out=200), ncol = p)
mu <- gp_post_mean_factory(X, y, funky_kern, nugget)
yy <- sapply(1:nrow(XX), function(xx) mu(XX[xx,]))
points(XX, yy, col = 'red', type = 'l')
}
|
94f41207c4622ee8e480ac0a3ed1836e8871b16d
|
fc00987cf8ddb7ee81fd7865cfd8f272a7f4a101
|
/R/by-game-parsers.r
|
d0fa28d8eff979a579a49739c450c7b029a8d40e
|
[
"MIT"
] |
permissive
|
zamorarr/msf
|
72bcaed4569b2c4f3bca05940965285b4c0c3fd4
|
d84327bd04a15efbd36e918646d82458bf61a280
|
refs/heads/master
| 2018-10-06T20:16:27.831851
| 2018-06-22T14:16:11
| 2018-06-22T14:16:11
| 116,212,412
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,596
|
r
|
by-game-parsers.r
|
#' Parse box scores
#'
#' @param json content from response
#' @export
#' @examples
#' \dontrun{
#' resp <- game_boxscore("nfl", "20170917-ARI-IND", season = "2017-2018-regular")
#' resp <- game_boxscore("nhl", "20171114-BUF-PIT", season = "2017-2018-regular")
#' parse_boxscore(resp$content)
#' }
parse_boxscore <- function(json) {
gameboxscore <- json[["gameboxscore"]]
# game data
game <- gameboxscore[["game"]]
away_id <- game[["awayTeam"]][["ID"]]
home_id <- game[["homeTeam"]][["ID"]]
# player stats
away <- gameboxscore[["awayTeam"]][["awayPlayers"]][["playerEntry"]]
home <- gameboxscore[["homeTeam"]][["homePlayers"]][["playerEntry"]]
df_away <- parse_team_boxscore(away)
df_home <- parse_team_boxscore(home)
# data frames
df_home$team_id <- home_id
df_away$team_id <- away_id
# combine home and away
result <- rbind(df_away, df_home)
important_names <- c("player_id", "team_id", "position")
result[c(important_names, setdiff(names(result), important_names))]
}
#' @keywords internal
parse_team_boxscore <- function(json) {
# stats
stats <- purrr::map(json, "stats")
toremove <- purrr::map_lgl(stats, is.null) # no stats? get outta here
stats <- stats[!toremove]
df_stats <- parse_stats(stats)
# players
players <- purrr::map(json, "player")
players <- players[!toremove]
player_ids <- purrr::map_chr(players, "ID")
positions <- purrr::map_chr(players, "Position")
result <- tibble::tibble(player_id = player_ids, position = positions)
cbind(result, df_stats)
}
#' Parse starting lineup for a game
#'
#' @param json content from response
#' @param type actual or expected lineup
#' @export
#' @examples
#' \dontrun{
#' resp <- game_starting_lineup("nhl", "20171014-BUF-LAK", season = "2017-2018-regular")
#' resp <- game_starting_lineup("mlb", "20170822-COL-KC", season = "2017-regular")
#' resp <- game_starting_lineup("nba", "42070", season = "2017-2018-regular")
#' parse_starting_lineup(resp$content, "actual")
#'
#' }
parse_starting_lineup <- function(json, type = c("actual", "expected")) {
startinglineup <- json[["gamestartinglineup"]]
# game info
game_id <- startinglineup[["game"]][["id"]]
# lineups
type <- match.arg(type)
lineups <- purrr::map(startinglineup[["teamLineup"]], parse_single_lineup, type)
# combine data frames
lineups <- do.call(rbind, lineups)
lineups$game_id <- game_id
lineups[c("player_id", "team_id", "game_id", "lineup_position")]
}
#' @keywords internal
parse_single_lineup <- function(lineup, type) {
# team info
team_id <- lineup[["team"]][["ID"]]
# player info
players <- lineup[[type]][["starter"]]
lineup_position <- purrr::map_chr(players, "position", .null = NA)
player_ids <- purrr::map_chr(players, c("player", "ID"), .null = NA)
tibble::tibble(player_id = player_ids, team_id = team_id, lineup_position = lineup_position)
}
#' Parse Play by Play Data
#' @param json list of data
#' @param sport sport
#' @export
#' @examples
#' \dontrun{
#' resp <- game_pbp("nhl", "20161215-FLO-WPJ", season = "2016-2017-regular")
#' parse_game_pbp(resp$content, "nhl")
#' }
parse_game_pbp <- function(json, sport = c(NA, "nba", "nhl", "nfl", "mlb")) {
sport = match.arg(sport)
# get plays or at-bats
if (is.na(sport)) {
stop("Please provide a sport argument.")
}
if (sport == "mlb") {
plays <- json[["gameplaybyplay"]][["atBats"]][["atBat"]]
} else {
plays <- json[["gameplaybyplay"]][["plays"]][["play"]]
}
# parse events
if (sport == "nba") {
quarter <- purrr::map_chr(plays, "quarter")
time <- purrr::map_chr(plays, "time")
event <- purrr::map_chr(plays, ~ names(.x)[3])
event_data <- purrr::map(plays, 3)
tibble::tibble(quarter = quarter, time = time, event = event, data = event_data)
} else if (sport == "nhl") {
period <- purrr::map_chr(plays, "period")
time <- purrr::map_chr(plays, "time")
event <- purrr::map_chr(plays, ~ names(.x)[3])
event_data <- purrr::map(plays, 3)
tibble::tibble(period = period, time = time, event = event, data = event_data)
} else if (sport == "nfl") {
quarter <- purrr::map_chr(plays, "quarter")
time <- purrr::map_chr(plays, "time")
event <- purrr::map_chr(plays, ~ utils::tail(names(.x),1))
event_data <- purrr::map(plays, utils::tail, 1)
tibble::tibble(quarter = quarter, time = time, event = event, data = event_data)
} else if (sport == "mlb") {
inning <- purrr::map_chr(plays, "inning")
inning_half <- purrr::map_chr(plays, "inningHalf")
batting_team <- purrr::map_chr(plays, c("battingTeam", "ID"))
atbat_id <- seq_along(inning)
event_data <- purrr::map(plays, "atBatPlay")
event_data <- purrr::map(event_data, parse_mlb_event)
results <- tibble::tibble(
inning = inning, inning_half = inning_half, batting_team = batting_team,
atbat_id = atbat_id, data = event_data)
#tidyr::unnest(results, data)
results
}
}
#' Parse mlb events
#' @param event a nested json event
#' @keywords internal
parse_mlb_event <- function(event) {
event_type <- purrr::map_chr(event, ~ names(head(.x)))
event_data <- purrr::map(event, 1)
play_id <- seq_along(event_type)
tibble::tibble(play_id, event = event_type, data = event_data)
}
#parse_starting_lineup(resp$content, "actual") %>%
# filter(!is.na(player_id)) %>%
# mutate(type = if_else(grepl("BO[0-9]", lineup_position), "BO", "position")) %>%
# tidyr::spread(type, lineup_position)
#mlb_batting_order <- function(id, position, team_id) {
# is_order <- grepl("BO", position) # batting order values start with BO
# col_type <- dplyr::if_else(is_order, "lineup_order", "position")
#
# df <- tibble::tibble(id = id, position = position, col_type = col_type)
# df <- dplyr::filter(df, !is.na(id))
#
# # hack to avoid errors when players are listed at multiple lineup spots
# # simply selects the first instance of that player
# df <- dplyr::arrange(df, id, position)
# df <- dplyr::group_by(df, id, col_type)
# df <- dplyr::slice(df, 1)
# df <- dplyr::ungroup(df)
#
# df <- tidyr::spread(df, col_type, position)
#
#
# # add lineup_order column if not found
# if (!("lineup_order" %in% colnames(df))) df[["lineup_order"]] <- NA_character_
#
# # batting orders are in the form BO1, BO2, BO3, etc..
# # stopifnot(length(df[["lineup_order"]]) == 9)
# df[["lineup_order"]] <- stringr::str_extract(df[["lineup_order"]], "[0-9]")
# df[["lineup_order"]] <- as.integer(df[["lineup_order"]])
#
# # add team id
# df[["team_id"]] <- team_id
#
# df[c("id", "lineup_order", "team_id")]
# }
|
e63ba045123abac07392c7175a229257ba14b35f
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/intrinsicDimension/examples/M_rozza.rd.R
|
a6eabda7b791ecdb0970acf93228058dc0cf14d5
|
[] |
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
| 430
|
r
|
M_rozza.rd.R
|
library(intrinsicDimension)
### Name: M_rozza
### Title: Manifolds from Rozza et al. (2012)
### Aliases: m14Manifold m15Manifold
### Keywords: datagen
### ** Examples
datap <- m14Manifold(800)
par(mfrow = c(1, 3))
plot(datap[,1], datap[,3])
plot(datap[,2], datap[,3])
plot(datap[,1], datap[,2])
datap <- m15Manifold(800)
par(mfrow = c(1, 3))
plot(datap[,1], datap[,3])
plot(datap[,2], datap[,3])
plot(datap[,1], datap[,2])
|
01bccb36b6bafff6eaa8ea497431ab4d0f8c0ef1
|
030b6b645e227da9a2be3b812c7846499e3bf65a
|
/hai.r
|
662fad51300f6badbd1465c165b9f482239f16cc
|
[] |
no_license
|
endft/kmmi_r
|
7b055db7722ad1367939a25bf5e3b6a313bd068a
|
0e502a94c23c6c4093012a4e58b44e295cc1a587
|
refs/heads/main
| 2023-06-30T04:45:12.580868
| 2021-08-09T03:31:27
| 2021-08-09T03:31:27
| 393,987,945
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 15
|
r
|
hai.r
|
teks1 ="haiiii"
|
ac906c1a8ada194f07d84769d9b81e641b5d3b92
|
b4c24634b5f5d84a23405e1339bd03b065ebc62c
|
/R/derivatives_basic.R
|
fbd16f4499b8ad73c542c3f7c4f792d95aa8eda3
|
[
"MIT"
] |
permissive
|
minghao2016/diseq
|
0f6f41ae1d3c258d7ce3df264c2137a2857e12ec
|
035c7d54f3c3fbe07fbb7255bf61f6a9c565f228
|
refs/heads/master
| 2023-02-28T00:06:13.168971
| 2021-01-26T17:22:02
| 2021-01-26T17:22:02
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13,701
|
r
|
derivatives_basic.R
|
#' @include system_basic.R
setGeneric("partial_beta_d_of_loglh", function(object) {
standardGeneric("partial_beta_d_of_loglh")
})
setMethod("partial_beta_d_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
c(((object@supply@psi * object@rho1 / (object@demand@sigma * object@supply@sigma) + (object@demand@Psi * object@demand@h - object@demand@psi * object@rho2) / object@demand@var)) / object@lh) * object@demand@independent_matrix
# nolint end
})
setGeneric("partial_beta_s_of_loglh", function(object) {
standardGeneric("partial_beta_s_of_loglh")
})
setMethod("partial_beta_s_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
c(((object@demand@psi * object@rho1 / (object@demand@sigma * object@supply@sigma) + (object@supply@Psi * object@supply@h - object@supply@psi * object@rho2) / object@supply@var)) / object@lh) * object@supply@independent_matrix
# nolint end
})
setGeneric("partial_var_d_of_loglh", function(object) {
standardGeneric("partial_var_d_of_loglh")
})
setMethod("partial_var_d_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
c((object@demand@h * object@supply@psi * object@rho1 / (2 * object@demand@var * object@supply@sigma) + (object@demand@Psi * (object@demand@h**2 - 1) - object@demand@h * object@demand@psi * object@rho2) / (2 * object@demand@sigma**3)) / object@lh)
# nolint end
})
setGeneric("partial_var_s_of_loglh", function(object) {
standardGeneric("partial_var_s_of_loglh")
})
setMethod("partial_var_s_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
c((object@supply@h * object@demand@psi * object@rho1 / (2 * object@demand@sigma * object@supply@var) + (object@supply@Psi * (object@supply@h**2 - 1) - object@supply@h * object@supply@psi * object@rho2) / (2 * object@supply@sigma**3)) / object@lh)
# nolint end
})
setGeneric("partial_rho_of_loglh", function(object) {
standardGeneric("partial_rho_of_loglh")
})
setMethod("partial_rho_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
c((object@rho1**2 * (object@demand@psi * object@demand@z / object@demand@sigma + object@supply@psi * object@supply@z / object@supply@sigma)) / object@lh)
# nolint end
})
setGeneric("partial_beta_d_partial_beta_d_of_loglh", function(object) {
standardGeneric("partial_beta_d_partial_beta_d_of_loglh")
})
setMethod("partial_beta_d_partial_beta_d_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(t(object@demand@independent_matrix * c(((object@supply@psi * object@rho1**2 * object@demand@sigma * object@demand@z + object@supply@sigma * (object@demand@Psi * (object@demand@h**2 - 1) - object@demand@psi * object@rho1 * (object@supply@h - object@rho1 * object@supply@z))) / (object@demand@sigma**3 * object@supply@sigma)) / object@lh)) %*% object@demand@independent_matrix) - t(partial_beta_d_of_loglh(object)) %*% partial_beta_d_of_loglh(object)
# nolint end
})
setGeneric("partial_beta_d_partial_beta_s_of_loglh", function(object) {
standardGeneric("partial_beta_d_partial_beta_s_of_loglh")
})
setMethod("partial_beta_d_partial_beta_s_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(t(object@supply@independent_matrix * c((object@rho1 * (object@demand@psi * object@supply@sigma * (object@demand@h - object@rho2 * object@supply@z) + object@supply@psi * object@demand@sigma * (object@supply@h - object@rho2 * object@demand@z)) / (object@demand@var * object@supply@var)) / object@lh)) %*% object@demand@independent_matrix) - t(partial_beta_s_of_loglh(object)) %*% partial_beta_d_of_loglh(object)
# nolint end
})
setGeneric("partial_beta_d_partial_var_d_of_loglh", function(object) {
standardGeneric("partial_beta_d_partial_var_d_of_loglh")
})
setMethod("partial_beta_d_partial_var_d_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(colSums(c(((object@supply@psi * object@rho1 * object@demand@sigma * (object@demand@h * object@rho1 * object@demand@z - 1) + object@supply@sigma * (object@demand@Psi * object@demand@h**3 - object@demand@h * (3 * object@demand@Psi + object@demand@psi * (2 * object@supply@h * object@rho1 - object@supply@z * (object@rho1**2 + 1))) + 2 * object@demand@psi * object@rho2)) / (2 * object@demand@sigma**4 * object@supply@sigma)) / object@lh) * object@demand@independent_matrix)) - t(partial_var_d_of_loglh(object)) %*% partial_beta_d_of_loglh(object)
# nolint end
})
setGeneric("partial_beta_d_partial_var_s_of_loglh", function(object) {
standardGeneric("partial_beta_d_partial_var_s_of_loglh")
})
setMethod("partial_beta_d_partial_var_s_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(colSums(c((object@rho1 * (object@demand@psi * object@supply@sigma * (object@demand@h * (object@supply@h - object@rho1 * object@supply@z) + object@demand@z * object@supply@z) - object@supply@psi * object@demand@sigma * (object@demand@h * object@rho1 * object@demand@z - object@supply@h**2 - object@demand@z**2 + 1)) / (2 * object@demand@var * object@supply@sigma**3)) / object@lh) * object@demand@independent_matrix)) - t(partial_var_s_of_loglh(object)) %*% partial_beta_d_of_loglh(object)
# nolint end
})
setGeneric("partial_beta_d_partial_rho_of_loglh", function(object) {
standardGeneric("partial_beta_d_partial_rho_of_loglh")
})
setMethod("partial_beta_d_partial_rho_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(colSums(c((-object@rho1**2 * (object@demand@psi * object@supply@sigma * (object@rho1 - object@demand@z * (object@demand@h - object@rho2 * object@supply@z)) - object@supply@psi * object@demand@sigma * (object@rho1 * object@demand@z * object@supply@z + object@rho2)) / (object@demand@var * object@supply@sigma)) / object@lh) * object@demand@independent_matrix)) - t(partial_rho_of_loglh(object)) %*% partial_beta_d_of_loglh(object)
# nolint end
})
setGeneric("partial_beta_s_partial_beta_s_of_loglh", function(object) {
standardGeneric("partial_beta_s_partial_beta_s_of_loglh")
})
setMethod("partial_beta_s_partial_beta_s_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(t(object@supply@independent_matrix * c(((object@demand@psi * object@rho1**2 * object@supply@sigma * object@supply@z + object@demand@sigma * (object@supply@Psi * (object@supply@h**2 - 1) - object@supply@psi * object@rho1 * (object@demand@h - object@rho1 * object@demand@z))) / (object@demand@sigma * object@supply@sigma**3)) / object@lh)) %*% object@supply@independent_matrix) - t(partial_beta_s_of_loglh(object)) %*% partial_beta_s_of_loglh(object)
# nolint end
})
setGeneric("partial_beta_s_partial_var_d_of_loglh", function(object) {
standardGeneric("partial_beta_s_partial_var_d_of_loglh")
})
setMethod("partial_beta_s_partial_var_d_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(colSums(c((object@rho1 * (object@demand@psi * object@supply@sigma * (object@demand@h**2 - object@supply@h * object@rho1 * object@supply@z + object@supply@z**2 - 1) + object@supply@psi * object@demand@sigma * (object@supply@h * (object@demand@h - object@rho1 * object@demand@z) + object@demand@z * object@supply@z)) / (2 * object@demand@sigma**3 * object@supply@var)) / object@lh) * object@supply@independent_matrix)) - t(partial_var_d_of_loglh(object)) %*% partial_beta_s_of_loglh(object)
# nolint end
})
setGeneric("partial_beta_s_partial_var_s_of_loglh", function(object) {
standardGeneric("partial_beta_s_partial_var_s_of_loglh")
})
setMethod("partial_beta_s_partial_var_s_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(colSums(c(((object@demand@psi * object@rho1 * object@supply@sigma * (object@supply@h * object@rho1 * object@supply@z - 1) + object@demand@sigma * (object@supply@Psi * object@supply@h**3 - object@supply@h * (3 * object@supply@Psi + object@supply@psi * (2 * object@demand@h * object@rho1 - object@demand@z * (object@rho1**2 + 1))) + 2 * object@supply@psi * object@rho2)) / (2 * object@demand@sigma * object@supply@sigma**4)) / object@lh) * object@supply@independent_matrix)) - t(partial_var_s_of_loglh(object)) %*% partial_beta_s_of_loglh(object)
# nolint end
})
setGeneric("partial_beta_s_partial_rho_of_loglh", function(object) {
standardGeneric("partial_beta_s_partial_rho_of_loglh")
})
setMethod("partial_beta_s_partial_rho_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(colSums(c((object@rho1**2 * (object@demand@psi * object@supply@sigma * (object@rho1 * object@demand@z * object@supply@z + object@rho2) - object@supply@psi * object@demand@sigma * (object@rho1 - object@supply@z * (object@supply@h - object@rho2 * object@demand@z))) / (object@demand@sigma * object@supply@var)) / object@lh) * object@supply@independent_matrix)) - t(partial_rho_of_loglh(object)) %*% partial_beta_s_of_loglh(object)
# nolint end
})
setGeneric("partial_var_d_partial_var_d_of_loglh", function(object) {
standardGeneric("partial_var_d_partial_var_d_of_loglh")
})
setMethod("partial_var_d_partial_var_d_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(sum(((object@demand@h * object@supply@psi * object@rho1 * object@demand@sigma * (object@demand@h * object@rho1 * object@demand@z - 3) + object@supply@sigma * (object@demand@Psi * (object@demand@h**4 + 3) + object@demand@h**2 * (-6 * object@demand@Psi + object@demand@psi * object@supply@z * (object@rho1**2 + 1)) - object@demand@psi * (object@supply@h * object@rho1 * (2 * object@demand@h**2 - 3) + 3 * object@supply@z))) / (4 * object@demand@sigma**5 * object@supply@sigma)) / object@lh)) - t(partial_var_d_of_loglh(object)) %*% partial_var_d_of_loglh(object)
# nolint end
})
setGeneric("partial_var_d_partial_var_s_of_loglh", function(object) {
standardGeneric("partial_var_d_partial_var_s_of_loglh")
})
setMethod("partial_var_d_partial_var_s_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(sum((object@rho1 * (object@supply@h * object@demand@psi * object@supply@sigma * (object@demand@h**2 - object@supply@h * object@rho1 * object@supply@z + object@supply@z**2 - 1) + object@supply@psi * object@demand@sigma * (object@demand@h * (object@supply@h**2 - 1) - object@supply@h * object@demand@z * (object@supply@h * object@rho1 - object@supply@z))) / (4 * object@demand@sigma**3 * object@supply@sigma**3)) / object@lh)) - t(partial_var_s_of_loglh(object)) %*% partial_var_d_of_loglh(object)
# nolint end
})
setGeneric("partial_var_d_partial_rho_of_loglh", function(object) {
standardGeneric("partial_var_d_partial_rho_of_loglh")
})
setMethod("partial_var_d_partial_rho_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(sum((object@rho1**2 * (-object@demand@psi * object@supply@sigma * (object@rho1 * (object@demand@h + object@supply@h * object@demand@z * object@supply@z) - object@demand@z * (object@demand@h**2 + object@supply@z**2 - 1)) + object@supply@psi * object@demand@sigma * (object@rho1 * (object@demand@h * object@demand@z * object@supply@z + object@supply@h) - object@supply@z)) / (2 * object@demand@sigma**3 * object@supply@sigma)) / object@lh)) - t(partial_rho_of_loglh(object)) %*% partial_var_d_of_loglh(object)
# nolint end
})
setGeneric("partial_var_s_partial_var_s_of_loglh", function(object) {
standardGeneric("partial_var_s_partial_var_s_of_loglh")
})
setMethod("partial_var_s_partial_var_s_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(sum(((object@supply@h * object@demand@psi * object@rho1 * object@supply@sigma * (object@supply@h * object@rho1 * object@supply@z - 3) + object@demand@sigma * (object@supply@Psi * (object@supply@h**4 + 3) + object@supply@h**2 * (-6 * object@supply@Psi + object@supply@psi * object@demand@z * (object@rho1**2 + 1)) - object@supply@psi * (object@demand@h * object@rho1 * (2 * object@supply@h**2 - 3) + 3 * object@demand@z))) / (4 * object@demand@sigma * object@supply@sigma**5)) / object@lh)) - t(partial_var_s_of_loglh(object)) %*% partial_var_s_of_loglh(object)
# nolint end
})
setGeneric("partial_var_s_partial_rho_of_loglh", function(object) {
standardGeneric("partial_var_s_partial_rho_of_loglh")
})
setMethod("partial_var_s_partial_rho_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(sum((object@rho1**2 * (object@demand@psi * object@supply@sigma * (object@rho1 * (object@demand@h + object@supply@h * object@demand@z * object@supply@z) - object@demand@z) - object@supply@psi * object@demand@sigma * (object@rho1 * (object@demand@h * object@demand@z * object@supply@z + object@supply@h) - object@supply@z * (object@supply@h**2 + object@demand@z**2 - 1))) / (2 * object@demand@sigma * object@supply@sigma**3)) / object@lh)) - t(partial_rho_of_loglh(object)) %*% partial_var_s_of_loglh(object)
# nolint end
})
setGeneric("partial_rho_partial_rho_of_loglh", function(object) {
standardGeneric("partial_rho_partial_rho_of_loglh")
})
setMethod("partial_rho_partial_rho_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
(sum((object@rho1**3 * (object@demand@psi * object@supply@sigma * (2 * object@supply@h + object@rho1 * object@supply@z * (object@demand@z**2 - 3)) + object@supply@psi * object@demand@sigma * (2 * object@demand@h + object@rho1 * object@demand@z * (object@supply@z**2 - 3))) / (object@demand@sigma * object@supply@sigma)) / object@lh)) - t(partial_rho_of_loglh(object)) %*% partial_rho_of_loglh(object)
# nolint end
})
|
802f61fe60a78fe20cb8c95e509e0f037f89e141
|
ffe095c7f1411c8cc009fcf09bc2392e7f739455
|
/tests/testthat.R
|
c4afda67212410b0052f0570b85991d4bb01b060
|
[
"MIT"
] |
permissive
|
atusy/swiper
|
da4fce37abbcf06bc3420ef4726b200fe066b389
|
d930167a705e3409bcb1ebca1718481c14844823
|
refs/heads/master
| 2023-06-08T08:49:25.606844
| 2021-07-01T16:23:54
| 2021-07-01T16:23:54
| 376,047,555
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 56
|
r
|
testthat.R
|
library(testthat)
library(swiper)
test_check("swiper")
|
e8862a8472346c49665b1688981a70961dfdc7a0
|
9c79f8d1e89ee5adf7b93115ccc741d3303404f1
|
/InteractiveMaps/Trojborg/Outs.R
|
08c423ecced7361684d62533c5f8a3f4ae6a5307
|
[] |
no_license
|
derek-corcoran-barrios/derek-corcoran-barrios.github.io
|
e1631feef111cfc9bc693df1853e02818435071a
|
ccb8f21c053fd41559082eb58ccb7f64cc7fcf86
|
refs/heads/master
| 2023-07-17T13:11:43.739914
| 2023-07-03T07:24:21
| 2023-07-03T07:24:21
| 107,616,762
| 33
| 33
| null | 2020-06-18T19:25:50
| 2017-10-20T01:23:44
|
HTML
|
UTF-8
|
R
| false
| false
| 4,953
|
r
|
Outs.R
|
library(tidyverse)
library(sf)
library(raster)
library(spThin)
Trojborg_Raster <- read_rds("TrojborgRaster.rds")
Trojborg_Raster <- Trojborg_Raster[[1]] %>% projectRaster(crs ="+proj=longlat +datum=WGS84 +no_defs")
Trojborg <- read_sf("GroupsTrojborg.shp") %>%
st_transform(crs = "+proj=longlat +datum=WGS84 +no_defs") %>%
fasterize::fasterize(Trojborg_Raster, field = "Group", background = 0)
Trojborg_Outline <- read_sf("GroupsTrojborg.shp") %>%
st_transform(crs = "+proj=longlat +datum=WGS84 +no_defs") %>%
st_union() %>%
st_as_sf()
Trojborg_Buff <- read_sf("GroupsTrojborg.shp") %>%
st_buffer(dist = 30) %>%
st_transform(crs = "+proj=longlat +datum=WGS84 +no_defs") %>%
fasterize::fasterize(Trojborg_Raster, field = "Group")
Trojborg_Large <- read_sf("GroupsTrojborgLarge.shp") %>%
st_transform(crs = "+proj=longlat +datum=WGS84 +no_defs") %>%
fasterize::fasterize(Trojborg_Raster, field = "Group")
Test <- (Trojborg_Large - Trojborg)
values(Test) <- ifelse(values(Test) < 1, NA, values(Test))
library(stars)
Test <- stars::st_as_stars(Test)
l = st_contour(Test, contour_lines = FALSE, breaks = 0:7) %>%
mutate(Group = case_when(layer == "[1,2)" ~ 1,
layer == "[2,3)" ~ 2,
layer == "[3,4)" ~ 3,
layer == "[4,5)" ~ 4,
layer == "[5,6)" ~ 5,
layer == "[6,7)" ~ 6)) %>%
dplyr::select(Group) %>%
mutate(Group = case_when(Group == 1 ~ "A1",
Group == 2 ~ "A2",
Group == 3 ~ "B1",
Group == 4 ~ "B2",
Group == 5 ~ "C1",
Group == 6 ~ "C2"))
Centroids <- st_read("Final_Centroids_Trojborg.shp")
ggplot() + geom_sf(data = l, aes(fill = as.factor(Group))) +
geom_sf(data = Centroids, aes(color = as.factor(Group)))
Groups <- unique(Centroids$Group)
Final_ones <- list()
for(i in 1:length(Groups)){
Temp_Pol <- l %>% dplyr::filter(Group == Groups[i])
Temp_Rast <- fasterize::fasterize(Temp_Pol, Trojborg_Raster)
Temp_Point <- Centroids %>%
dplyr::filter(Group == Groups[i])
set.seed(i)
Out_Random <- dismo::randomPoints(mask = Temp_Rast, 1000) %>%
as.data.frame()
Coords <- Out_Random
Out_Random <- Out_Random %>%
st_as_sf(coords = c(1,2), crs ="+proj=longlat +datum=WGS84 +no_defs")
# mutate(Group = Groups[i])
Distances <- Out_Random %>%
st_distance(Trojborg_Outline) %>%
as.numeric()
Coords$Distances <- Distances
Coords <- Coords %>% dplyr::filter(Distances > 50) %>% mutate(Group = Groups[i])
NewCoords <- spThin::thin(loc.data = Coords,
lat.col = "y",
long.col = "x",
spec.col = "Group",
verbose = F,
out.dir = getwd(),
thin.par = 0.05,
reps = 1,
locs.thinned.list.return = T,
write.files = F,
write.log.file = F)
Out_Random <- NewCoords[[1]] %>%
st_as_sf(coords = c(1,2), crs ="+proj=longlat +datum=WGS84 +no_defs") %>%
mutate(Group = Groups[i]) %>%
tibble::rowid_to_column()
Temp_Point$Distance <- st_distance(Temp_Point, Temp_Pol) %>% as.numeric()
Temp_Point <- Temp_Point %>% group_by(rowid) %>% dplyr::filter(Distance == min(Distance))
Final_points <- list()
for(j in 1:nrow(Temp_Point)){
To_Match <- Temp_Point[j,]
Dist_To_Match <- st_distance(To_Match, Out_Random) %>% as.numeric()
Matched <- Out_Random[Dist_To_Match == min(Dist_To_Match),]
Out_Random <- Out_Random[Dist_To_Match != min(Dist_To_Match),]
Code <- To_Match %>% separate(col = Code, into = c("Group1", "id", "treatment"))
Matched <- cbind(Matched, Code) %>% mutate(Code = paste(Group, id, "D", sep = "_")) %>%
dplyr::select("Group", "lat", "long", "Code")
Final_points[[j]] <- Matched %>% mutate(rowid = To_Match$rowid) %>% relocate(rowid, .before = everything())
}
Final_points <- Final_points %>% reduce(rbind)
Final_ones[[i]] <- Final_points
}
Final_ones <- Final_ones %>% reduce(rbind)
ggplot() + geom_sf(data = Final_points, shape=21, aes(color = "blue", fill = as.factor(rowid))) + geom_sf(data = Temp_Point, shape=21, aes(color = "red", fill = as.factor(rowid)))
saveRDS(Final_ones, "Final_ones_out.Trojborg.rds")
ToGPX <- Centroids %>% rbind(Final_ones) %>% arrange(Code) %>% dplyr::select(Code) %>%
st_transform("+proj=longlat + ellps=WGS84") %>%
as_Spatial()
# use the ID field for the names
ToGPX@data$name <- ToGPX@data$Code
library(rgdal)
#Now only write the "name" field to the file
writeOGR(ToGPX["name"], driver="GPX", layer="waypoints",
dsn="TreatmentsTrojborg.gpx")
read_sf("TreatmentsTrojborg.gpx")
|
d75ffd79a260959cfade2c8c0e26fc0a967725ca
|
8633d09805e0c6cd67765865d2dd8708e400b057
|
/scripts/excess_deaths_script.R
|
fef4313808a5dd2892bb4eb907208b78748774d4
|
[
"MIT",
"CC-BY-4.0"
] |
permissive
|
nnutter/covid-19-excess-deaths-tracker
|
0f51a258841fbc8664e90181d2560f43f1c2bf59
|
f8933ac749fe175f3078c8a8e4ac10d7575bcea8
|
refs/heads/master
| 2022-12-14T01:20:52.751827
| 2020-09-09T16:04:18
| 2020-09-09T16:04:18
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 12,558
|
r
|
excess_deaths_script.R
|
# Step 1: import libraries and data ---------------------------------------
# Import libraries
library(tidyverse)
library(readxl)
library(data.table)
library(lubridate)
options(scipen=999)
# Import data
austria_weekly_deaths <- fread("output-data/historical-deaths/austria_weekly_deaths.csv")
belgium_weekly_deaths <- fread("output-data/historical-deaths/belgium_weekly_deaths.csv")
brazil_monthly_deaths <- fread("output-data/historical-deaths/brazil_monthly_deaths.csv")
britain_weekly_deaths <- fread("output-data/historical-deaths/britain_weekly_deaths.csv")
chile_weekly_deaths <- fread("output-data/historical-deaths/chile_weekly_deaths.csv")
denmark_weekly_deaths <- fread("output-data/historical-deaths/denmark_weekly_deaths.csv")
ecuador_monthly_deaths <- fread("output-data/historical-deaths/ecuador_monthly_deaths.csv")
france_weekly_deaths <- fread("output-data/historical-deaths/france_weekly_deaths.csv")
germany_weekly_deaths <- fread("output-data/historical-deaths/germany_weekly_deaths.csv")
indonesia_monthly_deaths <- fread("output-data/historical-deaths/indonesia_monthly_deaths.csv")
italy_weekly_deaths <- fread("output-data/historical-deaths/italy_weekly_deaths.csv")
mexico_weekly_deaths <- fread("output-data/historical-deaths/mexico_weekly_deaths.csv")
netherlands_weekly_deaths <- fread("output-data/historical-deaths/netherlands_weekly_deaths.csv")
norway_weekly_deaths <- fread("output-data/historical-deaths/norway_weekly_deaths.csv")
peru_monthly_deaths <- fread("output-data/historical-deaths/peru_monthly_deaths.csv")
portugal_weekly_deaths <- fread("output-data/historical-deaths/portugal_weekly_deaths.csv")
russia_monthly_deaths <- fread("output-data/historical-deaths/russia_monthly_deaths.csv")
south_africa_weekly_deaths <- fread("output-data/historical-deaths/south_africa_weekly_deaths.csv")
spain_weekly_deaths <- fread("output-data/historical-deaths/spain_weekly_deaths.csv")
sweden_weekly_deaths <- fread("output-data/historical-deaths/sweden_weekly_deaths.csv")
switzerland_weekly_deaths <- fread("output-data/historical-deaths/switzerland_weekly_deaths.csv")
turkey_weekly_deaths <- fread("output-data/historical-deaths/turkey_weekly_deaths.csv")
united_states_weekly_deaths <- fread("output-data/historical-deaths/united_states_weekly_deaths.csv")
# Step 2: define function that calculates excess deaths, and apply to weekly deaths ---------------------------------------
# Define function that calculates excess deaths
get_excess_deaths <- function(df,frequency="weekly",calculate=TRUE){
if(frequency == "weekly" & calculate == TRUE) {
# Calculate expected deaths for weekly time series
expected_deaths <- df %>%
dplyr::select(-expected_deaths) %>%
filter(year == 2020) %>%
left_join(df %>%
filter(year >= 2015,year <= 2019) %>%
group_by(region,week) %>%
summarise(expected_deaths = mean(total_deaths,na.rm=T)))
} else if(frequency == "monthly" & calculate == TRUE) {
# Calculate expected deaths for monthly time series
expected_deaths <- df %>%
dplyr::select(-expected_deaths) %>%
filter(year == 2020) %>%
left_join(df %>%
filter(year >= 2015,year <= 2019) %>%
group_by(region,month) %>%
summarise(expected_deaths = mean(total_deaths,na.rm=T)))
} else { expected_deaths <- df %>% filter(year == 2020)}
# Calculate excess deaths
excess_deaths <- expected_deaths %>%
mutate(excess_deaths = total_deaths - expected_deaths,
non_covid_deaths = total_deaths - covid_deaths,
region_code = as.character(region_code)) %>%
mutate(covid_deaths_per_100k = covid_deaths / population * 100000,
excess_deaths_per_100k = excess_deaths / population * 100000,
excess_deaths_pct_change = ((expected_deaths + excess_deaths) / expected_deaths) - 1)
# Calculate weekly rates for monthly data
if(frequency == "monthly") {
excess_deaths <- excess_deaths %>%
mutate(month_days = as.numeric(difftime(end_date,start_date,units=c("days"))) + 1,
total_deaths_per_7_days = total_deaths / month_days * 7,
covid_deaths_per_7_days = covid_deaths / month_days * 7,
expected_deaths_per_7_days = expected_deaths / month_days * 7,
excess_deaths_per_7_days = excess_deaths / month_days * 7,
non_covid_deaths_per_7_days = non_covid_deaths / month_days * 7,
covid_deaths_per_100k_per_7_days = covid_deaths_per_100k / month_days * 7,
excess_deaths_per_100k_per_7_days = excess_deaths_per_100k / month_days * 7) %>%
dplyr::select(-month_days)
}
excess_deaths
}
# Export Austria
austria_excess_deaths <- get_excess_deaths(austria_weekly_deaths)
write.csv(austria_excess_deaths,"output-data/excess-deaths/austria_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Belgium
belgium_excess_deaths <- get_excess_deaths(belgium_weekly_deaths)
write.csv(belgium_excess_deaths,"output-data/excess-deaths/belgium_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Brazil
brazil_excess_deaths <- get_excess_deaths(brazil_monthly_deaths,frequency="monthly")
write.csv(brazil_excess_deaths,"output-data/excess-deaths/brazil_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Britain
britain_excess_deaths <- get_excess_deaths(britain_weekly_deaths)
write.csv(britain_excess_deaths,"output-data/excess-deaths/britain_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Chile
chile_excess_deaths <- get_excess_deaths(chile_weekly_deaths)
write.csv(chile_excess_deaths,"output-data/excess-deaths/chile_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Denmark
denmark_excess_deaths <- get_excess_deaths(denmark_weekly_deaths)
write.csv(denmark_excess_deaths,"output-data/excess-deaths/denmark_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Ecuador
ecuador_excess_deaths <- get_excess_deaths(ecuador_monthly_deaths,frequency="monthly")
write.csv(ecuador_excess_deaths,"output-data/excess-deaths/ecuador_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export France
france_excess_deaths <- get_excess_deaths(france_weekly_deaths)
write.csv(france_excess_deaths,"output-data/excess-deaths/france_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Germany
germany_excess_deaths <- get_excess_deaths(germany_weekly_deaths)
write.csv(germany_excess_deaths,"output-data/excess-deaths/germany_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Indonesia
indonesia_excess_deaths <- get_excess_deaths(indonesia_monthly_deaths,frequency="monthly")
write.csv(indonesia_excess_deaths,"output-data/excess-deaths/indonesia_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Italy
italy_excess_deaths <- get_excess_deaths(italy_weekly_deaths)
write.csv(italy_excess_deaths,"output-data/excess-deaths/italy_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Mexico
mexico_excess_deaths <- get_excess_deaths(mexico_weekly_deaths)
write.csv(mexico_excess_deaths,"output-data/excess-deaths/mexico_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export the Netherlands
netherlands_excess_deaths <- get_excess_deaths(netherlands_weekly_deaths)
write.csv(netherlands_excess_deaths,"output-data/excess-deaths/netherlands_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Norway
norway_excess_deaths <- get_excess_deaths(norway_weekly_deaths)
write.csv(norway_excess_deaths,"output-data/excess-deaths/norway_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Peru
peru_excess_deaths <- get_excess_deaths(peru_monthly_deaths,frequency="monthly")
write.csv(peru_excess_deaths,"output-data/excess-deaths/peru_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Portugal
portugal_excess_deaths <- get_excess_deaths(portugal_weekly_deaths)
write.csv(portugal_excess_deaths,"output-data/excess-deaths/portugal_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Russia
russia_excess_deaths <- get_excess_deaths(russia_monthly_deaths,frequency="monthly")
write.csv(russia_excess_deaths,"output-data/excess-deaths/russia_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export South Africa
south_africa_excess_deaths <- get_excess_deaths(south_africa_weekly_deaths,calculate=FALSE)
write.csv(south_africa_excess_deaths,"output-data/excess-deaths/south_africa_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Spain
spain_excess_deaths <- get_excess_deaths(spain_weekly_deaths,calculate=FALSE)
write.csv(spain_excess_deaths,"output-data/excess-deaths/spain_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Sweden
sweden_excess_deaths <- get_excess_deaths(sweden_weekly_deaths)
write.csv(sweden_excess_deaths,"output-data/excess-deaths/sweden_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Switzerland
switzerland_excess_deaths <- get_excess_deaths(switzerland_weekly_deaths)
write.csv(switzerland_excess_deaths,"output-data/excess-deaths/switzerland_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export Turkey
turkey_excess_deaths <- get_excess_deaths(turkey_weekly_deaths)
write.csv(turkey_excess_deaths,"output-data/excess-deaths/turkey_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Export the United States
united_states_excess_deaths <- get_excess_deaths(united_states_weekly_deaths)
write.csv(united_states_excess_deaths,"output-data/excess-deaths/united_states_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Step 3: combine weekly and monthly deaths together, and calculate deaths per 100,000 people and percentage change ---------------------------------------
# Combine weekly deaths and calculate per 100,000 people and percentage change
all_weekly_excess_deaths <- bind_rows(austria_excess_deaths,
belgium_excess_deaths,
britain_excess_deaths,
chile_excess_deaths,
denmark_excess_deaths,
france_excess_deaths,
germany_excess_deaths,
italy_excess_deaths,
mexico_excess_deaths,
netherlands_excess_deaths,
norway_excess_deaths,
portugal_excess_deaths,
south_africa_excess_deaths,
spain_excess_deaths,
sweden_excess_deaths,
switzerland_excess_deaths,
turkey_excess_deaths,
united_states_excess_deaths) %>%
mutate(covid_deaths_per_100k = covid_deaths / population * 100000,
excess_deaths_per_100k = excess_deaths / population * 100000,
excess_deaths_pct_change = ((expected_deaths + excess_deaths) / expected_deaths) - 1)
# Export weekly deaths
write.csv(all_weekly_excess_deaths,"output-data/excess-deaths/all_weekly_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
# Combine monthly deaths and calculate per 100,000 people and percentage change
all_monthly_excess_deaths <- bind_rows(brazil_excess_deaths,
ecuador_excess_deaths,
indonesia_excess_deaths,
peru_excess_deaths,
russia_excess_deaths) %>%
mutate(covid_deaths_per_100k = covid_deaths / population * 100000,
excess_deaths_per_100k = excess_deaths / population * 100000,
excess_deaths_pct_change = ((expected_deaths + excess_deaths) / expected_deaths) - 1)
# Export monthly deaths
write.csv(all_monthly_excess_deaths,"output-data/excess-deaths/all_monthly_excess_deaths.csv",
fileEncoding = "UTF-8",row.names=FALSE)
|
40d006c456321f8ecae7ad008c7d225985cd8143
|
2b36bf4a6b6ec05db94f6fa23076cd27843ff747
|
/scripts/IRAIL_data_exploration_020117.R
|
01c5436066032e9d9cbf80bd3509632cebbeae7d
|
[] |
no_license
|
simonkassel/IRAIL
|
9227f1a307221e4793df2387d7277654d6793eff
|
fd37b45343efd652b4cefcb2ca9f3e107b8cf336
|
refs/heads/master
| 2021-01-25T06:55:12.596187
| 2017-04-18T19:35:06
| 2017-04-18T19:35:06
| 80,666,443
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,530
|
r
|
IRAIL_data_exploration_020117.R
|
# INTRO -------------------------------------------------------------------
# Explore dataset through visualization and summary statistics
# Simon Kassel
# Created: 1 Feb 17
# load helper functions
source("https://raw.githubusercontent.com/simonkassel/IRAIL/master/scripts/IRAIL_helper_functions_032317.R")
# load packages
packages(c("plyr", "dplyr", "ggplot2", "ggmap", "ggthemes", "chron", "tidyr", "reshape2"))
# global options
options(stringsAsFactors = TRUE)
options(scipen = "999")
# data
stations <- read.csv("https://raw.githubusercontent.com/simonkassel/IRAIL/master/data/stations_cleaned.csv")
dat <- read.csv("https://raw.githubusercontent.com/simonkassel/IRAIL/master/data/trip_data_clean.csv")
md <- read.csv("https://raw.githubusercontent.com/simonkassel/IRAIL/master/data/model_variables.csv")
md <- md[,-1]
# MAP THE STATIONS --------------------------------------------------------
# new station variables
stations$instudy <- ifelse(stations$station %in% unique(trips$to), "Y", "N") %>% factor(levels = c("Y", "N"))
ggplot(stations, aes(x = instudy)) + geom_bar(stat = "count") +
theme(
axis.ticks = element_blank(),
plot.background = element_blank(),
legend.background = element_blank(),
axis.title = element_text(face = "italic")
) +
ylab("Count") + xlab("Does the station have a measurement?") +
ggtitle("Stations in and out of the sample")
# bounding box
get_Belgium_basemap <- function(){
bbox <- c(min(stations$longitude), min(stations$latitude),
max(stations$longitude), max(stations$latitude))
bm <- get_stamenmap(bbox = bbox, maptype = "toner-background")
}
# Get basemap
bm <- get_googlemap(center = c(mean(stations$longitude), mean(stations$latitude)), zoom = 7, color = "bw")
# Map stations in and out of training set
map.stations <- ggmap(bm) +
geom_point(data = stations, aes(x = longitude, y = latitude), size = .25) +
theme_map() +
scale_color_fivethirtyeight("Station in \nTraining Set?") +
ggtitle("Belgian Train Stations") +
theme(
legend.position = c(.05,.85),
legend.direction = "horizontal",
plot.title = element_text(face = "bold", hjust = "0.5", size = 14))
ggsave("IRAIL_stage1_mapping_stations.pdf", map.stations, device = "pdf", width = 8.5, height = 11, units = "in")
# TIME INTERVALS ----------------------------------------------------------
# cor b/w occupancy and weekend
weekday.cor.jitter <- ggplot(dat, aes(x = occupancy, y = weekend)) + geom_jitter() +
theme_fivethirtyeight() + xlab("ridership") + ylab("weekend day?") +
ggtitle("Correlation between ridership level and weekday/weekend") +
theme(
axis.title = element_text(face = "italic", colour = "grey50"),
plot.title = element_text(hjust = 0.5, size = 14)
)
ggsave("IRAIL_stage1_weekday-weekend_ridership_jitter.pdf", weekday.cor.jitter, device = "pdf", width = 11, height = 8.5, units = "in")
# trip segments by day of week
trip.segments.dow <- ggmap(bm) +
geom_segment(data = dat, aes(x = from.longitude, y = from.latitude, xend = to.longitude, yend = to.latitude,
colour = occupancy), size = .5) +
ggtitle("Train ridership by day-of-week") +
theme_fivethirtyeight() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
plot.background = element_blank(),
legend.background = element_blank()
) +
facet_wrap(~day_of_week, ncol = 4)
ggsave("IRAIL_stage1_trip_segments_by_dow.pdf", trip.segments.dow, device = "pdf", width = 11, height = 8.5, units = "in")
# trip segments by hour of day
trip.segments.hour.of.day <- ggmap(bm) +
geom_segment(data = dat, aes(x = from.longitude, y = from.latitude, xend = to.longitude, yend = to.latitude,
colour = occupancy), size = .5) +
ggtitle("Train ridership by hour of the day") +
theme_fivethirtyeight() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
plot.background = element_blank(),
legend.background = element_blank()
) +
facet_wrap(~hour, ncol = 6)
ggsave("IRAIL_stage1_trip_segments_by_hod.pdf", trip.segments.hour.of.day, device = "pdf", width = 11, height = 8.5, units = "in")
# Bar plot of observations by day of the week
day.barplot <- ggplot(dat, aes(x = day_of_week)) +
geom_bar(stat = "count", fill = "grey50") +
ylab("Count of samples") +
ggtitle("Train usage samples by day of week") +
theme(
axis.title = element_text(face = "italic"),
axis.title.x = element_blank(),
plot.title = element_text(face = "bold", hjust = 0.5),
panel.background = element_blank(),
axis.ticks = element_blank()
)
ggsave("IRAIL_stage1_bar_plot_samples_by_dow.pdf", day.barplot, device = "pdf", width = 11, height = 8.5, units = "in")
# Observations time series
daily.obs <- dat$date %>%
table() %>%
data.frame()
colnames(daily.obs) <- c("date", "obs")
daily.obs$date <- as.Date(daily.obs$date)
irail.collection.timeseries <- ggplot(daily.obs, aes(x = date, y = obs)) +
geom_line() +
geom_point(aes(colour = wday(date, label = TRUE))) +
scale_color_discrete("Day of \nthe Week") +
ggtitle("IRAIL traffic data collection time series") +
ylab("# of measurements") +
theme(
panel.background = element_rect(fill = "white"),
panel.grid.minor = element_line(color = "grey90"),
panel.grid.major.y = element_line(color = "grey90"),
axis.title.y = element_text(face = "italic"),
axis.title.x = element_blank(),
plot.title = element_text(hjust = 0.5, face = "bold", size = 24))
ggsave("IRAIL_stage1_data_collection_time_series.pdf", day.barplot, device = "pdf", width = 11, height = 8.5, units = "in")
ggplot(dat, aes(x = occupancy, fill = occupancy)) + geom_bar(stat = "count") +
ggtitle("Dist. of training set occupancy levels") + xlab("") + ylab("Count")
# PREDICTOR VARIABLES -----------------------------------------------------
catv <- md[, !sapply(md, is.numeric)]
catv$occ_binary <- md$occ_binary
catv_tidy <- melt(catv, id.vars = "occ_binary", measure.vars = names(catv)[which(names(catv) != "occ_binary")])
ggplot(catv_tidy, aes(x = as.factor(occ_binary),
fill = value)) +
geom_bar(position = "fill") + facet_wrap(~variable) +
labs(
title = "Categorical predictors"
) +
xlab("Train traffice level (0=low, 1=high)") +
theme_minimal() + theme(
legend.position = "none",
axis.text.y = element_blank()
)
conv <- md[, sapply(md, is.numeric)]
conv_tidy <- melt(conv, id.vars = "occ_binary", measure.vars = names(conv)[which(names(conv) != "occ_binary")])
ggplot(conv_tidy, aes(x = as.factor(occ_binary),
y = value)) +
geom_boxplot() + facet_wrap(~variable, scales = "free") +
labs(
title = "Continuous predictors"
) +
xlab("Train traffic level (0=low, 1=high)") +
theme_minimal() + theme(
legend.position = "none",
axis.text.y = element_blank()
)
# NETOWRK HIERARCHY -------------------------------------------------------
###
mult_k <- ldply(c(5:13), function(x) {
return(findHubs(stations, x))
})
mult_k$k <- factor(mult_k$k, levels(mult_k$k)[c(5:9, 1:4)])
ggplot(mult_k, aes(x = longitude, y = latitude, color = as.factor(groups))) +
geom_point() + geom_label(data = filter(mult_k, maxcount == count), aes(label = name), size = 2) +
theme_void() + facet_wrap(~k, ncol = 3) + ggtitle("Spatial Clustering of Stations, different numbers (k) of clusters") +
theme(
legend.position = "none",
strip.text = element_text(size = 12),
plot.title = element_text(hjust = 0.5)
)
stations$maj_groups <- as.factor(stations$maj_groups)
stations$maj_groups <- factor(stations$maj_groups, levels(stations$maj_groups)[c(2,1,3:5)])
leg_labels <- ddply(stations, ~maj_groups, summarise, name = paste0(min(count), " - ", max(count)))$name
pal <- c('#c7e9b4','#7fcdbb','#41b6c4','#2c7fb8','#253494')
ggplot(stations, aes(x = longitude, y = latitude, color = maj_groups)) +
geom_point(size = 2) +
theme_void() + labs(title = "Belgian Rail Hierarchy", subtitle = "# of trains to come through each station") +
scale_color_manual("Number of trains", values = pal, labels = leg_labels) +
geom_label(data = filter(stations, major_hub == "Y"), aes(label = name), size = 2) +
theme(
legend.position = "right",
plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "in")
)
stations$maj_groups <- stations[,c("count")] %>%
dist(method = "euclidean") %>%
hclust(method="ward.D") %>%
cutree(5) %>%
paste0("mg", .) %>%
as.factor()
# mapping clusters
hubs <- findHubs(st, 5)
for (i in c(6:13)) {
temp <- findHubs(st, i)
hubs <- rbind(hubs, temp)
}
ggplot(hubs, aes(longitude, latitude, color = as.factor(groups))) + geom_point(size = 0.5) +
geom_label(data = filter(hubs, maxcount == count), aes(label = name), size = 2) +
facet_wrap(~k, ncol = 3) + theme_void() + theme(legend.position = "none")
sorted <- arrange(st, desc(count))
sorted$count_rank <- c(1:nrow(sorted))
maxhubs <- head(sorted)
maxhubs$h <- "hubs = 5"
for (i in c(6:13)) {
temp <- head(sorted, i)
temp$h <- paste0("hubs = ", i)
maxhubs <- rbind(maxhubs, temp)
}
ggmap(bmc) +
geom_point(data = maxhubs, aes(longitude, latitude, color = count), size = 2) +
facet_wrap(~h, ncol = 3) + theme_void() + theme(legend.position = "none")
ggplot(filter(hubs, maxcount == count & k == "k = 11"), aes(x = groups, y = count)) +
geom_bar(stat = "identity") + facet_wrap(~k)
|
38b33ccb9acc2b85d68f4e24fe52ade94e2520b2
|
8ad3594325900e5a4715ca4405cd765bc9958158
|
/statistical-inference/goodness-of-fit/exercise-06.r
|
627e5be831eecf8a7f16f9492745c6a269e17943
|
[
"Apache-2.0"
] |
permissive
|
garciparedes/r-examples
|
22806859c7c147a6d503f1b1223a5168b6fa9d76
|
0e0e18439ad859f97eafb27c5e7f77d33da28bc6
|
refs/heads/master
| 2021-01-25T16:59:16.020983
| 2019-05-21T10:26:27
| 2019-05-21T10:26:27
| 102,385,669
| 1
| 0
|
Apache-2.0
| 2018-05-24T07:38:21
| 2017-09-04T17:27:27
|
Jupyter Notebook
|
UTF-8
|
R
| false
| false
| 809
|
r
|
exercise-06.r
|
## Author: Sergio García Prado
## Title: Statistical Inference - Goodness of Fit - Exercise 06
rm(list = ls())
observed <- c(442, 38, 514, 6)
(k <- length(observed))
# 4
(n <- sum(observed))
# 1000
EspectedProbabilities <- function(p) {
c(0.5 * p, 0.5 * (1 - p), 0.5 * p ^ 2 + p * (1 - p), 0.5 * (1 - p) ^ 2)
}
LogLikelihood <- function(p, y) {
sum(y * log(EspectedProbabilities(p)))
}
NegativeLogLikelihood <- function(...) {
- LogLikelihood(...)
}
opt <- optim(0.5, NegativeLogLikelihood, y = observed, hessian = TRUE,
lower = 10e-4, upper = 1 - 10e-4, method = 'L-BFGS-B')
(p.hat <- opt$par)
# 0.912941500560347
expected <- EspectedProbabilities(p.hat) * n
(Q <- sum((observed - expected) ^ 2 / expected))
# 3.08815842598583
(pvalue <- 1 - pchisq(Q, 2))
# 0.213508376478253
|
27bc7bcfbab83d8bdc522e01eb67dae55f5b41d5
|
c87eac12aee2d5403410e925baf8b4e5ec295475
|
/play_sequence_generators/test_prebuilt_lstm.R
|
ad1c5f68d17f2ecd92dded9df5c20b616522ccc7
|
[
"Apache-2.0"
] |
permissive
|
cxd/text_dnn_experiments
|
9cbc7274b4c760f801d5074229be2bdfb3ef64cc
|
4e57ca2db4151ba3796583abd0ed3bf2feaf8356
|
refs/heads/master
| 2021-06-14T07:13:36.161787
| 2021-03-11T10:14:00
| 2021-03-11T10:14:00
| 158,998,082
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,153
|
r
|
test_prebuilt_lstm.R
|
library(keras)
library(stringr)
source("lib/init.R")
source("lib/prepare_squad_data.R")
source("lib/read_glove.R")
source("lib/lstm_sequence_learner.R")
# Setup environment
cfg <- init(getwd())
prebuilt <- "test/bri-data-01/model3.h5"
modelTest <- load_model_hdf5(prebuilt, compile=TRUE)
path <- get_file(
"nietzsche.txt",
origin = "https://s3.amazonaws.com/text-datasets/nietzsche.txt"
)
text <- tolower(readChar(path, file.info(path)$size))
# Select a text seed at random
## Note the shortcoming of this model is that it is a sequence generator purely for
## sequences of text that it has seen before. These are the discrete character sequences
## that it has trained on. It is not capable of taking a sequence of characters that it has
## not been trained on and stringing togethor the next sequence of possible characters.
maxlen <- 60
start_index <- sample(1:(nchar(text) - maxlen - 1), 1)
seed_text <- str_sub(text, start_index, start_index + maxlen - 1)
(prediction <- predict_sequence_of_length(modelTest, seed_text, temperature=0.5))
(prediction2 <- predict_sequence_until(modelTest, seed_text, window=60, temperature=0.6))
|
ce2b19b78239e96c46b52f94cd4c74e1bb220a22
|
510734b2e6f1fe4110177aa90e647739764b737d
|
/man/rename_states.Rd
|
c182fa72aaf765d4240b19977c15cdb03b785e0c
|
[] |
no_license
|
helske/KFAS
|
4be85a2db7c33c9c1e7c95d66f0fa26ccdd6b764
|
e183590a08cce796763451a023e6714a52ce83fe
|
refs/heads/master
| 2023-03-11T03:07:50.040079
| 2023-02-06T15:12:09
| 2023-02-06T15:12:09
| 18,439,915
| 56
| 19
| null | 2016-06-11T12:28:15
| 2014-04-04T13:32:14
|
R
|
UTF-8
|
R
| false
| true
| 1,085
|
rd
|
rename_states.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rename_states.R
\name{rename_states}
\alias{rename_states}
\title{Rename the States of SSModel Object}
\usage{
rename_states(model, state_names)
}
\arguments{
\item{model}{Object of class SSModel}
\item{state_names}{Character vector giving new names for the states.}
}
\value{
Original model with dimnames corresponding to states renamed.
}
\description{
A simple function for renaming the states of \code{\link{SSModel}} object.
Note that since KFAS version 1.2.3 the auxiliary functions such as
\code{\link{SSMtrend}} have argument \code{state_names} which can be used to
overwrite the default state names when building the model with \code{\link{SSModel}}.
}
\examples{
custom_model <- SSModel(1:10 ~ -1 +
SSMcustom(Z = 1, T = 1, R = 1, Q = 1, P1inf = 1), H = 1)
custom_model <- rename_states(custom_model, "level")
ll_model <- SSModel(1:10 ~ SSMtrend(1, Q = 1), H = 1)
test_these <- c("y", "Z", "H", "T", "R", "Q", "a1", "P1", "P1inf")
identical(custom_model[test_these], ll_model[test_these])
}
|
dcaf315d43d018e823c86dffd72da8ecef1e09c9
|
6ef05ff1b841edfeea7a2e54e055d03a450b7469
|
/R/get.adjacency.matrix.R
|
92168ae72a4fc991cead750a124dd0cc5984ad07
|
[] |
no_license
|
cran/SIMMS
|
c0527ec2c154495a8dc3fb18237f47a5b808f999
|
7e3d61a1757bc01b3df19576814d381855388b74
|
refs/heads/master
| 2022-05-02T23:05:38.840427
| 2022-04-24T13:50:05
| 2022-04-24T13:50:05
| 17,693,536
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,161
|
r
|
get.adjacency.matrix.R
|
#' A utility function to convert tab delimited networks file into adjacency
#' matrices
#'
#' A utility function to convert tab-delimited networks file into adjacency
#' matrices
#'
#'
#' @param subnets.file A tab-delimited file containing networks. New networks
#' start with a new line with '#' at the begining of network name and
#' subsequent lines contain a binary interaction per line
#' @return A list of adjacency matrices
#' @author Syed Haider
#' @keywords Networks
#' @examples
#'
#' subnets.file <- get.program.defaults()[["subnets.file"]];
#' all.adjacency.matrices <- get.adjacency.matrix(subnets.file);
#'
#' @export get.adjacency.matrix
get.adjacency.matrix <- function(subnets.file = NULL) {
all.adjacency.matrices <- list();
subnets <- readLines(subnets.file, ok = TRUE);
graph.name <- "";
vertices <- "";
interactions <- "";
for(i in seq(1, length(subnets), 1)) {
# check if its a header line
if (length(grep("^#", subnets[i], perl = TRUE)) > 0) {
# time to process previous subgraph
if (nchar(as.character(vertices)) > 0) {
# make a matrix of this graph
adjacency.matrix <- make.matrix(vertices, interactions);
all.adjacency.matrices[[graph.name]] <- adjacency.matrix;
# reinitialise everything else
vertices <- "";
interactions <- "";
}
graph.name <- make.names(gsub("\t$", "", subnets[i]));
}
else {
id.p1.p2 <- unlist(strsplit(subnets[i], "\t"));
id.p1.p2 <- gsub("\\(|\\)", "-", id.p1.p2, perl=TRUE);
p1 <- paste("\"",id.p1.p2[2],"\"", sep="");
p2 <- paste("\"",id.p1.p2[3],"\"", sep="");
# this vertex is not already seen in this sub graph, lets add
if (length(grep(p1, vertices, perl = TRUE)) < 1) {
vertices <- paste(vertices, p1, sep=",");
}
if (length(grep(p2, vertices, perl = TRUE)) < 1) {
vertices <- paste(vertices, p2, sep = ",");
}
interactions <- paste(interactions, ",\"", id.p1.p2[2],":", id.p1.p2[3],"\"", sep="");
}
}
# make a matrix of this graph
adjacency.matrix <- make.matrix(vertices, interactions);
all.adjacency.matrices[[graph.name]] <- adjacency.matrix;
return (all.adjacency.matrices);
}
|
7c9992b712d51d5f381601b43c026bcd357d4c6c
|
c05e0de22f5699d1c2b2921480be68c8e8b8943f
|
/man/tab_caption.Rd
|
afc368c9dce30835382afd96867ca9d83cc85577
|
[
"MIT"
] |
permissive
|
rstudio/gt
|
36ed1a3d5d9a1717dfe71ed61e5c005bc17e0dce
|
c73eeceaa8494180eaf2f0ad981056c53659409b
|
refs/heads/master
| 2023-09-04T06:58:18.903630
| 2023-09-01T02:06:05
| 2023-09-01T02:06:05
| 126,038,547
| 1,812
| 225
|
NOASSERTION
| 2023-09-08T00:21:34
| 2018-03-20T15:18:51
|
R
|
UTF-8
|
R
| false
| true
| 2,404
|
rd
|
tab_caption.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tab_create_modify.R
\name{tab_caption}
\alias{tab_caption}
\title{Add a table caption}
\usage{
tab_caption(data, caption)
}
\arguments{
\item{data}{\emph{The gt table data object}
\verb{obj:<gt_tbl>} // \strong{required}
This is the \strong{gt} table object that is commonly created through use of the
\code{\link[=gt]{gt()}} function.}
\item{caption}{\emph{Table caption text}
\verb{scalar<character>} // \strong{required}
The table caption to use for cross-referencing in R Markdown, Quarto, or
\strong{bookdown}.}
}
\value{
An object of class \code{gt_tbl}.
}
\description{
Add a caption to a \strong{gt} table, which is handled specially for a table
within an R Markdown, Quarto, or \strong{bookdown} context. The addition of
captions makes tables cross-referencing across the containing document. The
caption location (i.e., top, bottom, margin) is handled at the document level
in each of these system.
}
\section{Examples}{
With three columns from the \code{\link{gtcars}} dataset, let's create a \strong{gt} table.
First, we'll add a header part with the \code{\link[=tab_header]{tab_header()}} function. After that,
a caption is added through use of \code{tab_caption()}.
\if{html}{\out{<div class="sourceCode r">}}\preformatted{gtcars |>
dplyr::select(mfr, model, msrp) |>
dplyr::slice(1:5) |>
gt() |>
tab_header(
title = md("Data listing from **gtcars**"),
subtitle = md("`gtcars` is an R dataset")
) |>
tab_caption(caption = md("**gt** table example."))
}\if{html}{\out{</div>}}
\if{html}{\out{
<img src="https://raw.githubusercontent.com/rstudio/gt/master/images/man_tab_caption_1.png" alt="This image of a table was generated from the first code example in the `tab_caption()` help file." style="width:100\%;">
}}
}
\section{Function ID}{
2-9
}
\section{Function Introduced}{
\code{v0.8.0} (November 16, 2022)
}
\seealso{
Other part creation/modification functions:
\code{\link{tab_footnote}()},
\code{\link{tab_header}()},
\code{\link{tab_info}()},
\code{\link{tab_options}()},
\code{\link{tab_row_group}()},
\code{\link{tab_source_note}()},
\code{\link{tab_spanner_delim}()},
\code{\link{tab_spanner}()},
\code{\link{tab_stub_indent}()},
\code{\link{tab_stubhead}()},
\code{\link{tab_style_body}()},
\code{\link{tab_style}()}
}
\concept{part creation/modification functions}
|
c80fc7ec62cd2ab4453e7f7563d75269d5ea1b37
|
c221bac282063ef7c50923eb6ae422b81bda8af8
|
/GoodnessOfFit.R
|
db69ad564883a27503814aa42a23941c0b9a13a9
|
[] |
no_license
|
daviddwlee84/StatisticInference
|
60481b9747371817e641244b4f80824b4223dabd
|
d02e3d33fd8e91a31f5bbcdd8d5bf990939981d4
|
refs/heads/master
| 2021-01-25T11:28:21.971166
| 2017-07-02T02:28:26
| 2017-07-02T02:28:26
| 93,928,780
| 2
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 536
|
r
|
GoodnessOfFit.R
|
# Goodness of fit
source("GoodnessOfFitFunctions.R")
alpha = as.numeric(readline("Significance level(in %): "))
alpha = alpha/100
dist <- menu(c("Multinomial Distribution", "Normal Distribution", "Poisson Distribution", "Binomial Distribution"), title="Select the distribution of the Sample Statistic")
if(dist == 1){
sce <- menu(c("Test if the die is fair"), title="Select the scenario of Sample Statistic")
if(sce == 1){
K = as.numeric(readline("How many cell each die (K nomial)? "))
GOF_MULTINOM_DIE(alpha, K)
}
}
|
b1c549fbb6df3d6e66c9a825242cb9c4dc74ad90
|
b844fc764deff4c305d5a5499f78266f2ec817e9
|
/man/fitted.mylm.Rd
|
b19e30afabeff13c30cef2f0826c1ec953335cbf
|
[] |
no_license
|
jenper/mylm
|
0aa7c1e7498a35dc9225e26dc2f75f35ab5a808a
|
785316d117b822c2edf9b63ff5d1c1e7f7902c22
|
refs/heads/main
| 2023-08-03T10:49:26.281973
| 2021-07-22T01:41:54
| 2021-07-22T01:41:54
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 332
|
rd
|
fitted.mylm.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/funcs.R
\name{fitted.mylm}
\alias{fitted.mylm}
\title{Fitted values}
\usage{
\method{fitted}{mylm}(object, ...)
}
\arguments{
\item{object}{object of class "mylm"}
\item{...}{additional arguments to be passed to methods}
}
\description{
Fitted values
}
|
2fd056d93f11620a97d4d17b37a0c7a7f86d41e1
|
530753dfb8c6b2db7d32e1de9b69e9c04df3c501
|
/cachematrix.R
|
9635c8e3259c98683846de0ace6851e79df3ecf8
|
[] |
no_license
|
khomyuk/ProgrammingAssignment2
|
45f3617ff88fb4d422e0dbf5447849dcd54fb10c
|
a8859a26b5236b7c905580322d8ca04647c9b10f
|
refs/heads/master
| 2021-01-15T11:08:17.863618
| 2015-12-27T23:10:12
| 2015-12-27T23:10:12
| 48,662,006
| 0
| 0
| null | 2015-12-27T22:00:18
| 2015-12-27T22:00:15
| null |
UTF-8
|
R
| false
| false
| 886
|
r
|
cachematrix.R
|
## The function makeCacheMatrix allows you to create an oblect of a matrix that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inverted_matrix <- NULL
set <- function(y) {
x <<- y
inverted_matrix <<- NULL
}
get <- function() x
setinv <- function(inv) inverted_matrix <<- inv
getinv <- function() inverted_matrix
list(set = set, get = get, setinv = setinv, getinv = getinv)
}
## The function cacheSolve computes the inverted matrix.
## In case it has already been computed earlier this function returns cached value.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inverted_matrix <- x$getinv()
if(!is.null(inverted_matrix)) {
message("getting cached inverse")
return(inverted_matrix)
}
data <- x$get()
inverted_matrix <- solve(data,...)
x$setinv(inverted_matrix)
inverted_matrix
}
|
049fb55dec8a56e5625293dbd9a742810115419c
|
3cb0fcdaa83cb2bc60aef905d229c00ac4440243
|
/R/ImmuneSpace.R
|
946854d4f37f169e3af928d9b276672cdc863068
|
[] |
no_license
|
jfrelinger/ImmuneSpaceR
|
45325d9841391dd6a7992d550ab777e5bff11b15
|
deefe0813ffd0b101b10c5c63fd549c21443a4e9
|
refs/heads/master
| 2021-01-21T19:13:20.752212
| 2014-11-19T21:29:58
| 2014-11-19T21:29:58
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 28,650
|
r
|
ImmuneSpace.R
|
#'@docType package
#'@title A Thin Wrapper Around ImmuneSpace.
#'@description ImmuneSpaceR provides a convenient API for accessing data sets within the ImmuneSpace database.
#'
#'@details Uses the Rlabkey package to connect to ImmuneSpace. Implements caching, and convenient methods for accessing data sets.
#'
#'@name ImmuneSpaceR-package
#'@aliases ImmuneSpaceR
#'@author Greg Finak
#'@import data.table Rlabkey methods Biobase gtools digest
NULL
#'@title CreateConnection
#'@name CreateConnection
#'@param study \code{"character"} vector naming the study.
#'@description Constructor for \code{ImmuneSpaceConnection} class
#'@details Instantiates and \code{ImmuneSpaceConnection} for \code{study}
#'The constructor will try to take the values of the various `labkey.*` parameters from the global environment.
#'If they don't exist, it will use default values. These are assigned to `options`, which are then used by the \code{ImmuneSpaceConnection} class.
#'@export CreateConnection
#'@return an instance of an \code{ImmuneSpaceConnection}
CreateConnection = function(study=NULL, verbose = FALSE){
labkey.url.path<-try(get("labkey.url.path",.GlobalEnv),silent=TRUE)
if(inherits(labkey.url.path,"try-error")){
if(is.null(study)){
stop("study cannot be NULL")
}
labkey.url.path<-paste0("/Studies/",study)
}else if(!is.null(study)){
labkey.url.path<-file.path(dirname(labkey.url.path),study)
}
labkey.url.base<-try(get("labkey.url.base",.GlobalEnv),silent=TRUE)
if(inherits(labkey.url.base,"try-error"))
labkey.url.base<-"https://www.immunespace.org"
labkey.url.base<-gsub("http:","https:",labkey.url.base)
if(length(grep("^https://", labkey.url.base)) == 0)
labkey.url.base <- paste0("https://", labkey.url.base)
labkey.user.email<-try(get("labkey.user.email",.GlobalEnv),silent=TRUE)
if(inherits(labkey.user.email,"try-error"))
labkey.user.email="unknown_user at not_a_domain.com"
options(labkey.url.base=labkey.url.base)
options(labkey.url.path=labkey.url.path)
options(labkey.user.email=labkey.user.email)
options(ISverbose = verbose)
new("ImmuneSpaceConnection")
}
#'@name ImmuneSpaceConnection
#'@aliases ImmuneSpaceConnection-class
#'@aliases ImmuneSpace
#'@rdname ImmuneSpaceConnection-class
#'@docType class
#'@title The ImmuneSpaceConnection class
#'@description Instantiate this class to access a study
#'@details Uses global variables \code{labkey.url.base}, and \code{labkey.url.path}, to access a study.
#'\code{labkey.url.base} should be \code{https://www.immunespace.org/}.
#'\code{labkey.url.path} should be \code{/Studies/studyname}, where 'studyname' is the name of the study.
#'The ImmunespaceConnection will initialize itself, and look for a \code{.netrc} file in \code{"~/"} the user's home directory.
#'The \code{.netrc} file should contain a \code{machine}, \code{login}, and \code{password} entry to allow access to ImmuneSpace,
#'where \code{machine} is the host name like "www.immunespace.org".
#'@seealso \code{\link{ImmuneSpaceR-package}} \code{\link{ImmuneSpaceConnection_getGEMatrix}} \code{\link{ImmuneSpaceConnection_getDataset}} \code{\link{ImmuneSpaceConnection_listDatasets}}
#'@exportClass ImmuneSpaceConnection
#'@examples
#'labkey.url.base <- "https://www.immunespace.org"
#'labkey.url.path <- "/Studies/SDY269"
#'labkey.user.email <- 'gfinak at fhcrc.org'
#'sdy269 <- CreateConnection("SDY269")
#'sdy269
#'@return An instance of an ImmuneSpaceConnection for a study in `labkey.url.path`
setRefClass(Class = "ImmuneSpaceConnection",
fields = list(study = "character", config="list",
available_datasets = "data.table",
data_cache="list",constants="list"),
methods=list(
initialize=function(){
constants<<-list(matrices="GE_matrices",matrix_inputs="GE_inputs")
.AutoConfig()
gematrices_success<-try(.GeneExpressionMatrices(),silent=TRUE)
geinputs_success<-try(.GeneExpressionInputs(),silent=TRUE)
if(inherits(gematrices_success,"try-error")){
message("No gene expression data")
}
},
.AutoConfig=function(){
#should use options
labkey.url.base<-getOption("labkey.url.base")
labkey.url.path<-getOption("labkey.url.path")
labkey.user.email<-getOption("labkey.user.email")
verbose <- getOption("ISverbose")
if(gsub("https://", "", labkey.url.base) == "www.immunespace.org"){
curlOptions <- labkey.setCurlOptions(ssl.verifyhost = 2, ssl.cipher.list="ALL")
} else{
curlOptions <- labkey.setCurlOptions(ssl.verifyhost = 2, sslversion=1)
}
study<<-basename(labkey.url.path)
config<<-list(labkey.url.base=labkey.url.base,
labkey.url.path=labkey.url.path,
labkey.user.email=labkey.user.email,
curlOptions = curlOptions,
verbose = verbose)
.getAvailableDataSets();
},
show=function(){
cat(sprintf("Immunespace Connection to study %s\n",study))
cat(sprintf("URL: %s\n",file.path(gsub("/$","",config$labkey.url.base),gsub("^/","",config$labkey.url.path))))
cat(sprintf("User: %s\n",config$labkey.user.email))
cat("Available datasets\n")
for(i in 1:nrow(available_datasets)){
cat(sprintf("\t%s\n",available_datasets[i,Name]))
}
if(!is.null(data_cache[[constants$matrices]])){
cat("Expression Matrices\n")
for(i in 1:nrow(data_cache[[constants$matrices]])){
cat(sprintf("%s\n",data_cache[[constants$matrices]][i,"name"]))
}
}
},
# There is something odd with Rlabkey::labkey.getFolders (permissions set to 0)
.checkStudy = function(verbose = FALSE){
browser()
if(length(available_datasets)==0){
validStudies <- mixedsort(grep("^SDY", basename(lsFolders(getSession(config$labkey.url.base, "Studies"))), value = TRUE))
req_study <- basename(config$labkey.url.path)
if(!req_study %in% validStudies){
if(!verbose){
stop(paste0(req_study, " is not a valid study"))
} else{
stop(paste0(req_study, " is not a valid study\nValid studies: ",
paste(validStudies, collapse=", ")))
}
}
}
},
.getAvailableDataSets=function(){
if(length(available_datasets)==0){
dataset_filter <- makeFilter(c("showbydefault", "EQUAL", TRUE))
available_datasets<<-data.table(labkey.selectRows(baseUrl = config$labkey.url.base,config$labkey.url.path,schemaName = "study",queryName = "DataSets", colFilter = dataset_filter))[,list(Label,Name,Description,`Key Property Name`)]
}
},
getDataset=function(x, original_view = FALSE, reload=FALSE, ...){
if(nrow(available_datasets[Name%in%x])==0){
stop(sprintf("Invalid data set: %s",x))
}else{
hash_key = digest(c(x,original_view))
if(!is.null(data_cache[[hash_key]])&!reload){
data_cache[[hash_key]]
}else{
viewName <- NULL
if(original_view){
viewName <- "full"
}
data_cache[[hash_key]] <<- data.table(labkey.selectRows(baseUrl = config$labkey.url.base,config$labkey.url.path,schemaName = "study", queryName = x, viewName = viewName, colNameOpt = "fieldname", ...))
setnames(data_cache[[hash_key]],.munge(colnames(data_cache[[hash_key]])))
data_cache[[hash_key]]
}
}
},
listDatasets=function(){
for(i in 1:nrow(available_datasets)){
cat(sprintf("\t%s\n",available_datasets[i,Name]))
}
if(!is.null(data_cache[[constants$matrices]])){
cat("Expression Matrices\n")
for(i in 1:nrow(data_cache[[constants$matrices]])){
cat(sprintf("%s\n",data_cache[[constants$matrices]][i,"name"]))
}
}
},
.munge=function(x){
tolower(gsub(" ","_",basename(x)))
},
.GeneExpressionInputs=function(){
if(!is.null(data_cache[[constants$matrix_inputs]])){
data_cache[[constants$matrix_inputs]]
}else{
ge<-labkey.selectRows(baseUrl = config$labkey.url.base,config$labkey.url.path,schemaName = "assay.ExpressionMatrix.matrix",queryName = "InputSamples",colNameOpt = "fieldname",viewName = "gene_expression_matrices",showHidden=TRUE)
setnames(ge,.munge(colnames(ge)))
data_cache[[constants$matrix_inputs]]<<-ge
}
},
.GeneExpressionFeatures=function(matrix_name,summary=FALSE){
if(!any((data_cache[[constants$matrices]][,"name"]%in%matrix_name))){
stop("Invalid gene expression matrix name");
}
annotation_set_id<-.getFeatureId(matrix_name)
#.lksession <- list()
#.lksession[["curlOptions"]] <- config$curlOptions
#.lksession[["curlOptions"]]$httpauth <- 1L
#print(.lksession[["curlOptions"]])
if(is.null(data_cache[[.mungeFeatureId(annotation_set_id)]])){
if(!summary){
message("Downloading Features..")
featureAnnotationSetQuery=sprintf("SELECT * from FeatureAnnotation where FeatureAnnotationSetId='%s';",annotation_set_id);
features<-labkey.executeSql(config$labkey.url.base,config$labkey.url.path,schemaName = "Microarray",sql = featureAnnotationSetQuery ,colNameOpt = "fieldname")
}else{
features<-data.frame(FeatureId=con$data_cache[[matrix_name]][,gene_symbol],GeneSymbol=con$data_cache[[matrix_name]][,gene_symbol])
}
data_cache[[.mungeFeatureId(annotation_set_id)]]<<-features
}
},
.GeneExpressionMatrices=function(){
if(!is.null(data_cache[[constants$matrices]])){
data_cache[[constants$matrices]]
}else{
ge<-labkey.selectRows(baseUrl = config$labkey.url.base,config$labkey.url.path,schemaName = "assay.ExpressionMatrix.matrix",queryName = "Runs",colNameOpt = "fieldname",showHidden = TRUE, viewName = "expression_matrices")
setnames(ge,.munge(colnames(ge)))
data_cache[[constants$matrices]]<<-ge
}
},
.downloadMatrix=function(x, summary = FALSE){
if(is.null(data_cache[[x]])){
if(nrow(subset(data_cache[[constants$matrices]],name%in%x))==0){
stop(sprintf("No matrix %s in study\n",x))
}
summary <- ifelse(summary, ".summary", "")
#link<-URLdecode(file.path(gsub("www.","",gsub("http:","https:",gsub("/$","",config$labkey.url.base))), paste0(gsub("^/","",subset(data_cache[[constants$matrices]],name%in%x)[,"downloadlink"]),summary)))
#shouldn't be removing the www reported by labkey. Fix your netrc entry instead
link<-URLdecode(file.path(gsub("http:","https:",gsub("/$","",config$labkey.url.base)),
"_webdav", gsub("^/","",config$labkey.url.path), "@files/analysis/exprs_matrices",
paste0(x, ".tsv", summary)))
localpath<-.localStudyPath(link)
if(.isRunningLocally(localpath)){
fl<-localpath
message("Reading local matrix")
data_cache[[x]]<<-fread(fl,header=TRUE)
}else{
opts <- config$curlOptions
opts$netrc <- 1L
opts$httpauth <- 1L
handle<-getCurlHandle(.opts=opts)
h<-basicTextGatherer()
message("Downloading matrix..")
curlPerform(url=link,curl=handle,writefunction=h$update)
fl<-tempfile()
write(h$value(),file=fl)
EM <- fread(fl,header=TRUE)
if(nrow(EM) == 0){
stop("The downloaded matrix has 0 rows. Something went wrong")
}
data_cache[[x]] <<-EM
file.remove(fl)
}
}else{
data_cache[[x]]
}
},
getGEMatrix=function(x, summary = FALSE){
if(x%in%names(data_cache)){
data_cache[[x]]
}else{
.downloadMatrix(x, summary)
.GeneExpressionFeatures(x,summary)
.ConstructExpressionSet(x, summary)
data_cache[[x]]
}
},
.ConstructExpressionSet=function(matrix_name, summary){
#matrix
message("Constructing ExpressionSet")
matrix<-data_cache[[matrix_name]]
#features
features<-data_cache[[.mungeFeatureId(.getFeatureId(matrix_name))]][,c("FeatureId","GeneSymbol")]
#inputs
pheno<-unique(subset(data_cache[[constants$matrix_inputs]],biosample_accession%in%colnames(matrix))[,c("biosample_accession","subject_accession","arm_name","study_time_collected")])
if(summary){
fdata <- data.frame(FeatureId = matrix$gene_symbol, gene_symbol = matrix$gene_symbol, row.names = matrix$gene_symbol)
fdata <- AnnotatedDataFrame(fdata)
} else{
try(setnames(matrix," ","FeatureId"),silent=TRUE)
setkey(matrix,FeatureId)
rownames(features)<-features$FeatureId
features<-features[matrix$FeatureId,]#order feature info
fdata <- AnnotatedDataFrame(features)
}
rownames(pheno)<-pheno$biosample_accession
pheno<-pheno[colnames(matrix)[-1L],]
ad_pheno<-AnnotatedDataFrame(data=pheno)
es<-ExpressionSet(assayData=as.matrix(matrix[,-1L,with=FALSE]),phenoData=ad_pheno,featureData=fdata)
data_cache[[matrix_name]]<<-es
},
.getFeatureId=function(matrix_name){
subset(data_cache[[constants$matrices]],name%in%matrix_name)[,"featureset"]
},
.mungeFeatureId=function(annotation_set_id){
return(sprintf("featureset_%s",annotation_set_id))
},
.isRunningLocally=function(path){
file.exists(path)
},
.localStudyPath=function(urlpath){
LOCALPATH<-"/shared/silo_researcher/Gottardo_R/immunespace"
PRODUCTION_HOST<-"www.immunespace.org"
STAGING_HOST<-"posey.fhcrc.org"
TEST_HOST<-"test.immunespace.org"
PRODUCTION_PATH<-"production/files"
STAGING_PATH<-"staging/files"
if(grepl(PRODUCTION_HOST,urlpath)){
PROCESS<-PRODUCTION_PATH
}else if(grepl(STAGING_HOST,urlpath)){
PROCESS<-STAGING_PATH
}else if(grepl(TEST_HOST,urlpath)){
LOCALPATH <- "/share/files"
PROCESS <- ""
}else{
stop("Can't determine if we are running on immunespace (production) or posey (staging)")
}
gsub(file.path(gsub("/$","",config$labkey.url.base), "_webdav"), file.path(LOCALPATH,PROCESS), urlpath)
},
listGEAnalysis = function(){
GEA <- labkey.selectRows(config$labkey.url.base, config$labkey.url.path,
"gene_expression", "gene_expression_analysis",
colNameOpt = "rname")
print(GEA)
},
getGEAnalysis = function(analysis_accession){
"Get gene expression analysis resluts from a connection"
if(missing(analysis_accession)){
stop("Missing analysis_accession argument.
Use listGEAnalysis to get a list of available
analysis_accession numbers")
}
AA_filter <- makeFilter(c("analysis_accession", "IN", analysis_accession))
GEAR <- labkey.selectRows(config$labkey.url.base, config$labkey.url.path,
"gene_expression", "gene_expression_analysis_results",
colFilter = AA_filter)
colnames(GEAR) <- .munge(colnames(GEAR))
return(GEAR)
},
clear_cache = function(){
data_cache[grep("^GE", names(data_cache), invert = TRUE)] <<- NULL
},
.qpHeatmap = function(dt, normalize_to_baseline, legend, text_size){
contrast <- "study_time_collected"
annoCols <- c("name", "subject_accession", contrast, "Gender", "Age", "Race")
palette <- ISpalette(20)
expr <- parse(text = paste0(contrast, ":=as.factor(", contrast, ")"))
dt <- dt[, eval(expr)]
#No need to order by legend. This should be done after.
if(!is.null(legend)){
dt <- dt[order(name, study_time_collected, get(legend))]
} else{
dt <- dt[order(name, study_time_collected)]
}
form <- as.formula(paste("analyte ~ name +", contrast, "+ subject_accession"))
mat <- acast(data = dt, formula = form, value.var = "response") #drop = FALSE yields NAs
if(ncol(mat) > 2 & nrow(mat) > 1){
mat <- mat[rowSums(apply(mat, 2, is.na)) < ncol(mat),, drop = FALSE]
}
# Annotations:
anno <- data.frame(unique(dt[, annoCols, with = FALSE]))
rownames(anno) <- paste(anno$name, anno[, contrast], anno$subject_accession, sep = "_")
expr <- parse(text = c(rev(legend), contrast, "name"))
anno <- anno[with(anno, order(eval(expr))),]
anno <- anno[, c(rev(legend), contrast, "name")] #Select and order the annotation rows
anno[, contrast] <- as.factor(anno[, contrast])
anno_color <- colorpanel(n = length(levels(anno[,contrast])), low = "white", high = "black")
names(anno_color) <- levels(anno[, contrast])
anno_color <- list(anno_color)
if(contrast == "study_time_collected"){
setnames(anno, c("name", contrast), c("Arm Name", "Time"))
contrast <- "Time"
}
names(anno_color) <- contrast
if("Age" %in% legend){
anno_color$Age <- c("yellow", "red")
}
mat <- mat[, rownames(anno), drop = FALSE]
# pheatmap parameters
if(normalize_to_baseline){
scale <- "none"
max <- max(abs(mat), na.rm = TRUE)
breaks <- seq(-max, max, length.out = length(palette))
} else{
scale <- "row"
breaks <- NA
}
show_rnames <- ifelse(nrow(mat) < 50, TRUE, FALSE)
cluster_rows <- ifelse(nrow(mat) > 2 & ncol(mat) > 2, TRUE, FALSE)
e <- try({
p <- pheatmap(mat = mat, annotation = anno, show_colnames = FALSE,
show_rownames = show_rnames, cluster_cols = FALSE,
cluster_rows = cluster_rows, color = palette,
scale = scale, breaks = breaks,
fontsize = text_size, annotation_color = anno_color)
})
if(inherits(e, "try-error")){
p <- pheatmap(mat = mat, annotation = anno, show_colnames = FALSE,
show_rownames = show_rnames, cluster_cols = FALSE,
cluster_rows = FALSE, color = palette,
scale = scale, breaks = breaks,
fontsize = text_size, annotation_color = anno_color)
}
return(p)
},
quick_plot = function(dataset, normalize_to_baseline = TRUE,
type = "auto", filter = NULL,
facet = "grid", text_size = 15,
legend = NULL, ...){
ggthemr("solarized")
addPar <- c("Gender", "Age", "Race")
annoCols <- c("name", "subject_accession", "study_time_collected", addPar)
toKeep <- c("response", "analyte", annoCols)
logT <- TRUE #By default, log transform the value_reported
message_out <- ""
extras <- list(...)
e <- try({
dt <- con$getDataset(dataset, reload = TRUE, colFilter = filter)
setnames(dt, c("gender", "age_reported", "race"), addPar)
if(!"analyte" %in% colnames(dt)){
if("analyte_name" %in% colnames(dt)){
dt <- dt[, analyte := analyte_name]
} else{
dt <- dt[, analyte := ""]
}
}
if(type == "auto"){
if(length(unique(dt$analyte)) < 10){
type <- "boxplot"
} else{
type <- "heatmap"
}
}
# Datasets
if(dataset == "elispot"){
dt <- dt[, value_reported := (spot_number_reported) / cell_number_reported]
} else if(dataset == "pcr"){
if(all(is.na(dt[, threshold_cycles]))){
stop("PCR results cannot be displayed for studies that do not use threshold cycles.
Use LabKey Quick Chart interface to plot this dataset.")
}
dt <- dt[, value_reported := threshold_cycles]
dt <- dt[, analyte := entrez_gene_id]
logT <- FALSE #Threshold cycle is already log transformed
} else if(dataset == "mbaa"){
if(all(dt$concentration_value ==0) || all(is.na(dt$concentration_value))){
if(any(!is.na(dt$mfi)) && any(dt$mfi != 0)){
dt <- dt[, value_reported := as.numeric(mfi)]
}else{
stop("Plotting MBAA requires either concentration or MFI values")
}
} else{
dt <- dt[, value_reported := as.numeric(concentration_value)]
}
}
dt <- dt[, response := ifelse(value_reported <0, 0, value_reported)]
if(logT){
dt <- dt[, response := mean(log2(response+1), na.rm = TRUE),
by = "name,subject_accession,analyte,study_time_collected"]
} else{
dt <- dt[, response := mean(response, na.rm = TRUE),
by = "name,subject_accession,analyte,study_time_collected"]
}
dt <- unique(dt[, toKeep, with = FALSE])
if(normalize_to_baseline){
dt <- dt[,response:=response-response[study_time_collected==0],
by="name,subject_accession,analyte"][study_time_collected!=0]
ylab <- "Response normalized to baseline"
} else{
ylab <- "Response (log2)"
}
})
if(inherits(e, "try-error")){
type <- "error"
error_string <- attr(e, "condition")$message
}
# Plot
if(facet == "grid"){
facet <- facet_grid(aes(analyte, name), scales = "free")
} else if(facet == "wrap"){
facet <- facet_wrap(~name + analyte, scales = "free")
}
if(type == "heatmap"){
p <- .qpHeatmap(dt, normalize_to_baseline, legend, text_size)
} else if(type == "boxplot"){
p <- ggplot(data = dt, aes(as.factor(study_time_collected), response)) +
geom_boxplot(outlier.size = 0) +
xlab("Time") + ylab(ylab) + facet +
theme(text = element_text(size = text_size),
axis.text.x = element_text(angle = 45))
if(!is.null(extras[["size"]])){
p <- p + geom_jitter(aes_string(...))
} else{
p <- p + geom_jitter(size = 3, aes_string(...))
}
print(p)
} else if(type == "line"){
p <- ggplot(data = dt, aes(study_time_collected, response, group = subject_accession)) +
geom_line(aes_string(...)) +
xlab("Time") + ylab(ylab) + facet +
theme(text = element_text(size = text_size),
axis.text.x = element_text(angle = 45))
if(!is.null(extras[["size"]])){
p <- p + geom_point(aes_string(...))
} else{
p <- p + geom_point(size = 3, aes_string(...))
}
print(p)
} else{#} if(type == "error"){
data <- data.frame(x = 0, y = 0, err = error_string)
p <- ggplot(data = data) + geom_text(aes(x, y, label = err), size = text_size)
print(p)
}
return(message_out)
}
))
#'@title get Gene Expression Matrix
#'@aliases getGEMatrix
#'@param x \code{"character"} name of the Gene Expression Matrix
#'@details Returns an `ExpressionSet` from the matrix named 'x', downloads it if it is not already cached.
#'@return an \code{ExpressionSet}
#'@name ImmuneSpaceConnection_getGEMatrix
#'@examples
#'labkey.url.base="https://www.immunespace.org"
#'labkey.url.path="/Studies/SDY269"
#'labkey.user.email='gfinak at fhcrc.org'
#'sdy269<-CreateConnection("SDY269")
#'sdy269$getGEMatrix("TIV_2008")
NULL
#'@title get a dataset
#'@aliases getDataset
#'@param x A \code{character}. The name of the dataset
#'@param original_view A \code{logical}. If set to TRUE, download the ImmPort view. Else,
#' download the default grid view.
#'@param reload A \code{logical}. Clear the cache. If set to TRUE, download the
#' dataset, whether a cached versio exist or not.
#'@details Returns the dataset named 'x', downloads it if it is not already cached.
#'@return a \code{data.table}
#'@name ImmuneSpaceConnection_getDataset
#'@examples
#'labkey.url.base="https://www.immunespace.org"
#'labkey.url.path="/Studies/SDY269"
#'labkey.user.email='gfinak at fhcrc.org'
#'sdy269<-CreateConnection("SDY269")
#'sdy269$getDataset("hai")
NULL
#'@title list available datasets
#'@aliases listDatasets
#'@details Prints the names of the available datasets
#'@return Doesn't return anything, just prints to console.
#'@name ImmuneSpaceConnection_listDatasets
#'@examples
#'labkey.url.base="https://www.immunespace.org"
#'labkey.url.path="/Studies/SDY269"
#'labkey.user.email='gfinak at fhcrc.org'
#'sdy269<-CreateConnection("SDY269")
#'sdy269$listDatasets()
NULL
#'@title list available gene expression analysis
#'@aliases listGEAnalysis
#'@details Prints the table of differential expression analysis
#'@return A \code{data.frame}. The list of gene expression analysis.
#'@name ImmuneSpaceConnection_listGEAnalysis
#'@examples
#'labkey.url.base="https://www.immunespace.org"
#'labkey.url.path="/Studies/SDY269"
#'labkey.user.email='gfinak at fhcrc.org'
#'sdy269<-CreateConnection("SDY269")
#'sdy269$listGEAnalysis()
NULL
|
9877c4ee0f911e1f708d069ab0dc8db6e9c5515e
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/rhnerm/examples/cmseRHNERM.Rd.R
|
28ee8deb135c6ada587bd81229cc12899b5455b7
|
[] |
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
| 688
|
r
|
cmseRHNERM.Rd.R
|
library(rhnerm)
### Name: cmseRHNERM
### Title: Conditional mean squared error estimation of the empirical Bayes
### estimators under random heteroscedastic nested error regression
### models
### Aliases: cmseRHNERM
### ** Examples
#generate data
set.seed(1234)
beta=c(1,1); la=1; tau=c(8,4)
m=20; ni=rep(3,m); N=sum(ni)
X=cbind(rep(1,N),rnorm(N))
mu=beta[1]+beta[2]*X[,2]
sig=1/rgamma(m,tau[1]/2,tau[2]/2); v=rnorm(m,0,sqrt(la*sig))
y=c()
cum=c(0,cumsum(ni))
for(i in 1:m){
term=(cum[i]+1):cum[i+1]
y[term]=mu[term]+v[i]+rnorm(ni[i],0,sqrt(sig[i]))
}
#fit the random heteroscedastic nested error regression
C=cbind(rep(1,m),rnorm(m))
cmse=cmseRHNERM(y,X,ni,C,B=10)
cmse
|
c37675c91c6ce937c750378a3b3c81a02eaed72b
|
87a5d63aa52e25dfb121b4283c6dc935a6fa4c87
|
/R_Cointegration Case/S_ECM_wADF.R
|
017fbf722c1e08d96e8e7fb825bf8318d59f5592
|
[] |
no_license
|
AnthonyGachuru/cqf-1
|
fca9834a95bf24d1753fedb4813390d70c630a4c
|
8d66227755dff201b671a25cf45408ce7527bcfb
|
refs/heads/master
| 2021-05-21T22:07:47.254035
| 2019-10-29T06:59:41
| 2019-10-29T06:59:41
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,320
|
r
|
S_ECM_wADF.R
|
######################################################################
# 2015. Richard Diamond. Quieries to r.diamond@cqf.com #
# Models are specified and validated but any use is at your own risk #
######################################################################
# ECM IMPLEMENTATION (two variables, Engle-Granger method)
# ADF TEST ON SERIES
# "drift" -- refers Delta Y= constant, "trend" refers to Delta Y = beta*t -- increases critical values but overfits time dependence
adf.test = ur.df(curve2.this$X10, type = "drift")
print(summary(adf.test))
adf.test = ur.df(curve2.this$X25, type = "drift")
print(summary(adf.test))
# NAIVE COINTEGRATING EQUATION
coint.reg = lm(curve2.this$X10 ~ curve2.this$X25)
print(summary(coint.reg))
# CADF TEST ON RESIDUAL
cadf.test = ur.df(residuals(coint.reg), type = "none") # CADF because ADF test applies to cointegrated residual
print(summary(cadf.test))
# ECM PARAMETERS ESTIMATION (one-way)
tenorY.diff = diff(curve2.this$X10) #tenorY.diff = tenorY.diff - mean(tenorY.diff) # however, mean is very small
tenorX.diff = diff(curve2.this$X25)
eq_corr.lag = lag(residuals(coint.reg), k = -1)
ecm.reg = lm(tenorY.diff ~ tenorX.diff + eq_corr.lag + 0)
print(summary(ecm.reg))
#ECM with Delta Y_t-1 # // but that variable comes as not significant by t statistic
#ecm.reg = lm(tenorY.diff[ time(tenorY.diff) != as.Date("2013-05-31")] ~ lag(tenorY.diff, k = -1) + tenorX.diff[ time(tenorX.diff) != as.Date("2013-05-31")] + eq_corr.lag[ time(eq_corr.lag) != as.Date("2013-05-31")] + 0)
#print(summary(ecm.reg))
# to check the relationship 'the other way', r_25Y on r_10Y -- we recompute the residual eq-correction term
# that will save time on deciding which way is 'better' and which variable is leading (we do two things in one)
cointO.reg = lm(curve2.this$X25 ~ curve2.this$X10)
eq_corrO.lag = lag(residuals(cointO.reg), k = -1) #omit the step of testing residual with CADF but test result given on Case - Extra Slides
ecmO.reg = lm(tenorX.diff ~ tenorY.diff + eq_corrO.lag + 0)
print(summary(ecmO.reg))
# LINEAR REGRESSION ON DIFFERENCES (for comparison) // linear regression in differences gives the minimum variance hedge
simple.reg = lm(diff(curve2.this$X10) ~ diff(curve2.this$X25) + 0) # + 0 means no cash holdings
print(summary(simple.reg))
|
3667fa6be414754330b9a087e7808b68d0c40b71
|
00c98a4502e7a0670813325a408e16d1c7da4139
|
/man/dorem_no_link_func.Rd
|
708f948a4482f29d7caf6719a71070f05627e5e7
|
[
"MIT"
] |
permissive
|
mladenjovanovic/dorem
|
7f192c94080d511bef5a2244205a7a4e06198de2
|
573377ac7740b8e5190bf92d5f023bf06e8cc277
|
refs/heads/master
| 2023-04-11T07:09:31.504879
| 2022-07-18T19:03:29
| 2022-07-18T19:03:29
| 256,017,605
| 7
| 3
|
MIT
| 2020-08-30T17:04:29
| 2020-04-15T19:32:52
|
R
|
UTF-8
|
R
| false
| true
| 365
|
rd
|
dorem_no_link_func.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dorem-control.R
\name{dorem_no_link_func}
\alias{dorem_no_link_func}
\title{Default link function}
\usage{
dorem_no_link_func(x)
}
\arguments{
\item{x}{Numeric vector}
}
\value{
Numeric vector
}
\description{
By default there is no link function.
}
\examples{
dorem_no_link_func(1:10)
}
|
1969ba4540326c5579608bd503a131f3c1b9d227
|
4d77b035d6cbb2b2ba6111b63298f87f3279d778
|
/run_analysis.R
|
55e7cad356754fa1532c36d6d1e56b698b3ae0cb
|
[] |
no_license
|
xnoamix/GettingAndCleaningDataCourseProject
|
799af7e73dd55e094b618405082b84ab623eadec
|
575421e36d66d447ca9e202dedb746d924943bc2
|
refs/heads/master
| 2021-01-17T09:31:54.078234
| 2014-11-23T13:53:32
| 2014-11-23T13:53:32
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,019
|
r
|
run_analysis.R
|
library(dplyr)
unzip("getdata_projectfiles_UCI HAR Dataset.zip")
## Loading all the different files belong to the training and test sets
features <- read.table("UCI HAR Dataset/features.txt")
test_data <- read.table("UCI HAR Dataset/test/X_test.txt")
subject_test <- read.table("UCI HAR Dataset/test/subject_test.txt")
activity_test <- read.table("UCI HAR Dataset/test/y_test.txt")
subject_train <- read.table("UCI HAR Dataset/train/subject_train.txt")
activity_train <- read.table("UCI HAR Dataset/train/y_train.txt")
train_data <- read.table("UCI HAR Dataset/train/X_train.txt")
activity_labels <- read.table("UCI HAR Dataset/activity_labels.txt")
## First, merging separetly activity labels, subjects and datasets from training and test,
## then, extracting from the datasets only those columns which calculate mean or std.
## Only then merging all together to "all_merged".
activity_merged <- rbind(activity_train, activity_test)
subject_merged <- rbind(subject_train, subject_test)
merged <- rbind(train_data, test_data)
cname <- features[, 2]
rel_col <- grepl("mean\\(\\)|std\\(\\)", cname)
all_merged <- cbind(subject_merged, activity_merged, merged[ , rel_col])
##Setting the columns names to be clean and descriptive, replacing activity indices with labels
col_names <- c("Subject", "Activity", as.character(cname[rel_col]))
colnames(all_merged) <- col_names
all_merged$Activity <- cut(all_merged$Activity, breaks=6, labels=activity_labels[, 2])
clean_col <- gsub("_|\\(|\\)|,|-", "", colnames(all_merged))
clean_col <- gsub("^t", "Time", clean_col)
clean_col <- gsub("^f", "Freq", clean_col)
clean_col <- gsub("BodyBody", "Body", clean_col)
clean_col <- gsub("std", "Std", clean_col)
clean_col <- gsub("mean", "Mean", clean_col)
clean_col <- make.names(clean_col, unique=TRUE)
colnames(all_merged) <- clean_col
## Creating another data frame that holds the average of each variable per subject and activity
mean_var <- group_by(all_merged, Subject, Activity) %>% summarise_each(funs(mean))
|
b87101f2a7e258c35bfe56ef04b2034e77644255
|
d3a4319a66f8b86051c127c28d32619a256de156
|
/R/onload.R
|
ff5872961b2830ccd3fa0b52bf2cbdcfe9a19c60
|
[
"MIT"
] |
permissive
|
SCAR/sohungry
|
11fbfbc14bb740754047ae5146f6c137f2ba913a
|
fd33d2c05fb7f2a8d51bb341ee30497f6098bab2
|
refs/heads/master
| 2023-04-22T07:14:44.234956
| 2023-04-03T23:21:38
| 2023-04-03T23:21:38
| 78,815,311
| 4
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,388
|
r
|
onload.R
|
.onLoad <- function(libname, pkgname) {
## populate the options slot
this_options <- list(
doi_file = "scar_diet_energetics_doi.txt",
sources_table = "ecology.dbo.scar_references",
sources_file = "scar_sources.csv",
energetics_table = "ecology.dbo.scar_energetics",
energetics_file = "scar_energetics.csv",
isotopes_table = "ecology.dbo.scar_isotopes",
##isotopes_file = "scar_isotopes.csv", ## deprecated as of v0.9.0
isotopes_mv_file = "scar_isotopes_mv.csv",
diet_table = "ecology.dbo.scar_diet",
diet_file = "scar_diet.csv",
dna_diet_table = "ecology.dbo.scar_dna_diet",
dna_diet_file = "scar_dna_diet.csv",
lipids_table = "ecology.dbo.scar_lipids",
lipids_file = "scar_lipids.csv",
zenodo_id = 5072527, ## this is the concept ID, which should always point to the most recent version
zip_file = "SCAR_Diet_Energetics.zip",
issue_text = "If the problem persists, please lodge an issue at https://github.com/SCAR/sohungry/issues",
session_cache_dir = file.path(tempdir(), "sohungry-cache"), ## cache directory to use for cache_directory = "session"
persistent_cache_dir = rappdirs::user_cache_dir("sohungry", "SCAR") ## and for cache_directory = "persistent"
)
options(list(sohungry = this_options))
invisible()
}
|
a73a202d42e7ea843f350e4972bfa9d3702eef5c
|
72721f21b5e6bd7802f88fefda30dc77cd602a64
|
/FDR_control/simulated_dames.R
|
bf2fa1936edf02fb9c13df32f4abdab4d1b402d9
|
[] |
no_license
|
markrobinsonuzh/allele_specificity_paper
|
9effc5cad41159c7473743449968c7313a2369e6
|
4bb4cc7c7270f1a7a063e8a5482a0f9b84b89bc8
|
refs/heads/master
| 2021-03-19T13:12:32.965571
| 2020-02-14T12:24:46
| 2020-02-14T12:24:46
| 49,428,747
| 2
| 1
| null | 2020-02-14T12:24:47
| 2016-01-11T13:40:19
|
R
|
UTF-8
|
R
| false
| false
| 13,133
|
r
|
simulated_dames.R
|
#!/usr/bin/env Rscript
# chmod +x
# run as [R < scriptName.R --no-save]
#########################################################################################
# Benchmark p-val assigment strategies with simulations
#
# TBS-seq data CRCs Vs Norm
#
#
# Stephany Orjuela, May 2019
#########################################################################################
library(SummarizedExperiment)
library(ggplot2)
library(iCOBRA)
library(tidyr)
#### Set sim ####
load("data/derASM_fullCancer.RData")
derASM <- GenomeInfoDb::sortSeqlevels(derASM) #only necessary for old calc_derivedasm()
derASM <- sort(derASM)
#use only the sites completely covered by all samples
filt <- rowSums(!is.na(assay(derASM, "der.ASM"))) >= 12
derASM <- derASM[filt,] #9073
x <- assay(derASM,"der.ASM")
##Get only norm samples
prop.clust <- x[,7:12]
original <- prop.clust
means <- rowMeans(prop.clust)
diffs <- apply(prop.clust, 1, function(w){mean(w[1:3]) - mean(w[4:6])})
var <- rowVars(prop.clust)
dd <- as.data.frame(cbind(var, means, diffs))
head(dd)
#MD plot
MD1 <- ggplot(dd, aes(means, diffs)) + geom_point(alpha = 0.2) +
theme_bw()
#MV plot
MV1 <- ggplot(dd, aes(means, var)) + geom_point(alpha = 0.2) + theme_bw()
#### play with clust length given maxGap ####
#20
clust <- bumphunter::clusterMaker(as.character(seqnames(derASM)), start(derASM), maxGap = 20)
max20 <- data.frame(clusL = rle(clust)$length, maxGap = 20)
#100
clust <- bumphunter::clusterMaker(as.character(seqnames(derASM)), start(derASM), maxGap = 100)
maxcien <- data.frame(clusL = rle(clust)$length, maxGap = 100)
#1000
clust <- bumphunter::clusterMaker(as.character(seqnames(derASM)), start(derASM), maxGap = 1000)
maxmil <- data.frame(clusL = rle(clust)$length, maxGap = 1000)
clustab <- rbind(max20,maxcien,maxmil)
ggplot(clustab, aes(clusL)) + geom_histogram() +
theme_bw() +
labs(x= "Number of CpGs") +
facet_grid(~maxGap)
ggsave("curvesNscatters/sim_cluster_sizes.png")
#### inverse sampling ####
#inverse sampling with truncated beta
set.seed(20) # params for very obvious regions
alpha <- 1
beta <- 2.5
minb <- 0.35 # 0.15 too small for lmfit to consider it a difference
maxb <- 0.75
pDiff <- 0.2 #this should affect the k choice
cluster.ids <- unique(clust) #3229, 1038
diffClusts <- 1:floor(pDiff*length(cluster.ids)) #645
#plot runif and beta
fullb <- qbeta(runif(length(diffClusts), minb, maxb), alpha, beta)
p1 <- ggplot() + geom_histogram(aes(fullb), bins = 6) + theme_bw() + labs(x = "Effect sizes")
un <- runif(length(diffClusts), minb, maxb)
p2 <- ggplot() + geom_histogram(aes(un), bins = 7) + theme_bw() + labs(x = "Unif(0.35,0.75)")
ran <- seq(0, 1, length = 100)
db <- dbeta(ran, alpha,beta)
d3 <- data.frame(p = ran, density = db)
p3 <- ggplot(d3, aes(p,density)) + geom_line() + theme_bw()
cowplot::plot_grid(p1,p2,p3, nrow = 3, ncol = 1, labels = c("A","B","C"))
ggsave("curvesNscatters/beta_and_unif_hists.png", width = 6,
height = 10)
#get real coordinates to start from
chr <- as.character(seqnames(derASM))
starts <- start(derASM)
ends <- end(derASM)
realregs <- data.frame(chr=sapply(cluster.ids,function(Index) chr[clust == Index][1]),
start=sapply(cluster.ids,function(Index) min(starts[clust == Index])),
end=sapply(cluster.ids, function(Index) max(ends[clust == Index])),
clusL=sapply(cluster.ids, function(Index) length(clust[clust == Index])))
#create 50 more simulations to run the methods
draw_sims <- function(numsims = 50, x, alpha, beta, minb, maxb, diffClusts, clust, #same params
cluster.ids, chr, starts, ends, realregs, original,
trend, methlmfit = "ls"){ #for find_dames, ggfile
all_perf <- list()
all_points <- list()
for(j in 1:numsims){
print(j)
prop.clust <- x[,7:12]
d <- qbeta(runif(length(diffClusts), minb, maxb), alpha, beta)
#hist(d)
### Simulation ####
for(i in diffClusts){
#get CpGs per cluster that will have spike-in
cpgs <- which(clust == cluster.ids[i])
#choose number of CpGs diff per regions, and from what position
if(length(cpgs) > 1){
numdiff <- sample(1:length(cpgs), 1)
maxpos <- length(cpgs) - numdiff + 1
posdiff <- sample(1:maxpos,1)
cpgs <- cpgs[1:posdiff]
#reset region start end ends
realregs$start[i] <- min(starts[cpgs])
realregs$end[i] <- max(ends[cpgs])
realregs$clusL[i] <- length(cpgs)
}
#randomly choose which group is diff
ran <- sample(c(1,2),1)
if(ran == 1) {group <- 1:3} else {group <- 4:6}
#get cluster ASMsnp mean (if more than one sample)
if(length(cpgs) > 1){
DMRmean <- mean(rowMeans(prop.clust[cpgs,]))
} else{
DMRmean <- mean(prop.clust[cpgs,])
}
#sign is deterministic:
#if the DMR mean (across samples and loci) is below
#effect size 0.5, sign is positive
if(DMRmean < 0.5) {sign <- 1} else {sign <- -1}
#if any of the values goes outside of [0,1], keep the original prop (second)
prop.clust[cpgs,group] <- original[cpgs,group] + (d[i] * sign)
if(any(prop.clust[cpgs,group] < 0 | prop.clust[cpgs,group] > 1)){
w <- which(prop.clust[cpgs,group] < 0 | prop.clust[cpgs,group] > 1)
prop.clust[cpgs,group][w] <- original[cpgs,group][w]
}
}
#make real GRanges
realregsGR <- GRanges(realregs$chr, IRanges(realregs$start, realregs$end),
clusL = realregs$clusL,
label = c(rep(1,length(diffClusts)),
rep(0,(length(cluster.ids)-length(diffClusts)))))
filt <- realregsGR$clusL != 1
realregsGR <- realregsGR[filt] #773
#table(realregsGR$label)
#head(prop.clust)
#head(original)
#re-do plots with added effects
# means <- rowMeans(prop.clust)
# var <- rowVars(prop.clust)
# diffs <- apply(prop.clust, 1, function(w){mean(w[1:3]) - mean(w[4:6])})
# dd <- as.data.frame(cbind(diffs, means,var))
# ggplot(dd, aes(means, diffs)) + geom_point(alpha = 0.2) + theme_bw()
# ggplot(dd, aes(means, var)) + geom_point(alpha = 0.2) + theme_bw()
#build a sumExp with new data
fakeDerAsm <- derASM[,7:12]
assay(fakeDerAsm, "der.ASM") <- prop.clust
grp <- factor(c(rep("CRC",3),rep("NORM",3)), levels = c("NORM", "CRC"))
mod <- model.matrix(~grp)
#### Apply all methods ####
#simes
regs <- find_dames(fakeDerAsm, mod, maxGap = 100, trend = trend, method = methlmfit)
regsGR <- GRanges(regs$chr, IRanges(regs$start, regs$end),
clusterL = regs$clusterL, pval = regs$pvalSimes, FDR = regs$FDR)
#empirical
regs2 <- find_dames(fakeDerAsm, mod, maxGap = 100, pvalAssign = "empirical", Q = 0.2,
trend = trend, method = methlmfit)
regs1GR <- GRanges(regs2$chr, IRanges(regs2$start, regs2$end), segmentL = regs2$segmentL,
clusterL = regs2$clusterL, pval = regs2$pvalEmp, FDR = regs2$FDR)
regs2 <- find_dames(fakeDerAsm, mod, maxGap = 100, pvalAssign = "empirical", Q = 0.5,
trend = trend, method = methlmfit)
regs2GR <- GRanges(regs2$chr, IRanges(regs2$start, regs2$end), segmentL = regs2$segmentL,
clusterL = regs2$clusterL, pval = regs2$pvalEmp, FDR = regs2$FDR)
regs2 <- find_dames(fakeDerAsm, mod, maxGap = 100, pvalAssign = "empirical", Q = 0.8,
trend = trend, method = methlmfit)
regs3GR <- GRanges(regs2$chr, IRanges(regs2$start, regs2$end), segmentL = regs2$segmentL,
clusterL = regs2$clusterL, pval = regs2$pvalEmp, FDR = regs2$FDR)
#### build tables with pval methods ####
pvalmat <- data.frame(matrix(1, nrow = length(realregsGR), ncol = 4))
fdrmat <- data.frame(matrix(1, nrow = length(realregsGR), ncol = 4))
colnames(pvalmat) <- colnames(fdrmat) <- c("simes",
"perms_02",
"perms_05",
"perms_08")
#simes
over <- findOverlaps(realregsGR, regsGR, type = "within")
pvalmat$simes[queryHits(over)] <- mcols(regsGR)$pval[subjectHits(over)]
fdrmat$simes[queryHits(over)] <- mcols(regsGR)$FDR[subjectHits(over)]
#perms.0.2
over <- findOverlaps(realregsGR, regs1GR, type = "within")
pvalmat$perms_02[queryHits(over)] <- mcols(regs1GR)$pval[subjectHits(over)]
fdrmat$perms_02[queryHits(over)] <- mcols(regs1GR)$FDR[subjectHits(over)]
#perms.0.5
over <- findOverlaps(realregsGR, regs2GR, type = "within")
pvalmat$perms_05[queryHits(over)] <- mcols(regs2GR)$pval[subjectHits(over)]
fdrmat$perms_05[queryHits(over)] <- mcols(regs2GR)$FDR[subjectHits(over)]
#perm.0.8
over <- findOverlaps(realregsGR, regs3GR, type = "within")
pvalmat$perms_08[queryHits(over)] <- mcols(regs3GR)$pval[subjectHits(over)]
fdrmat$perms_08[queryHits(over)] <- mcols(regs3GR)$FDR[subjectHits(over)]
#### plot powerFDR ####
#generate truth + facet table
truth <- as.data.frame(mcols(realregsGR))
#change clusL to num.CpGs
#run iCOBRa
cobradat <- COBRAData(pval = pvalmat,
padj = fdrmat,
truth = truth)
#single plot
cobraperf <- calculate_performance(cobradat, binary_truth = "label",
cont_truth = "label",
aspects = c("fdrtpr","fdrtprcurve"),
thrs = c(0.01, 0.05, 0.1))
all_perf[[j]] <- cobraperf@fdrtprcurve
all_points[[j]] <- cobraperf@fdrtpr
}
#### set up to plot all sims ####
#lines
tpr <- lapply(all_perf, function(x){x$TPR})
allperftab <- data.frame(sim = rep(1:numsims, lengths(tpr)),
FDR = unlist(lapply(all_perf, function(x){x$FDR})),
TPR = unlist(tpr),
method = unlist(lapply(all_perf, function(x){x$method})))
allperftab <- unite(allperftab, unique_id, c(sim, method), sep="_", remove = FALSE)
#points
tpr <- lapply(all_points, function(x){x$TPR})
allpointtab <- data.frame(sim = rep(1:numsims, lengths(tpr)),
FDR = unlist(lapply(all_points, function(x){x$FDR})),
TPR = unlist(tpr),
method = unlist(lapply(all_points, function(x){x$method})),
thr = unlist(lapply(all_points, function(x){x$thr})),
satis = unlist(lapply(all_points, function(x){x$satis})))
summpoints <- allpointtab %>%
dplyr::group_by(method, thr) %>%
dplyr::summarise(meanTPR=mean(TPR), meanFDR = mean(FDR)) %>%
as.data.frame()
summpoints$thr <- as.numeric(gsub("thr","",summpoints$thr))
summpoints$satis <- ifelse(summpoints$meanFDR <= summpoints$thr,16,21)
myColor <- RColorBrewer::brewer.pal(8, "Set1")
gplot <- ggplot(allperftab) +
geom_line(aes(FDR, TPR, color=method, group=unique_id), alpha = 0.11) +
scale_x_continuous(trans='sqrt', breaks = c(0.01,0.05,0.10,0.5)) +
scale_color_manual(values = myColor) +
labs(color = "Method") +
geom_vline(xintercept = c(0.01,0.05,0.1), linetype = 2) +
geom_line(data = summpoints, aes(x = meanFDR, y = meanTPR,color=method), size = 1) +
geom_point(data = summpoints, aes(x = meanFDR,y = meanTPR,color=method, shape = satis),
size = 5, fill = "white") +
scale_shape_identity() +
theme_bw()
return(gplot)
}
#figure 3
pdiff02 <- draw_sims(numsims = 50, x, alpha, beta, minb, maxb, diffClusts, clust,
cluster.ids, chr, starts, ends, realregs, original,
FALSE)
ggplot2::ggsave("curvesNscatters/powerFDR_pdiff02.png", pdiff02, width = 6,
height = 5)
#supp fig 1
#figure 3
pdiff05 <- draw_sims(numsims = 50, x, alpha, beta, minb, maxb, diffClusts, clust,
cluster.ids, chr, starts, ends, realregs, original,
FALSE)
#pdiff 0.2, len 20 (change above clust)
len20 <- draw_sims(numsims = 50, x, alpha, beta, minb, maxb, diffClusts, clust,
cluster.ids, chr, starts, ends, realregs, original,
TRUE)
#pdiff 0.2, len 1000 (change above clust)
len1000 <- draw_sims(numsims = 50, x, alpha, beta, minb, maxb, diffClusts, clust,
cluster.ids, chr, starts, ends, realregs, original,
FALSE)
#pdiff 0.2, len 100, trend true
trendtrue <- draw_sims(numsims = 50, x, alpha, beta, minb, maxb, diffClusts, clust,
cluster.ids, chr, starts, ends, realregs, original,
TRUE)
len20 <- len20 + theme(legend.position = "none")
len1000 <- len1000 + theme(legend.position = "none")
trendtrue <- trendtrue + theme(legend.position = "none")
pdiff05 <- pdiff05 + theme(legend.position = "none")
legend <- cowplot::get_legend(trendtrue)
m4 <- cowplot::plot_grid(len20, len1000, legend, trendtrue, pdiff05, ncol=3, nrow = 2,
labels = c("A", "B", "","C", "D"),
rel_widths = c(1, 1, 0.3))
ggplot2::ggsave("curvesNscatters/powerFDR_otherparams.png", m4, width = 8,
height = 7)
|
b62341d9eaeace251dfe76fef1142c5f51055895
|
b2d46260f641db68780b0899f41661cb52413b43
|
/survival_gene_list_tcga.R
|
3b6b9202489ae9a4d462ea63e2949ec4b91a3d3d
|
[] |
no_license
|
bio-liucheng/brca-singlecell
|
53dd13a82f8fba4411edcc247d81227a9883cdc4
|
98d63348e6daad001f8d0b9f3aea7ae5d483834e
|
refs/heads/main
| 2023-04-12T06:21:37.666495
| 2021-12-14T11:24:55
| 2021-12-14T11:24:55
| 423,029,266
| 1
| 0
| null | null | null | null |
WINDOWS-1252
|
R
| false
| false
| 6,196
|
r
|
survival_gene_list_tcga.R
|
library(survival)
library(survminer)
library(survMisc)
gene_list = c("GPR157")
setwd("G:/scRNA-seq/LC/TCGA/BRCA")
options(stringsAsFactors = F)
clin <- read.delim("BRCA_clinicalMatrix")
expr <- read.delim("HiSeqV2.gz")
surv <- read.delim("BRCA_survival.txt.gz")
rownames(expr) <- expr[,1]
expr <- expr[,-1]
colnames(expr) <- gsub(".", '-', colnames(expr), fixed = T)
rownames(clin) <- clin$sampleID
#¼ÆËãtumor vs normal ²îÒì
clin <- clin[colnames(expr),]
tumor_flag <- clin$sample_type == "Primary Tumor"
normol_flag <- clin$sample_type == "Solid Tissue Normal"
ER_positive <- clin$ER_Status_nature2012 == "Positive"
#caculate survival HR
clin <- clin[tumor_flag,]
expr <- expr[,tumor_flag]
rownames(surv) <- surv$sample
surv <- surv[,-1]
surv <- surv[rownames(clin),]
clin <- cbind(clin, surv)
#filter clin
clin2 <- clin[!grepl("Her2|Lum", clin$PAM50Call_RNAseq),]
expr2 <- expr[,rownames(clin2)]
data <- as.matrix(expr)
surv_genelist <- function(data, clin_s, gene_list){
inter_gene <- intersect(rownames(data), gene_list[1:20])
expr_gene <- data[inter_gene,]
expr_gene <- apply(expr_gene, 1, scale)
rownames(expr_gene) <- colnames(data)
expr_gene <- t(expr_gene)
expr_gene <- apply(expr_gene, 2, sum)
flag <- ifelse(expr_gene >median(expr_gene, na.rm = T), "high", "low")
flag2 <- expr_gene < quantile(expr_gene, probs = 0.4)
flag3 <- expr_gene > quantile(expr_gene, probs = 0.6)
fit <- coxph(Surv(OS.time, OS) ~ flag ,subset = flag2 | flag3, data = clin_s)
sum_fit <- summary(fit)
fit2 <- survfit(Surv(OS.time, OS) ~ flag, subset = flag2 | flag3, data = clin_s)
ggsurvplot(fit2, data = clin_s, pval = TRUE, )
sum_fit
}
gene_list
#calculate survival with gene_list average expression gene
surv_genelist <- function(data, clin, clin_type, sub_type, gene_list){
if(is.null(clin_type)){
sub_expr <- data
sub_clin <- clin
}else{
flag_sub <- clin[,clin_type] %in% sub_type
sub_clin <- clin[flag_sub,]
sub_expr <- data[,flag_sub]
}
if(length(gene_list) >1){
inter_gene <- intersect(rownames(sub_expr), gene_list)
expr_gene <- sub_expr[inter_gene,]
expr_gene <- apply(expr_gene, 2, mean)
}else{
expr_gene <- sub_expr[gene_list,]
expr_gene <- as.numeric(expr_gene)
}
flag <- ifelse(expr_gene >median(expr_gene, na.rm = T), "high", "low")
flag <- factor(flag, levels = c("high", "low"))
flag_low <- expr_gene < quantile(expr_gene, probs = 0.45, na.rm = T)
flag_high <- expr_gene > quantile(expr_gene, probs = 0.55, na.rm = T)
sub_clin$flag <- flag
sub_clin$flag_low <- flag_low
sub_clin$flag_high <- flag_high
# fit <- coxph(Surv(time = OS.time, event = OS) ~ flag, subset = flag_low |flag_high, data = sub_clin)
fit2 <- survfit(Surv(time = OS.time, event = OS) ~ flag, subset = flag_low |flag_high, data = sub_clin)
ggsurvplot(fit2, data = sub_clin, pval = TRUE)
# sum_fit <- summary(fit)
}
gene_list_a <- gene_list$gene
gene_list_b <- c("LAMP3", "C1DC", "CLEC9A")
gene_a <- apply(data[gene_list_a,], 1, scale)
gene_a <- t(gene_a)
gene_a <- apply(gene_a, 2, mean)
gene_a <- apply(data[intersect(gene_list_a, rownames(data)),], 2, mean)
gene_b <- apply(data[gene_list_b,], 2, mean)
gene_a <- as.numeric(data["LAMP3",])
gene_b <- as.numeric(data["LAMP3",])
relative_gene <- scale(gene_a) / gene_b
relative_gene <- gene_b / gene_a
relative_gene <- gene_a
flag <- ifelse(relative_gene >median(relative_gene, na.rm = T), "high", "low")
flag <- factor(flag, levels = c("high", "low"))
flag_low <- relative_gene < quantile(relative_gene, probs = 0.45, na.rm = T)
flag_high <- relative_gene > quantile(relative_gene, probs = 0.55, na.rm = T)
clin$flag <- flag
clin$flag_low <- flag_low
clin$flag_high <- flag_high
fit2 <- survfit(Surv(time = OS.time, event = OS) ~ flag, subset = flag_low |flag_high, data = clin)
ggsurvplot(fit2, data = clin, pval = TRUE)
if(T){
#get a gene list
gene_cluster <- "CAF_C3_PLA2G2A"
gene_list <- cluster_significant_markers[cluster_significant_markers$cluster == gene_cluster,]$gene
if(!is.null(intersect(gene_list, rownames(expr))))
fit <- surv_genelist(expr, clin, "ER_Status_nature2012", "Positive", gene_list)
}
type <- unique(clin$ER_Status_nature2012)
type <- type[-c(1,4)]
library(dplyr)
#calculate survival
for(j in 1:length(type)){
type_s <- type[j]
cluster <- unique(final_marker_s$cluster)
for(i in 1:length(cluster)){
gene_list <- final_marker_s$gene[final_marker_s$cluster == cluster[i]]
f <- surv_genelist(expr, clin, NULL, type_s, gene_list)
f_table <-data.frame(coef = f[["conf.int"]][1],
low_95 = f[["conf.int"]][3],
high_95 = f[["conf.int"]][4],
pvalue = f[["coefficients"]][5])
if(i == 1)
f_table_final <- f_table
else
f_table_final <- rbind(f_table_final, f_table)
}
f_table_final$cluster <- cluster
f_table_final$patient_type <- type_s
if(j ==1)
f_table_final_final <- f_table_final
else
f_table_final_final <- rbind(f_table_final_final, f_table_final)
}
#calculate survival with two gene (high low) expression gene
surv_TwoGene <- function(data, clin_s, two_gene){
inter_gene <- intersect(rownames(data), two_gene)
expr_gene <- data[inter_gene,]
expr_gene <- apply(expr_gene, 1, scale)
rownames(expr_gene) <- colnames(data)
gene1_hgih <- ifelse(expr_gene[,1] > median(expr_gene[,1]),paste0(two_gene[1], "_high"),paste0(two_gene[1], "low"))
gene2_high <- ifelse(expr_gene[,2] > median(expr_gene[,2]),paste0(two_gene[2], "_high"),paste0(two_gene[2], "low"))
s <- paste(gene1_hgih, gene2_high, sep = '_')
s <- factor(s, levels = unique(s)[c(2,1,3,4)])
clin_s$s <- s
fit <- coxph(Surv(OS.time, OS) ~ s, data = clin_s)
sum_fit <- summary(fit)
fit2 <- survfit(Surv(OS.time, OS) ~ s, data = clin_s)
ggsurvplot(fit2, data = clin_s, pval = TRUE)
}
surv_genelist(expr, clin, gene_list)
unique(clin$PAM50Call_RNAseq)
|
cb77c1de7f55a5cccccd59e541872cf5989d100d
|
2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0
|
/fuzzedpackages/snipEM/R/sclust.R
|
5e322f8dcfcd1d390596939589e055b1e947216d
|
[] |
no_license
|
akhikolla/testpackages
|
62ccaeed866e2194652b65e7360987b3b20df7e7
|
01259c3543febc89955ea5b79f3a08d3afe57e95
|
refs/heads/master
| 2023-02-18T03:50:28.288006
| 2021-01-18T13:23:32
| 2021-01-18T13:23:32
| 329,981,898
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,906
|
r
|
sclust.R
|
.eigenConst <- function(Sev, p, lambda=12){
moptv <- 999999
if( Sev[p] > 0 & Sev[1]/Sev[p] < lambda){
moptv <- sum(log(Sev) + 1)
return(Sev)
} else{
for(i in 1:p){
cm <- Sev[i]
if( cm > 0){
m <- cm/lambda
Sevm <- pmin( pmax( Sev, m ), cm)
moptv_tmp <- sum(log(Sevm) + Sev/Sevm)
if( moptv_tmp < moptv ){
moptv <- moptv_tmp
mopt <- m
}
}
m <- Sev[p-i+1]
if( m > 0){
cm <- m*lambda
Sevm <- pmin( pmax( Sev, m ), cm)
moptv_tmp <- sum(log(Sevm) + Sev/Sevm)
if( moptv_tmp < moptv ){
moptv <- moptv_tmp
mopt <- m
}
}
}
Sevm <- pmin( pmax( Sev, mopt ), mopt*lambda)
return( Sevm )
}
}
sclust<- function(X,k,V,R,restr.fact=12,tol=1e-4,maxiters=100,maxiters.S=1000, print.it=FALSE) {
if( missing(X) ) stop("'X' missing")
if( missing(k) ) stop("'k' missing")
if( missing(V) ) stop("'V' missing")
if( missing(R) ) stop("'R' missing")
if(is.data.frame(X) | is.matrix(X))
X <- data.matrix(X)
else stop("Data matrix must be of class matrix or data.frame")
n <- nrow(X)
p <- ncol(X)
if(is.data.frame(V) | is.matrix(V))
V <- data.matrix(V)
if( any(dim(V) != dim(X)) ) stop("'X' and 'V' have non-conforming size")
epsilon=sum(V==0)/(n*p)
if( length(unique(R)) != k) stop("Number of cluster labels must be 'k'")
## init ##
m=matrix(NA,k,p)
Sigma=array(0,c(k,p,p))
D=matrix(NA,n,p)
Dd=Ddtmp=matrix(NA,n,k)
autovalues=matrix(NA,p,k)
U=Sigma
CSmat=array(NA,c(k,n,p))
det=rep(NA,k)
## init values ##
pi=prop.table(table(R[R!=0]))
Xt=X
Xt[V==0]=NA
for(j in 1:k) {
m[j,]=apply(Xt[R==j,],2,mean,na.rm=T)
Sigma[j,,]=var(Xt[R==j,],na.rm=T)
s=eigen(var(Xt[R==j,],na.rm=T))
U[j,,]=s$vectors
autovalues[,j]=s$values
}
autovalues <- matrix(.eigenConst(as.vector( autovalues), p, restr.fact),ncol=k)
for(j in 1:k) {
Sigma[j,,]=U[j,,]%*%diag(autovalues[,j])%*%t(U[j,,])
det[j]=prod(autovalues[,j])
}
for(j in 1:k) {
Dd[,j]=log(pi[j])+ldmvnorm(Xt,m[j,],Sigma[j,,])
}
Dd=exp(Dd-apply(Dd,1,sumlog))
lik=0
for(j in 1:k) {
lik=lik+sum(Dd[,j]*(log(pi[j])+ldmvnorm(Xt,m[j,],Sigma[j,,])))}
likold=lik-2*tol
ii=0
while(lik-likold>tol & ii < maxiters) {
ii=ii+1
## CES step
iter=0
flag=FALSE
while(iter < maxiters.S & flag==FALSE) {
iter=iter+1
s1=sample(which(V==1),1)
s2=sample(which(V==0),1)
Vc=V
Vc[s1]=0
Vc[s2]=1
Xt=X
Xt[Vc==0]=NA
likcand=0
for(j in 1:k) {
likcand=likcand+sum(Dd[,j]*(log(pi[j])+ldmvnorm(Xt,m[j,],Sigma[j,,])))}
if(likcand>lik) {
V=Vc
flag=TRUE
}
}
for(j in 1:k) {
Dd[,j]=log(pi[j])+ldmvnorm(Xt,m[j,],Sigma[j,,])
}
R=apply(Dd,1,which.max)
R[which(apply(V==0,1,all))]=0
Dd[which(apply(V==0,1,all)),]=0
## M step
pi=apply(Dd[which(apply(V!=0,1,any)),],2,sumlog)
pi=exp(pi-sumlog(pi))
Dd=exp(Dd-apply(Dd,1,sumlog))
Xt <- X
Xt[V==0] <- NA
for(j in 1:k) {
XtDd <- sweep(Xt, 1, Dd[,j], "*")
VDd <- sweep(V, 1, Dd[,j], "*")
m[j,] <- colSums(XtDd, na.rm=T)
m[j,] <- m[j,]/colSums(VDd, na.rm=T)
Stmp <- matrix(NA, p,p)
for(h in 1:(p-1)) {
for(l in (h+1):p) {
Stmp[h,l] <- sum(Dd[,j]*(Xt[,h]-m[j,h])*(Xt[,l]-m[j,l]),na.rm=T)
Stmp[h,l] <- Stmp[h,l]/sum(Dd[,j]*V[,h]*V[,l])
Stmp[l,h] <- Stmp[h,l]
}
}
for(h in 1:p) Stmp[h,h] <- sum(Dd[,j]*(Xt[,h]-m[j,h])^2,na.rm=T)/sum(Dd[,j]*V[,h])
s=eigen(Stmp)
U[j,,]=s$vectors
autovalues[,j]=s$values
}
autovalues <- matrix(.eigenConst(as.vector( autovalues), p, restr.fact),ncol=k)
for(j in 1:k) {
Sigma[j,,]=U[j,,]%*%diag(autovalues[,j])%*%t(U[j,,])
det[j]=prod(autovalues[,j])
}
likold=lik
lik=0
for(j in 1:k) {
lik=lik+sum(Dd[,j]*(log(pi[j])+ldmvnorm(Xt,m[j,],Sigma[j,,])))}
if( print.it )cat("iter", ii, "; current lik:", lik, "; change in lik:",lik-likold, "\n")
}
return(list(R=R,pi=pi,mu=m,S=Sigma,V=V,lik=lik,iter=ii))}
|
4d2b36c3695aec153a87f78e677ba58f09869d22
|
9969b02c26fa5388ac971b8212c761c6abf98efb
|
/inst/helperCode/find_gaps.r
|
39c21f8ea9cbe3a1b1f644ecd6cba6752e6f000c
|
[] |
no_license
|
tmcd82070/CAMP_RST
|
0cccd7d20c8c72d45fca31833c78cd2829afc169
|
eca3e894c19936edb26575aca125e795ab21d99f
|
refs/heads/master
| 2022-05-10T13:33:20.464702
| 2022-04-05T21:05:35
| 2022-04-05T21:05:35
| 10,950,738
| 0
| 0
| null | 2017-05-19T20:42:56
| 2013-06-25T21:24:52
|
R
|
UTF-8
|
R
| false
| false
| 7,672
|
r
|
find_gaps.r
|
find_gaps <- function( river, site, taxon, min.date, max.date ){
# river <- "American River"
# site <- 57000
# taxon <- 161980
# min.date <- '1980-01-01'
# max.date <- '2016-03-02'
if(river == ''){
db.file <- db.file1
} else if(river == 'Sacramento River'){
db.file <- db.file2
} else if(river == 'American River'){
db.file <- db.file3
} else if(river == ''){
db.file <- db.file4
} else if(river == 'Feather River'){
db.file <- db.file5
} else if(river == 'Stanislaus River'){
db.file <- db.file6
} else if(river == 'Old American Test'){
db.file <- db.file7
} else if(river == 'Mokelumne River'){
db.file <- db.file8
# } else if(river == "Knight's Landing"){
# db.file <- db.file9
} else if(river == "Knight's Landing"){
db.file <- db.fileA
}
db.file <<- db.file
cat(paste0(db.file,"\n"))
# Check that times are less than 1 year apart
strt.dt <- as.POSIXct( min.date, format="%Y-%m-%d" )
end.dt <- as.POSIXct( max.date, format="%Y-%m-%d" )
run.season <- data.frame( start=strt.dt, end=end.dt )
nvisits <- F.buildReportCriteria( site, min.date, max.date )
if( nvisits == 0 ){
warning("Your criteria returned no trapVisit table records.")
return()
}
db <- get( "db.file", env=.GlobalEnv )
ch <- odbcConnectAccess(db)
F.run.sqlFile( ch, "QrySamplePeriod.sql", R.TAXON=taxon ) # This SQL file develops the hours fished and TempSamplingSummary table
F.run.sqlFile( ch, "QryNotFishing.sql" ) # This SQL generates times when the traps were not fishing
F.run.sqlFile( ch, "QryUnmarkedByRunLifestage.sql", R.TAXON=taxon ) # This SQL generates unmarked fish by run and life stage
catch <- sqlFetch( ch, "TempSumUnmarkedByTrap_Run_Final" ) # Now, fetch the result
F.sql.error.check(catch)
close(ch)
if(nrow(catch) == 0){
warning("Your criteria returned no catch records. Check to make sure valid Fishing occurred within your date range.")
stop
}
catch$river <- river
catch$site <- site
catch
}
ame57000 <- find_gaps("American River" , 57000, 161980, '1980-01-01', '2016-03-02')
fea3000 <- find_gaps("Feather River" , 3000, 161980, '1980-01-01', '2016-03-02')
fea52000 <- find_gaps("Feather River" , 52000, 161980, '1980-01-01', '2016-03-02')
fea5000 <- find_gaps("Feather River" , 5000, 161980, '1980-01-01', '2016-03-02')
fea4000 <- find_gaps("Feather River" , 4000, 161980, '1980-01-01', '2016-03-02')
fea2000 <- find_gaps("Feather River" , 2000, 161980, '1980-01-01', '2016-03-02')
fea6000 <- find_gaps("Feather River" , 6000, 161980, '1980-01-01', '2016-03-02')
sac42000 <- find_gaps("Sacramento River", 42000, 161980, '1980-01-01', '2016-03-02')
sta1000 <- find_gaps("Stanislaus River", 1000, 161980, '1980-01-01', '2016-03-02')
mok34000 <- find_gaps("Mokelumne River" , 34000, 161980, '1980-01-01', '2016-03-02')
kni63000 <- find_gaps("Knight's Landing", 63000, 161980, '1980-01-01', '2016-03-02')
gaps <- rbind(ame57000,fea3000,fea52000,fea5000,fea4000,fea2000,fea6000,sac42000,sta1000,mok34000,kni63000)
table(gaps$TrapStatus)
gapsT <- gaps[gaps$TrapStatus == "Not fishing",]
gapsT$SampleHours <- gapsT$SampleMinutes / 60
gapsT$SampleDays <- gapsT$SampleMinutes / 60 / 24
gapsT <- gapsT[,c('trapPositionID','TrapPosition','SampleDate','StartTime','EndTime','SampleMinutes','SampleHours','SampleDays','siteID','siteName','river')]
gapsT <- gapsT[order(gapsT$SampleDays, decreasing=TRUE),]
gapsT365 <- gapsT[gapsT$SampleDays <= 365,]
gapsT365 <- gapsT365[order(gapsT365$river,gapsT365$siteID,gapsT365$trapPositionID,gapsT365$SampleDate,gapsT365$SampleMinutes),]
traps <- unique(gapsT365$trapPositionID)#[c(1:6)]
png("C:/Users/jmitchell/Desktop/theGaps_DO_NOT_PRINT.png",units="in",width=24,height=120,res=300)
par(mfrow=c(length(traps),6))
for(i in 1:length(traps)){
trap <- traps[i]
# -------- set it up ------------
df <- gapsT365[gapsT365$trapPositionID == trap,]
the95 <- quantile(df$SampleMinutes,c(0.95))
river <- df[1,]$river
siteName <- df[1,]$siteName
TrapPosition <- df[1,]$TrapPosition
dist1 <- df$SampleMinutes
dist2 <- ecdf(df$SampleDays)
if(nrow(df) >= 10){
dist3 <- df[df$trapPositionID == trap & df$SampleMinutes <= the95,]$SampleMinutes
dist3b <- df[df$trapPositionID == trap & df$SampleMinutes <= 15840,]$SampleMinutes
dist4 <- ecdf(df[df$trapPositionID == trap & df$SampleMinutes <= the95,]$SampleDays)
} else {
dist4 <- dist3 <- "Insufficient Data"
}
# -------- make the plot --------
plot(1,1,type = "n",frame.plot = FALSE,axes = FALSE,xlab="",ylab=""); u <- par("usr") # zero out margins, make empty plot, get bounding box
text(1,u[3] + 1.1*(u[4]-u[3])/2,river,cex=1)
text(1,u[3] + 1.0*(u[4]-u[3])/2,siteName,cex=1)
text(1,u[3] + 0.9*(u[4]-u[3])/2,TrapPosition,cex=1)
box()
h1 <- hist(dist1,main="Histogram -- All Data",xlab="Minutes",xaxt="n")
axis(1,at=h1$breaks,formatC(h1$breaks, digits = 0, format = "f",big.mark=",") )
plot(dist2, xaxt="n",verticals = TRUE, main="EDF",col.points = "blue",col.hor = "red", col.vert = "bisque",xlab="Days")
axis(1,at=pretty(seq(0,max(df$SampleDays)),length.out=10),formatC(pretty(seq(0,max(df$SampleDays)),length.out=10), digits = 0, format = "f",big.mark=",") )
if(class(dist3)[1] == "character" | class(dist4)[1] == "character"){
plot(1,1,type = "n",frame.plot = FALSE,axes = FALSE,xlab="",ylab=""); u <- par("usr") # zero out margins, make empty plot, get bounding box
text(1,u[3] + 1.1*(u[4]-u[3])/2,paste0("Only N=",nrow(df)," points."),cex=1)
box()
plot(1,1,type = "n",frame.plot = FALSE,axes = FALSE,xlab="",ylab=""); u <- par("usr") # zero out margins, make empty plot, get bounding box
text(1,u[3] + 1.1*(u[4]-u[3])/2,paste0("Only N=",nrow(df)," points."),cex=1)
box()
plot(1,1,type = "n",frame.plot = FALSE,axes = FALSE,xlab="",ylab=""); u <- par("usr") # zero out margins, make empty plot, get bounding box
text(1,u[3] + 1.1*(u[4]-u[3])/2,paste0("Only N=",nrow(df)," points."),cex=1)
box()
} else {
plot(1,1,type = "n",frame.plot = FALSE,axes = FALSE,xlab="",ylab="")
par(new=TRUE)
h <- hist(dist3b,breaks=seq(0,15840,length.out=11*4+1),col="lightgray",xaxt="n",main="Histogram -- 0 to 15,840, with Bin Size = 360 Mins",xlab="Minutes")
axis(side=1, at=seq(0,15840,length.out=11*2+1), labels=seq(0,15840,length.out=11*2+1))
par(new=TRUE)
for(i in 1:11){
drawEm <- seq(0,15840,length.out=11+1)
#abline(v=drawEm[i],col="blue")
segments(drawEm[i],0,drawEm[i],max(h$counts),col="blue",lwd=2)
text(drawEm[i]+400,max(h$counts),drawEm[i]/1440 + 1,cex=1.5,pos=4)
}
hist(dist3,breaks=length(dist3)/5,main="Histogram -- 0 to 95th Percentile",xlab="Minutes")
plot(dist4, xaxt="n",verticals = TRUE, main="EDF -- 0 to 95th Percentile",col.points = "blue",col.hor = "red", col.vert = "bisque",xlab="Days")
axis(1,at=pretty(seq(0,max(df[df$trapPositionID == trap & df$SampleDays <= the95/60/24,]$SampleDays)),length.out=10),formatC(pretty(seq(0,max(df[df$trapPositionID == trap & df$SampleDays <= the95/60/24,]$SampleDays)),length.out=10), digits = 0, format = "f",big.mark=",") )
}
}
dev.off()
par(mfrow=c(1,1))
|
76a87887d72473969097e79efdb7f4dc761dd6e0
|
77dc1bb37706ca78aec3efa42b0e4e39c9aab257
|
/R/RcppExports.R
|
b13a9348a40ed2113e915ddc59f6bbec8d44cba4
|
[] |
no_license
|
yjzeng017/StatComp20088
|
82ee555adda1140cca32667ad7c57b464f59d9aa
|
15219b56be63e117ff21cfd6fe68304b3259f05b
|
refs/heads/master
| 2023-02-02T16:25:54.334536
| 2020-12-20T12:33:42
| 2020-12-20T12:33:42
| 323,021,375
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 673
|
r
|
RcppExports.R
|
# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' @title Random walk
#' @description Random walk with Metropolis sampling method using Rcpp
#' @param sigma variance
#' @param x0 initial value
#' @param N sample size
#' @return a random sample of size N
#' @export
NULL
#' @title Laplace density
#' @description Laplace density
#' @param x real value
#' @return the density at x
#' @export
denLa <- function(x) {
.Call('_StatComp20088_denLa', PACKAGE = 'StatComp20088', x)
}
RWcpp <- function(sigma, x0, N) {
.Call('_StatComp20088_RWcpp', PACKAGE = 'StatComp20088', sigma, x0, N)
}
|
2ecd71938696966f9a496b4e7883e36e8a29372b
|
323d3dcc710c658eeffd9e95878530f720efad42
|
/2-Regression/1. Simple Linear Regression/SimpleLinearRegression.R
|
1273ef260b0b89bab343ced44f33dc91521bda70
|
[] |
no_license
|
ahorsager/MLFoundations
|
35eb98bbf86c57d4606e6760e8ec6f10d192d85e
|
9a6b5848202ebb21f307f38e518ec9e360f63c7a
|
refs/heads/master
| 2020-04-11T01:11:30.260798
| 2019-01-15T01:31:55
| 2019-01-15T01:31:55
| 161,408,250
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,549
|
r
|
SimpleLinearRegression.R
|
# SimpleLinearRegression.R
# A template for a simple linear regression model
# Author: Alan Horsager
# Created: 12-DEC-2018
# **DATA PREPROCESSING**
# Importing the data set
setwd("/Users/horsager/Dropbox/projects/analytics/MLTraining/2-Regression/Simple Linear Regression")
DataSet = read.csv('SalaryData.csv')
# **SPLIT TRAINING & TEST DATA**
set.seed(123)
Split = sample.split(DataSet$Salary, SplitRatio = 2/3)
TrainingSet = subset(DataSet, Split == TRUE)
TestSet = subset(DataSet, Split == FALSE)
# **MODEL**
# Fitting linear regression to the data set
regressor = lm(formula = Salary ~ YearsExperience,
data = TrainingSet)
summary(regressor) # Summary of lm fit results
# Predict test set results
TestPredict = predict(regressor, newdata = TestSet)
# **DATA VISUALIZATION**
# Plot regression fit of training set
ggplot() +
geom_point(aes(x = TrainingSet$YearsExperience, y = TrainingSet$Salary),
color = 'red') +
geom_line(aes(x = TrainingSet$YearsExperience, y = predict(regressor, newdata = TrainingSet)),
color = 'blue') +
ggtitle('Salary vs. Experience (Training Set)') +
xlab('Years of Experience') +
ylab('Salary')
# Plot regression of test set
ggplot() +
geom_point(aes(x = TestSet$YearsExperience, y = TestSet$Salary),
color = 'red') +
geom_line(aes(x = TrainingSet$YearsExperience, y = predict(regressor, newdata = TrainingSet)),
color = 'blue') +
ggtitle('Salary vs. Experience (Test Set)') +
xlab('Years of Experience') +
ylab('Salary')
|
a59325be06c7b1a48d1bec440125968585d62e43
|
d2ca86d0aa2e84b14b0d455ded547df90b1a7bc1
|
/plot1.R
|
fea142390474772b9ccda2b58659320db590f66c
|
[] |
no_license
|
adoroszlai/ExData_Plotting1
|
e5622bf069b8aa2e138a362a596e0377c34ce3ef
|
ac1e9481a8fcb62ffa02d5a99210b4816c5f3777
|
refs/heads/master
| 2021-01-18T11:08:33.306316
| 2014-06-03T17:40:32
| 2014-06-03T17:40:32
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 201
|
r
|
plot1.R
|
source('clean_data.R')
# plot 1
png('plot1.png', width = 480, height = 480)
hist(df$global_active_power, main = "Global Active Power", xlab = "Global Active Power (kilowatts)", col = "red")
dev.off()
|
08d80b9ed21597dd15f87c3e38be2ee33b8d3fa0
|
6b95e88fd11aff60e778c90ef75e75383a965c0c
|
/Q1.R
|
1ff42b01eda80588f0c332c416209856ddeefaa6
|
[] |
no_license
|
bishal839/AP_LAB8
|
2de69d3c7ef804a222793ddfe652ee4d447b889b
|
e8a87d00e36853e5c4119885d24ab018c6d56f8c
|
refs/heads/master
| 2020-05-03T08:59:32.315312
| 2019-03-30T10:29:43
| 2019-03-30T10:29:43
| 178,541,736
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 638
|
r
|
Q1.R
|
x=read.csv("student.csv")
print(paste("\nquestion 1\n"))
y<-max(x$Percent)
print(subset(x,(Percent==y)))
print(paste("\nquestion 2\n"))
z<-subset(x,(Branch=="cse"))
print(subset(z,(Percent>=80)))
print(paste("\nquestion 3\n"))
print(subset(x,as.Date(DOA)>(as.Date("2016/07/01"))))
print(paste("Question 4"))
write.csv(x,"student1.csv")
cat(11,160,"Sohail Alam","CSE",82,"2017/05/23",file = "student1.csv",sep = ",",append = TRUE)
cat(file="student1.csv",sep = "\n",append = TRUE)
cat(12,327,"Md. Hamid Reza","CSE",96,"2018/05/12",file = "student1.csv",sep = ",",append = TRUE)
y=read.csv("student1.csv")
print(y)
|
672f2b3b7cef92d57004aea0bbcb769786c37aa9
|
d13a597f0dca27d35d63991e887a19dc3e5354c4
|
/R/packages.R
|
363c6ae1f9b543d93a1ba98bd910eafbd162a083
|
[
"Apache-2.0"
] |
permissive
|
NewGraphEnvironment/backupr
|
9de69ffc286450a33fc807ac74673aa391fab54b
|
0230245296bc59557f307f56eac910e22d789a2c
|
refs/heads/main
| 2023-03-30T13:07:19.789039
| 2021-03-28T22:47:23
| 2021-03-28T22:47:23
| 346,763,301
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 85
|
r
|
packages.R
|
pacman::p_load(
tidyverse,
RPostgres,
RPostgreSQL,
DBI,
sf,
data.table
)
|
f0e397809ee482545fe27f95e8dacf202e5aa2ea
|
3f47892735e42d094e31341f6b306424cf9d36d2
|
/R/feed.extract.R
|
93c554c6bdbffb2a85c259af48dabd79b0e43de7
|
[] |
no_license
|
lovetoken/feed
|
f562c900a760a44344f25bb23d3baf138d64a7e5
|
52dc2f9f577ae26756c579c0611a2f184fe537ff
|
refs/heads/master
| 2020-03-25T01:02:11.830724
| 2018-08-02T10:07:46
| 2018-08-02T10:07:46
| 143,218,782
| 1
| 0
| null | 2018-08-01T23:37:09
| 2018-08-01T23:37:09
| null |
UTF-8
|
R
| false
| false
| 807
|
r
|
feed.extract.R
|
#' feed.extract
#'
#' This function extract and re-combine the list from feed.info().
#' @param url A URL that you want to scraping.
#' @param n A number of list from feed.info().
#' @keywords feed, feedipedia
#' @export
#' @import rvest
#' @import dplyr
#' @examples
#' feed.extract("https://www.feedipedia.org/node/556",2)
#'
feed.extract <- function(url,n) {
# package
stopifnot(require(rvest), require(dplyr))
html <- read_html(url, encoding="UTF-8")
#nutrients
list <- html %>% html_nodes("table") %>% html_table()
a <- list[[n]]
end <- c(which(a[,2] == '') - 1,nrow(a))
start <- c(2,which(a[,2] == '') + 2)
df <- list()
for(i in 1:length(end)){
df[[i]] <- a[start[i]:end[i],]
names(df[i]) <- a[start[i]-1,1]
colnames(df[[i]]) <- a[start[i]-1,]
}
print(df)
}
|
618933237507ac6651cdffe043c3380b1919a47a
|
ee360f07fd7a202207aec4a26cfc68ba3d053bc5
|
/analysis/utils.R
|
d539c3071d8b6ec15738a9254f9bee1834741d0a
|
[] |
no_license
|
timole/usage
|
bb3715d903022d33b4028f472908db5c43b7c2f8
|
a06fc4349c6b968b6020b2be8065761a42322464
|
refs/heads/master
| 2021-01-10T19:41:36.064006
| 2015-05-07T10:05:59
| 2015-05-07T10:05:59
| 33,768,302
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,205
|
r
|
utils.R
|
library("kohonen")
library("rjson")
classifierToXY <- function(somMap, c) {
col <- (c - 1) %% somMap$grid$xdim + 1
row <- somMap$grid$ydim - ( floor( (c - 1) / somMap$grid$xdim))
return(list(x = col - 1, y = row - 1))
}
getSomItemLocations <- function(somMap) {
lapply(somMap$unit.classif, function(c) { return(classifierToXY(somMap, c))})
}
somToDataMap <- function(somMap) {
ids <- rownames(somMap$data)
datas <- split(somMap$data, row(somMap$data))
locations <- getSomItemLocations(somMap)
all <- list()
for(i in seq(1:length(ids))) {
item <- list(x = locations[[i]]$x, y = locations[[i]]$y)
all[[ids[i]]] <- item
}
dimensions <- getDimensions(somMap)
m <- kohmap$data
m <- cbind(id = rownames(m), m)
itemDimensionValues = toMapByColumnName(m, "id")
dataMap <- list(xdim = somMap$grid$xdim, ydim = somMap$grid$ydim, items = all, dimensions = dimensions) #itemDimensionValues = itemDimensionValues
return(dataMap)
}
somToJSON <- function(somMap) {
dataMap <- somToDataMap(somMap)
return(rjson::toJSON(dataMap))
}
toMapByColumnName <- function(df, columnName) {
colIndex <- grep(paste(paste("^", columnName, sep = ""), "$", sep = ""), colnames(df))
userList <- list()
dataTypes <- sapply(df, class)
apply(df, 1, function(d) {
itemList <- list()
i <- 1
lapply(colnames(df), function(colName) {
item <- d[colName]
if(dataTypes[i] != "factor") {
class(item) <- dataTypes[i]
} else {
}
itemList[[colName]] <<- item[[1]]
i <<- i + 1
})
userList[[ d[colIndex] ]] <<- itemList
})
return(userList)
}
getDimensions <- function(somMap) {
matrices <- list()
m <- matrix(0, nrow = somMap$grid$ydim, ncol = somMap$grid$xdim)
vals <- lapply(seq(1, ncol(somMap$data)), function(i) {
return(aggregate(as.numeric(somMap$data[,i]), by=list(somMap$unit.classif), FUN=mean, simplify=TRUE)[,2])
})
d <- 1
for(dimension in vals) {
i <- 1
for(val in dimension) {
coords <- classifierToXY(somMap, i)
col <- coords$x + 1
row <- coords$y + 1
m[row, col] <- val
i <- i + 1
}
name <- colnames(somMap$data)[d]
matrices[[name]] <- t(m)
d <- d + 1
}
return(matrices)
}
|
637cf985e944cb990ad33d23d196c287fb5587bd
|
26dfe6af409cb36c6aa723dd92e53d793420632f
|
/long_term_trials/code/data_soil_carbon.R
|
3995a9ecd19e6ba14e6ed2212bb9b8cd5a0fe189
|
[] |
no_license
|
cwreed/SHI
|
d1a6f8050eaf384473dc82a2f02c13662dd5196d
|
88e874faec7568eaf62501faf3bb039080b3f102
|
refs/heads/master
| 2021-06-27T22:39:21.269491
| 2021-01-21T21:07:21
| 2021-01-21T21:07:21
| 201,963,976
| 0
| 0
| null | 2020-04-28T17:58:22
| 2019-08-12T16:04:30
|
R
|
UTF-8
|
R
| false
| false
| 7,092
|
r
|
data_soil_carbon.R
|
source("code/libraries.R")
d.carbon.raw <- read.xlsx('data/Long_term_yield _data.xlsx', sheet = 'Carbon')
d.carbon <- d.carbon.raw[,-20]
names(d.carbon)[1:5] <- c("Paper",
"DOI",
"Study_name",
"Years_of_study",
"Year_of_observation")
d.carbon %>%
fill(names(.)[c(1:2)]) %>%
group_by(DOI) %>%
fill(names(.)[c(3:4,6:16,17,18)]) %>%
separate(col = "Years_of_study", into = c("Year_started","Year_ended"), sep = "-") %>%
ungroup() %>%
mutate_if(grepl(names(.),pattern = "Yield|begin|end|start|length"), as.numeric) %>%
mutate_if(is.character, as.factor) -> d.carbon
d.carbon[d.carbon == 'Placeholder'] <- NA
paste.drop.NA <- function(x, sep = ", ") {
x <- gsub("^\\s+|\\s+$", "", x)
ret <- paste(x[!is.na(x) & !(x %in% "")], collapse = sep)
is.na(ret) <- ret == ""
return(ret)
}
d.carbon$Trt.combo <- apply(d.carbon[,7:13], 1, paste.drop.NA)
## Merge trt.codes
d.carbon.trts <- read.csv("data/d.carbon.trts.csv")
str(d.carbon.trts)
test <- d.carbon %>%
anti_join(d.carbon.trts[,c(1,10:12)])
d.carbon %>%
inner_join(d.carbon.trts[,c(1,10,11,12)]) -> d.carbon
d.carbon <- d.carbon[!is.na(d.carbon$Trt.code),]
## Summarize within papers
d.carbon <- droplevels(d.carbon[-which(d.carbon$`Soil.sample.depth.(cm)` %in% c(">115",">120")),])
d.carbon$`Soil.sample.depth.(cm)` <- as.character(d.carbon$`Soil.sample.depth.(cm)`)
d.carbon$Year_of_observation <- as.numeric(as.character(d.carbon$Year_of_observation))
d.carbon$Bulk.density <- as.numeric(as.character(d.carbon$Bulk.density))
d.carbon %>%
dplyr::group_by(Paper) %>%
filter(Year_of_observation == max(as.numeric(Year_of_observation))) %>%
separate(`Soil.sample.depth.(cm)`, into = c("Top.depth", "Bottom.depth"), sep = "-", remove = F) %>%
mutate(Depth.increment=as.numeric(Bottom.depth) - as.numeric(Top.depth)) %>%
mutate(max.bottom = max(as.numeric(Bottom.depth))) %>%
mutate(Depth.proportion =as.numeric(Depth.increment)/max.bottom) %>%
mutate(Soil.kg.per.hectare = case_when(
!is.na(Bulk.density) ~ case_when(
Bulk.density.units == 'g cm-3' ~ as.numeric(100000 * Bulk.density * Depth.increment),
Bulk.density.units == 'kg m-3' ~ as.numeric(10000 * Bulk.density * Depth.increment),
Bulk.density.units == 'Mg m-3' ~ as.numeric(1e+7 * Bulk.density * Depth.increment),
Bulk.density.units == 't m-3' ~ as.numeric(1e+7 * Bulk.density * Depth.increment)
),
is.na(Bulk.density) ~ as.numeric(100000*Depth.increment))) -> d.carbon
d.carbon$Depth.proportion
d.carbon[is.na(d.carbon$Depth.proportion), "Depth.proportion"] <- 1
d.carbon <- d.carbon[as.numeric(as.character(d.carbon$Bottom.depth)) < 50,]
## Filter out unusual C measurements, convert all SOC data to same units, assume BD of one
d.carbon %>%
filter(
`SOM.or.SOC` == "SOM"|
`SOM.or.SOC` == "SOC"|
`SOM.or.SOC` == "SOM (total)"|
`SOM.or.SOC` == "SOC stock as equivalent soil mass"|
`SOM.or.SOC` == "SOC stock"|
`SOM.or.SOC` == "SOC content"|
`SOM.or.SOC` == "SOC storage"|
`SOM.or.SOC` == "SOC (total)"|
`SOM.or.SOC` == "SOC Stock"|
`SOM.or.SOC` == "TOC"|
`SOM.or.SOC` == "Total C"|
`SOM.or.SOC` == "Total SOC"|
`SOM.or.SOC` == "SOC pool"
) -> d.carbon
d.carbon <- droplevels(d.carbon)
unique(d.carbon$C.Units)
d.carbon <- droplevels(d.carbon[!d.carbon$C.Units %in% "g kg-1 aggregates",])
d.carbon <- d.carbon[!d.carbon$C.Units %in% "kg C m-2\n(on 450 kg m-2 soil)",]
d.carbon$Amount <- as.numeric(as.character(d.carbon$Amount))
d.carbon %>%
mutate(SOC.g.kg = case_when(
C.Units == "%" ~ Amount/.1,
#C.Units == "kg C m-2\n(on 450 kg m-2 soil)" ~ Amount*1000/Soil.kg.per.hectare*1000,
C.Units == "kg m-2" ~ Amount*10000*1000/Soil.kg.per.hectare,
C.Units == "g kg-1" ~ Amount,
C.Units == "Mg ha-1" ~ (Amount*1000000/Soil.kg.per.hectare),
C.Units == "T ha-1" ~ (Amount*1000000/Soil.kg.per.hectare),
C.Units == "t ha-1" ~ (Amount*1000000/Soil.kg.per.hectare)
)) -> d.carbon
d.carbon[d.carbon$SOM.or.SOC %in% c("SOM","SOM (total)"),"SOC.g.kg"] <- d.carbon[d.carbon$SOM.or.SOC %in% c("SOM","SOM (total)"),"SOC.g.kg"]*.58
##
d.carbon$`Soil.sample.depth.(cm)` <- as.factor(d.carbon$`Soil.sample.depth.(cm)`)
d.carbon %>%
group_by(Paper, Trt.combo, `Soil.sample.depth.(cm)`) %>%
mutate(SOC.g.kg.weighted = Depth.proportion*SOC.g.kg) %>%
group_by(Paper, Trt.combo) %>%
dplyr::summarise(SOC.SD = sd(SOC.g.kg.weighted, na.rm = TRUE),
SOC.n = n(),
SOC.g.kg.weighted = sum(SOC.g.kg.weighted, na.rm = TRUE)) -> d.carbon.summary
#d.carbon.summary <- (d.carbon.summary[!d.carbon.summary$SOC.g.kg.weighted > 150,])
#d.carbon.summary <- (d.carbon.summary[!d.carbon.summary$SOC.g.kg.weighted == 0,])
## New carbon data
d.carbon.new <- read.xlsx("data/AgEvidence_Oldfield_selected.xlsx", sheet = "carbon")
d.carbon.new$Trt.combo <- apply(d.carbon.new[,6:12], 1, paste.drop.NA)
d.carbon.trts <- read.csv("data/d.carbon.trts.csv")
test <- d.carbon.new %>%
anti_join(d.carbon.trts[,c(1,10,11,12)])
d.carbon.new %>%
inner_join(d.carbon.trts[,c(1,10,11,12)]) -> d.carbon.new
## Summarize within papers
d.carbon.new %>%
group_by(Paper, crop) %>%
filter(obs.year == max(obs.year)) %>%
mutate(Depth.increment = as.numeric(`bottom.measurement.depth.(cm)`) - as.numeric(`top.measurement.depth.(cm)`)) %>%
do(mutate(., max.bottom = as.numeric(max(as.numeric(`bottom.measurement.depth.(cm)`))))) %>%
mutate(Depth.proportion = as.numeric(Depth.increment)/max.bottom) %>%
mutate(Soil.kg.per.hectare = case_when(
!is.na(soil.bulk.density.units) ~ case_when(
soil.bulk.density.units == 'g/cm^3' ~ as.numeric(1e+8/1000 * soil.bulk.density.value * Depth.increment),
soil.bulk.density.units == 'Mg/m^3' ~ as.numeric(1e+7 * soil.bulk.density.value * Depth.increment)),
is.na(soil.bulk.density.units) ~ as.numeric(100000*Depth.increment))) -> d.carbon.new
d.carbon.new[is.na(d.carbon.new$Depth.proportion), 'Depth.proportion'] <- 1
d.carbon.new <- d.carbon.new %>%
filter(`bottom.measurement.depth.(cm)` < 50 | is.na(`bottom.measurement.depth.(cm)`)) %>%
filter(Paper != "Campbell et al. 2007")
d.carbon.new %>%
mutate(SOC.g.kg = case_when(
soil.carbon.units == '%' ~ soil.carbon.value/.1,
soil.carbon.units == 'Mg/ha' ~ (soil.carbon.value*1000000/Soil.kg.per.hectare),
soil.carbon.units == 'g C/g soil' ~ soil.carbon.value*1000)) -> d.carbon.new
d.carbon.new %>%
group_by(Paper, Trt.combo, `top.measurement.depth.(cm)`) %>%
mutate(SOC.g.kg.weighted = Depth.proportion*SOC.g.kg) %>%
group_by(Paper, Trt.combo) %>%
summarize(SOC.SD = sd(SOC.g.kg.weighted, na.rm = TRUE),
SOC.n = n(),
SOC.g.kg.weighted = sum(SOC.g.kg.weighted, na.rm = TRUE)) -> d.carbon.new_summary
d.carbon.summary %>%
rbind(d.carbon.new_summary) -> d.carbon.summary
d.carbon.summary[is.na(d.carbon.summary$SOC.SD), "SOC.SD"] <- 0
save("d.carbon.summary", file = "data/d.carbon.summary.RData")
|
4a758677541eba70396a48644b72220b47cda9d3
|
b4dac3475d3c9d6f56b5cc24b80f904cf24400b5
|
/r_course_phd/all_R_script_files1/simple_function.R
|
9e3eab7e6a0a20bc4c511468717f8a17aac003fc
|
[] |
no_license
|
rohitfarmer/learning
|
8aecdeddfa82bddf59be4ee9005e6783df4a4010
|
f0550cb9bc91287f3fbcee13d63fb182462ee920
|
refs/heads/master
| 2020-03-13T00:43:24.836300
| 2019-10-06T21:20:10
| 2019-10-06T21:20:10
| 130,882,198
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 65
|
r
|
simple_function.R
|
add <- function(arg1, arg2) {
result <- arg1 + arg2
result
}
|
c1b40a7a1dfa71e429b934fe33b59146660b3d40
|
ef290d0ed8111815f8d83054a80f79f34e2b82ce
|
/Alderaan.R
|
1842f13cb5d02dc4ef541be63cf8784da75c3e6a
|
[] |
no_license
|
Venkatagutha/Web-APIs-in-R.
|
14ec0451d2f3ac92b2e00a4d649c0c6724039a2a
|
1b475563b4e3166872dbc00b160aa1d2a7f272cc
|
refs/heads/master
| 2020-03-22T12:31:39.423708
| 2018-07-12T03:50:14
| 2018-07-12T03:50:14
| 140,045,321
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 549
|
r
|
Alderaan.R
|
install.packages("httr")
install.packages("jsonlite")
install.packages("magrittr")
library(httr)
library(jsonlite)
library(magrittr)
# With the help of method GET, get the
#data for planet Alderaan in StarWorlds
alderaan<-GET("http://swapi.co/api/planets/",
query = list(search="alderaan"))
alderaan$status_code
alderaan$header$`content-type`
names(alderaan)
#getting the content
text<- content(alderaan, as="text", encoding = "UTF-8")
# parsing with JSON LITE
cont<- text%>% fromJSON
planet_data<-cont$results
str(planet_data)
|
9f3d2c0965a4a03cb9b954a303eaefd4cab9ffab
|
7aa6036ba7caf7ca08c6e341814ada363838ad39
|
/Ch04/4_2_Condition.R
|
6d2efdc2059ead34a039be13034f8b59ae3ed482
|
[] |
no_license
|
kimhalyn/R
|
1db9ee75fa944f66fee63cf9abc33f94be82b296
|
ca67137b14d1e14650f859ced5dfbd9e6670c0cf
|
refs/heads/master
| 2023-06-09T17:17:24.121110
| 2021-07-01T15:11:34
| 2021-07-01T15:11:34
| 330,568,605
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,810
|
r
|
4_2_Condition.R
|
# 날짜 : 2021/01/19
# 이름 : 김하린
#내용 : Ch04.제어문과 함수 - 조건문 교재 p110
#교재 p110 실습 - if() 사용하기
x <- 50;y <- 4;z <- x * y
if(x * y >= 40) {
cat("x * y의 결과는 40 이상입니다.\n")
cat("x * y = ", z)
}else{
cat("x * y의 결과는 40 미만입니다. x * y = ",z, "\n")
}
#교재 p110 실습 - if() 사용으로 입력된 점수의 학점 구하기
score <- scan()
result <- "노력" #결과 초기값 설정
if(score >= 80){
result <- "우수"
}
cat("당신의 학점은", result, score)
#교재 p111 실습 - if ~ else if 형식으로 학점 구하기
score <- scan()
if(score >= 90){
result = "A학점"
}else if(score >= 80){
result = "B학점"
}else if(score >= 70){
result = "C학점"
}else if(score >= 60){
result = "D학점"
}else{
result = "F학점"
}
cat("당신의 학점은", result)
print(result)
#교재 p112 실습 - ifelse() 사용하기 (조건, 참일 경우 처리문, 거짓일 경우 처리문)
score <- scan()
ifelse(score >= 80, "우수", "노력")
ifelse(score <= 80, "우수", "노력")
#교재 p113 실습 - switch() 를 사용하여 사원명으로 급여정보 보기
switch("name", id="hong", pwd="1234", age=35, name="홍길동")
empname <- scan(what="")
empname
switch(empname,
hong = 250,
lee = 350,
kim = 200,
kang = 400)
#교재 p114 실습 - 벡터에서 which() 사용:index 값을 반환
name <- c("kim", "lee","choi", "park")
which(name == "choi")
#교재 p114 실습 - 데이터프레임에서 which() 사용
no <- c(1:5)
name <- c("홍길동", "이순신", "강감찬", "유관순", "김유신")
score <- c(85, 78, 89, 90, 74)
exam <- data.frame(학번 = no, 이름 = name, 성적 = score)
exam
which(exam$이름 == "유관순")
exam[4,] #4행 데이터 보기
|
e8dd351fa7fe437e55b7010faff998dc0812fe26
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/VTrack/examples/PointsCircuitous_crocs.Rd.R
|
e1e707477fb175add4cb413cdca25eb47546a626
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 696
|
r
|
PointsCircuitous_crocs.Rd.R
|
library(VTrack)
### Name: PointsCircuitous_crocs
### Title: Points File Containing VR2 Locations on the Wenlock River in
### 2008 with Waypoints Connecting Receivers
### Aliases: PointsCircuitous_crocs
### Keywords: datasets
### ** Examples
# Load the points file for the Wenlock River
data(PointsCircuitous_crocs)
head(PointsCircuitous_crocs)
receiversonly <- na.omit(PointsCircuitous_crocs)
# Plot the locations of the receivers plus the waypoints
par(mfrow=c(1,1),las=1,bty="l")
plot(PointsCircuitous_crocs$LONGITUDE, PointsCircuitous_crocs$LATITUDE,
pch=1,cex=0.5,col="grey",xlab="Longitude",ylab="Latitude")
points(receiversonly$LONGITUDE,receiversonly$LATITUDE,cex=1,pch=10)
|
e62ecf9fa74ff3a725b37268de28c8f5f2c1da85
|
66f8711bc942a1bc635a6deea253e9a49c718094
|
/man/romanToArabic.Rd
|
afccb3474947a903ada699c05917d38b32775986
|
[
"MIT"
] |
permissive
|
seanrsilver/novnet
|
bd179476c48a8dd809757c60488dde7193a4145b
|
85107cfbbabc68c603134db5b5fc8bbf9219624b
|
refs/heads/master
| 2020-06-05T18:20:58.057024
| 2019-06-18T14:29:45
| 2019-06-18T14:29:45
| 192,495,039
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 490
|
rd
|
romanToArabic.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/romanToArabic.R
\name{romanToArabic}
\alias{romanToArabic}
\title{Roman Numeral Conversion}
\usage{
romanToArabic(filename)
}
\arguments{
\item{filename}{File name as character string, i.e. "Crusoe".}
}
\description{
This function converts roman numeral chapter numbers to arabic, from Project Gutenberg files.
It expects chapter headings in this format: CHAPTER I, CHAPTER II, etc...
}
\keyword{Disambiguation}
|
ac435ccee6822aa1ed7324dac50670fc2e95d0a2
|
a010c9aaf3a0e87e289f6fc9aa232ebf80b15116
|
/Code_fraud_project_12_new.R
|
64f0720ba3b4f13f2daa18637ddd9b391e08e9f0
|
[] |
no_license
|
jmunich/Fraud-detection
|
a0d6d07841453f7ff1e498b26f6088a583c3cc95
|
dd0b8e7e9b6e98206c403a9ef99d4e745dfca1e0
|
refs/heads/master
| 2020-05-05T04:09:17.420473
| 2019-04-05T14:55:49
| 2019-04-05T14:55:49
| 179,699,613
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,056
|
r
|
Code_fraud_project_12_new.R
|
###Create corpus
library(caret)
library(readtext)
library(quanteda)
library(spacyr)
library(igraph)
library(text2vec)
library(reshape2)
library(gtools)
library(lexicon)
library(gdata)
library(pROC)
source("Get_new_text.R")
###Clean data
##From British to American
toks<-tokens(tot_corpus)
vocabulary<-read.csv("vocab.txt")
toks<-tokens_replace(toks, as.character(vocabulary[,1]), as.character(vocabulary[,2]))
toks<-tokens_remove(toks, pattern = " ")
toks<-lapply(toks, function(x) tolower(x))
ctoks<-unlist(lapply(toks, function(x) paste(x, sep=" ", collapse=" ")))
tot_corpus_clean<-corpus(ctoks)
###########
### Entities
spacy_initialize(model='en')
docvars(tot_corpus_clean, "text")<-all_id[,1]
identify<-docvars(tot_corpus_clean)
identify<-cbind(identify, rownames(identify))
parses<-spacy_parse(tot_corpus_clean, dependency=TRUE)
entities<-entity_extract(parses, type = "all")
entities<-cbind(entities, mne=rep(0,length(entities[,1])))
entities[,5]<-ifelse(entities$entity_type %in% c("ORG", "QUANTITY", "ORDINAL", "CARDINAL"),1,0)
encount <-aggregate(entities[,5], by=list(entities[,1]), FUN=sum, na.rm=TRUE)
colnames(identify)<-c("id","match")
colnames(encount)<-c("match","score")
endata<-merge(identify, encount, by="match", all.x=TRUE)
endata[which(is.na(endata[,3])==TRUE),3]<-0
endata[,3]<-endata[,3]/ntoken(tot_corpus_clean)
###Get lemma corpus
lemmatized<-c()
counter<-0
for(i in unique(parses[,1])){
counter<-counter+1
wordvec<-parses[which(parses[,1]==i),5]
lemmatized[counter]<-paste(wordvec, sep=" ", collapse=" ")
}
tot_corpus_clean_lemma<-corpus(lemmatized)
### Dependencies
prop_root<-c()
prop_nsubj<-c()
prop_nobj<-c()
slope_root<-c()
slope_nsubj<-c()
slope_nobj<-c()
counteri<-0
for(i in unique(parses[,1])){
counteri<-counteri+1
root<-c()
subject<-c()
object<-c()
counterj<-0
for(j in unique(parses[which(parses[,1]==i),2])){
counterj<-counterj+1
set<-NULL
set<-as.matrix(parses[which(parses[,1]==i & parses[,2]==j),])
root_id<-set[which(set[,8]=="ROOT"),3]
nsubj_id<-set[which(set[,8]=="nsubj"),3]
pobj_id<-set[which(set[,8]=="pobj"),3]
root[counterj]<-length(which(set[,7]==root_id))/length(set[,1])
subject[counterj]<-length(which(set[,7]==nsubj_id))/length(set[,1])
object[counterj]<-length(which(set[,7]==pobj_id))/length(set[,1])
}
prop_root[counteri]<-mean(root)
prop_nsubj[counteri]<-mean(subject)
prop_nobj[counteri]<-mean(object)
slope_root[counteri]<-lm(root~c(1:length(root)))$coefficients[2]
slope_nsubj[counteri]<-lm(subject~c(1:length(subject)))$coefficients[2]
slope_nobj[counteri]<-lm(object~c(1:length(object)))$coefficients[2]
}
dependencies_data<-cbind(prop_root,prop_nsubj,prop_nobj, slope_root, slope_nsubj, slope_nobj)
###N-grams with stopwords
ngrams<-dfm(tot_corpus_clean_lemma, remove_punct=TRUE, remove_numbers=TRUE, ngrams=2:3)
select<-dfm_trim(ngrams, sparsity=.90)
select1<-dfm_tfidf(select)
ngram_data<-as.data.frame(select1)
###Ngrams nonstop
##NB Get better stopwords
source("get_sw.R")
nostop_tot_corp<-tokens(tot_corpus_clean_lemma)
nostop_tot_corp<-tokens_remove(nostop_tot_corp, pattern=c(sws, "et","al", "p","c"))
ns_ngrams<-dfm(nostop_tot_corp, remove_punct=TRUE, remove_numbers=TRUE, ngrams=1:3)
ns_select<-dfm_trim(ns_ngrams, sparsity=.90)
ns_select1<-dfm_tfidf(ns_select)
ns_ngram_data<-as.data.frame(ns_select1)
dup<-which(colnames(ns_ngram_data) %in% colnames(ngram_data))
ns_ngram_data<-ns_ngram_data[,-dup]
###Readability
toks<-tokens(tot_corpus)
vocabulary<-read.csv("vocab.txt")
toks<-tokens_replace(toks, as.character(vocabulary[,1]), as.character(vocabulary[,2]))
toks<-tokens_remove(toks, pattern = " ")
ctoksup<-unlist(lapply(toks, function(x) paste(x, sep=" ", collapse=" ")))
tot_corpus_cleanup<-corpus(ctoksup)
readability<-textstat_readability(tot_corpus_cleanup, "Flesch")
names(readability)<-c("id","Flesch")
### Semantic network: here, I create a
### boolean co-occurence network for every individual document.
### The networks consist of the 30 most frequent tokens in all aggregated documents.
my_toks<-tokens(tot_corpus_clean_lemma, remove_punct = TRUE, remove_numbers = TRUE)
my_toks<-tokens_remove(my_toks, c(stopwords('en'),"et","al", "p", "c"))
my_dfm<-dfm(my_toks, tolower=TRUE)
# Get wordlist
source("words.R")
# Total probablities of occurrence
my_dfm_p<-dfm_weight(my_dfm, scheme="prop")
props<-dfm_select(my_dfm_p, allw)
tprops<-as.data.frame(props)[,-1]
# Quantites of word occurrences
quants<-dfm_select(my_dfm, allw)
tquant<-as.data.frame(quants)[,-1]
# Prepare a dataframe for values
edgelist<-permutations(length(allw), r=2, allw, repeats.allowed = TRUE)
edgelist<-paste(edgelist[,1],edgelist[,2], sep = "_")
my_fcms<-data.frame(matrix(0,ncol=length(edgelist), nrow=length(my_toks)))
colnames(my_fcms)<-edgelist
for(h in 1:length(my_toks)){
cmat<-fcm(paste(my_toks[h], sep=" ", collapse=" "), context="window", window=10, count="weighted")
pcmat<-as.matrix(fcm_select(cmat, allw))
namemat<-colnames(pcmat)
if(length(pcmat)>0){
for(i in 1:length(pcmat[,1])){
for(j in 1:length(pcmat[,1])){
pcmat[i,j]<-(pcmat[i,j]/tquant[h,namemat[j]])/tprops[h,namemat[i]]
}
}
el<-melt(as.matrix(pcmat))
edges<-paste(el[,1],el[,2], sep = "_")
my_fcms[i, edges]<-el[,3]
}
}
my_fcms<-my_fcms[,-which(colSums(my_fcms)==0)]
### Add sentiment
file_gn<-read.table("unretracted_new.txt", header=TRUE)
file_fn<-read.table("fraudulent_new.txt", header=TRUE)
file_gn<-file_gn[,c(4,7)]
file_fn<-file_fn[,c(4,7)]
LIWC<-rbind(file_gn,file_fn)
### Transform from long to wide
names(all_id)[2]<-"section_of_article"
final_data<-list(as.matrix(all_id[,3],ncol=1), ngram_data[,-1], ns_ngram_data, as.matrix(readability[,-1],ncol=1), my_fcms, LIWC, as.matrix(endata[,3],ncol=1), dependencies_data)
feature_names<-c("fraud", "ngrams", "ns_ngrams", "readability", "hedging", "liwc", "entities", "dependencies")
feat_namelist<-list()
final_list<-list()
for(i in 1:length(final_data)){
set<-cbind(all_id[,c(1,2)],final_data[[i]])
features<-reshape(set, idvar = "id", timevar = "section_of_article", direction = "wide")
feat_namelist[[i]]<-colnames(final_data[[i]])
final_list[[i]]<-features[,-1]
}
widid<-reshape(all_id[,c(1,2)], idvar = "id", timevar = "section_of_article", direction = "wide")
for(i in 1:length(widid[,1])){
if(widid[i,1]>900){widid[i,1]<-widid[i,1]/1000}
}
sortedmems<-rep(max(memberdata[,2])+1,length(matchids))
for(i in 1:length(matchids)){
if(matchids[i]>900){matchids[i]<-matchids[i]/1000}
if(length(memberdata[which(memberdata[,1]==matchids[i]),2])>0){
sortedmems[i]<-memberdata[which(memberdata[,1]==matchids[i]),2]
}
}
wide_feat_namelist<-list()
for(i in 1:length(feat_namelist)){
namevec<-c()
secs<-c(".d",".i",".m",".r")
for(k in 1:4){
l<-length(feat_namelist[[i]])*(k-1)
for(j in 1:length(feat_namelist[[i]])){
namevec[j+l]<-paste(feat_namelist[[i]][j],secs[k],sep="")
}
}
wide_feat_namelist[[i]]<-namevec
}
final_list[[1]][is.na(final_list[[1]])]<-1000
score<-list()
for(i in 1:length(final_list[[1]][,1])){
score[[i]]<-unique(unlist(final_list[[1]][i,]))
}
fraudvec<-c()
for(i in 1:length(score)){
if(length(score[[i]])==1){
fraudvec[i]<-score[[i]][1]
}
if(length(score[[i]])==2){
fraudvec[i]<-min(score[[i]])
}
if(length(score[[i]])>2){
print("Warning, something went teribly wrong!!!!")
}
}
final_list[[1]]<-as.matrix(fraudvec, ncol=1)
names(final_list)<-feature_names
names(wide_feat_namelist)<-feature_names
for(i in 1:length(final_list)){
names(final_list[[i]])<-wide_feat_namelist[[i]]
}
names(final_list[[1]])<-"fraud"
names(final_list[[4]])<-c("flesch.d","flesch.i","flesch.m","flesch.r")
names(final_list[[7]])<-c("entities.d","entities.i","entities.m","entities.r")
final_frame<-do.call(cbind, final_list)
exclude<-complete.cases(final_frame)
for(i in 1:length(final_list)){
final_list[[i]]<-final_list[[i]][-which(exclude==FALSE),]
if(i>1){
if(length(which(colSums(final_list[[i]])==0)==TRUE)>0)
final_list[[i]]<-final_list[[i]][,-which(colSums(final_list[[i]])==0)]
}
}
widid<-widid[-which(exclude==FALSE),]
source("Get_membership.R")
matchids<-reshape(all_id[,-3], idvar = "id", timevar = "section_of_article", direction = "wide")
matchids<-matchids[,1]
usemember<-sortedmems[-which(exclude==FALSE)]
set.seed(1)
selects<-c()
a<-1
b<-1
while(a<.6 | a>.65 | b<.494 | b>.506){
selects<-sample(unique(usemember), sample(1:(length(unique(usemember)))))
intraining<-which(usemember%in%selects)
a<-length(intraining)/length(usemember)
b<-sum(as.numeric(unlist(final_list[1])[intraining]))/length(unlist(final_list[1])[intraining])
}
inTraining_sep<-intraining
|
91bda5082d8724b25f8049415845500ea23afa50
|
0886d094611c5e514a3366482ae2238a7b7a3e4b
|
/man/pmwright1.Rd
|
ac0f4ec7203f5abf08e717b6f4eecda08e3cc2ba
|
[] |
no_license
|
cran/MWright
|
8ac3118ce26d91ded51c14f1ba709b2e28711e12
|
828384ab72439cdbc6bf53d1385ea72fb59204dc
|
refs/heads/master
| 2020-06-30T00:49:44.347860
| 2019-08-07T22:00:05
| 2019-08-07T22:00:05
| 200,671,449
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,574
|
rd
|
pmwright1.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distn1side.R
\name{pmwright1}
\alias{pmwright1}
\title{Distribution function for one-sided M-Wright distribution}
\usage{
pmwright1(alp, sc, upper)
}
\arguments{
\item{alp}{point estimate for shape parameter alpha.}
\item{sc}{point estimate for scale parameter s.}
\item{upper}{non-negative upper quantile}
}
\value{
numeric
}
\description{
Calculates a left-tail probability.
}
\examples{
pmwright1(runif(1), runif(1,0,10),Inf )
pmwright1(runif(1), runif(1,0,10), 0.5 )
}
\references{
Cahoy and Minkabo (2017). \emph{Inference for three-parameter M-Wright distributions with applications.} Model Assisted Statistics and Applications, 12(2), 115-125.
\url{https://doi.org/10.3233/MAS-170388}
Cahoy (2012). \emph{Moment estimators for the two-parameter M-Wright distribution.} Computational Statistics, 27(3), 487-497.
\url{https://doi.org/10.1007/s00180-011-0269-x}
Cahoy (2012). \emph{Estimation and simulation for the M-Wright function.} Communications in Statistics-Theory and Methods, 41(8), 1466-1477.
\url{https://doi.org/10.1080/03610926.2010.543299}
Cahoy (2011). \emph{On the parameterization of the M-Wright function.} Far East Journal of Theoretical Statistics, 34(2), 155-164.
\url{http://www.pphmj.com/abstract/5767.htm}
Mainardi, Mura, and Pagnini (2010). \emph{The M-Wright Function in Time-Fractional Diffusion Processes: A Tutorial Survey}. Int. J. Differ. Equ., Volume 2010.
\url{https://doi.org/10.1155/2010/104505}
}
|
b1a7a9887c90a42bc8f152513629c95ed21703b5
|
1c74d653f86b446a9cd87435ce3920977e2cb109
|
/packages/av/test.R
|
338ef472ee47953b7503858c788e553ce5b0966a
|
[
"Apache-2.0"
] |
permissive
|
rstudio/shinyapps-package-dependencies
|
f1742d5cddf267d06bb895f97169eb29243edf44
|
8d73ce05438f49368b887de7ae00ff9d2681df38
|
refs/heads/master
| 2023-07-22T08:53:56.108670
| 2023-07-12T13:58:58
| 2023-07-12T13:58:58
| 22,746,486
| 81
| 76
|
NOASSERTION
| 2023-07-12T13:59:00
| 2014-08-08T04:57:26
|
R
|
UTF-8
|
R
| false
| false
| 498
|
r
|
test.R
|
options(download.file.method="curl")
install.packages("av", repos="https://cran.rstudio.com")
# from av_demo
output = tempfile(fileext = ".mp4")
av::av_demo(output = output)
stopifnot(file.exists(output))
output = tempfile(fileext = ".mkv")
av::av_demo(output = output)
stopifnot(file.exists(output))
output = tempfile(fileext = ".mov")
av::av_demo(output = output)
stopifnot(file.exists(output))
output = tempfile(fileext = ".flv")
av::av_demo(output = output)
stopifnot(file.exists(output))
|
18881877321fe312d54ecc102f4551d41a56580e
|
229f163de91efd1d38909e1f4d24aac4741c92f6
|
/PRSmodels/lassosum.R
|
be9c6d8fe04e79d7972cc1fc5667a8836f5c9e68
|
[] |
no_license
|
daiqile96/OTTERS
|
531433cdaeb9e6dbe35eb1ba92977c55974154ae
|
8e0bb1f1c9c1065a05ecf9f88a53d76a828a76cd
|
refs/heads/main
| 2023-09-01T17:37:25.069311
| 2023-08-09T14:41:34
| 2023-08-09T14:41:34
| 468,945,830
| 14
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,470
|
r
|
lassosum.R
|
#!/usr/bin/env Rscript
###################################################################
# Import packages needed
library(data.table)
library(lassosum)
###############################################################
# parse input arguments
Sys.setlocale("LC_ALL", "C")
options(stringsAsFactors = F)
## Collect arguments
args <- commandArgs(TRUE)
## Default setting when no arguments passed
if(length(args) < 1) {
args <- c("--help")
}
## Parse arguments (we expect the form --arg=value)
parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
argsL <- as.list(as.character(argsDF$V2))
names(argsL) <- argsDF$V1
## Check mandatory parameters
if (is.null(argsL$chr)) {
cat('* Please specify the chromosome --chr\n')
q(save="no")
} else if(is.null(argsL$medianN)) {
cat('* Please specify the path to the median sample size file using --medianN_path\n')
q(save="no")
} else if (is.null(argsL$bim_file)) {
cat('* Please specify the directory to the reference PLINK file --bim_dir\n')
q(save="no")
} else if (is.null(argsL$sst_file)) {
cat('* Please specify the path to the summary statistics with standardized beta using --sst_dir\n')
q(save="no")
} else if (is.null(argsL$LDblocks)) {
cat('* Please specify the name of LDblocks --LDblocks\n')
q(save="no")
} else if (is.null(argsL$out_path)) {
cat('* Please specify the output path\n')
q(save="no")
}
## Check optional parameters and assign default values
if (is.null(argsL$n_thread)){
argsL$n_thread <- 1
}
print(argsL)
###############################################################
# time calculation
start_time <- Sys.time()
# Create the output file
gene_name=argsL$gene_name
chr = argsL$chr
# Specify the PLINK file of the reference panel
bfile <- argsL$bim_file
# Read the summary statistics of standardized beta in single variant test
# the standardized beta in single variant test = correlation
ss <- fread(argsL$sst_file)
cor <- ss$Beta
# lassosum only allow -1 < cor < 1
if (sum(abs(cor) >= 1) > 0){
shrink_factor = max(abs(cor)) / 0.9999
cor = cor / shrink_factor
}
ss$SNPPos <- sapply(1:length(ss$SNP), function(i) strsplit(ss$SNP[i], "_")[[1]][2])
ss$Chrom <- chr
# train lassosum
out <- lassosum.pipeline(cor=cor,
chr=as.numeric(ss$Chrom),
pos=as.numeric(ss$SNPPos),
A1=ss$A1,
A2=ss$A2, # A2 is not required but advised
s = c(0.2, 0.5, 0.9, 1),
lambda = exp(seq(log(0.0001), log(0.1), length.out = 20)),
ref.bfile = bfile, # The reference panel dataset
test.bfile = bfile, # We don't have test data here
LDblocks = argsL$LDblocks,
exclude.ambiguous = F,
destandardize = F,
trace = 0)
# perform pseudovalidation
v <- pseudovalidate(out)
lassosum_out <- subset(out, s=v$best.s, lambda=v$best.lambda)
# save estimated beta
sumstats = lassosum_out$sumstats[, c("chr", "pos", "A1", "A2")]
beta = unlist(lassosum_out$beta)
results = data.frame(sumstats, ES = beta)
results = results[, c("chr", "pos", "A1", "A2", "ES")]
write.table(results,
argsL$out_path,
quote = F,
row.names= F,
col.names= T,
sep = "\t",
append = F)
|
c7b7b8fdf40c2819a9862b84085b3f5065490ae7
|
4c14bcc37fa428673536b87083afb734866f947c
|
/man/series.Rd
|
aa5e156f41bd3b263686e7f1eb45d14f60bb8d13
|
[] |
no_license
|
RobinHankin/ResistorArray
|
9c06802cb867eb3c40014ae5552ae8b8420411d1
|
fe8588cc44b3c5afd91033efd768ce9846860087
|
refs/heads/master
| 2021-09-28T17:31:52.431138
| 2021-09-18T22:25:41
| 2021-09-18T22:25:41
| 168,077,182
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 985
|
rd
|
series.Rd
|
\name{series}
\alias{series}
\title{Conductance matrix for resistors in series}
\description{
Conductance matrix for resistors of arbitrary resistance in series
}
\usage{
series(x)
}
\arguments{
\item{x}{The resistances of the resistors.}
}
\details{
\strong{Note:} if \code{length(x)=n}, the function returns a
conductance matrix of size \code{n+1} by \code{n+1}, because \code{n}
resistors in series have \code{n+1} nodes to consider.
}
\author{Robin K. S. Hankin}
\seealso{\code{\link{cube}}}
\examples{
## Resistance of four resistors in series:
resistance(series(rep(1,5)),1,5) ##sic! FOUR resistors have FIVE nodes
## What current do we need to push into a circuit of five equal
## resistors in order to maintain the potentials at 1v, 2v, ..., 6v?
circuit(series(rep(1,5)),v=1:6) #(obvious, isn't it?)
## Now, what is the resistance matrix of four nodes connected in series
## with resistances 1,2,3 ohms?
Wu(series(1:3)) #Yup, obvious again.
}
\keyword{array}
|
0324b1faab7b06bba5491ad27f965f412e339b6f
|
7c6017497f50d6e068f4ad18d70c1acb119c391a
|
/cachematrix.R
|
f624dbd3d66412954f093fce55f0a2aa13613c01
|
[] |
no_license
|
johnfossella/ProgrammingAssignment2
|
f9af7a77993b85aa20ff2cded7493cbce2249bb8
|
296e196254e9102e3d5068cd31a18f51c48a5111
|
refs/heads/master
| 2021-01-16T23:02:03.406020
| 2015-09-21T23:02:52
| 2015-09-21T23:02:52
| 42,868,290
| 0
| 0
| null | 2015-09-21T13:37:13
| 2015-09-21T13:37:12
| null |
UTF-8
|
R
| false
| false
| 901
|
r
|
cachematrix.R
|
## For an invertable matrix this script creates
## a list of functions sets and gets the matrix
## and also sets and gets the inverse of the matrix.
## These are used by the cacheSolve script.
makeCacheMatrix <- function(x = matrix()) {
inv = NULL
set = function(y) {
x <<- y
inv <<- NULL
}
get = function() x
setinverse = function(inverse) inv <<- inverse
getinverse = function() inv
list(set=set, get=get, setinverse=setinverse, getinverse=getinverse)
}
## This script gets the output of makeCacheMatrix()
## and uses it to calculate the inverse.
## Then it sets the value of the inverse in the cache.
cacheSolve <- function(x, ...) {
inv = x$getinvers()
if (!is.null(inv)){
message("getting cached data")
return(inv)
}
matrixvalues = x$get()
inv = solve(matrixvalues, ...)
x$setinvers(inv)
return(inv)
}
|
374dad41da5ae7ab808091d4e44529211951f91a
|
3866452efa0b4bc18eb3e560106c6c4d7951f07c
|
/man/step.Rd
|
718f723789145c7f017a0943177ff56c65f597e0
|
[] |
no_license
|
cran/relax
|
39b419a84a1271e725aa01748fd5152fcd454212
|
9032feb8f608664c9fb145fd62ef11fa83fe998f
|
refs/heads/master
| 2020-04-14T23:23:20.277277
| 2014-03-10T00:00:00
| 2014-03-10T00:00:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 789
|
rd
|
step.Rd
|
\name{step}
\alias{step}
\title{ modified version of step for relax }
\description{Select a formula-based model by AIC.}
\usage{
step(object, scope, scale = 0, direction = c("both", "backward", "forward"),
trace = 1, keep = NULL, steps = 1000, k = 2, ...)
}
\arguments{
\item{object}{ model }
\item{scope}{ range of model }
\item{scale}{ used in the definiton of AIC statistic }
\item{direction}{ mode of search }
\item{trace}{ printing during running }
\item{keep}{ filter function }
\item{steps}{ max number of steps }
\item{k}{ multiple of number of d.f. for penalty }
\item{\dots}{ further arguments }
}
\details{ see help of step (package stats) }
\value{ stepwise-selected model is returned ... }
\seealso{ \code{\link{step}} }
\examples{ ## }
\keyword{ IO }
|
13990779e4ec34828f6c370b52fd6871aa7d8b90
|
03bf43d695db86fb8203e5186a8b3ce12d92d9aa
|
/tarea0/problema6.R
|
723c14e4c172a7c19ac38c18ae22027d8cfbccfb
|
[] |
no_license
|
joseaznar/simulacion
|
e01fc9bace916cb445641190c7583cda87d0d8f1
|
525484c4616fb8e0fde3483ca70a7f1f93dc1ee2
|
refs/heads/master
| 2021-09-06T06:02:24.358856
| 2018-02-03T00:40:24
| 2018-02-03T00:40:24
| 119,995,028
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 218
|
r
|
problema6.R
|
# problema 6
# primero definimos la ecuación
eq = function(x){exp(-x*x)/(1+x*x)}
# graficamos la función entre 0 y 10
curve(eq, from=0, to=10)
# ahora integramos entre 0 e infinito
integrate(eq, lower=0, upper=Inf)
|
8e1871d2afb46c6c782a6ddb153cf6bf2a992b46
|
5e1560b3a996ed2f56a74f32dc987a8e60e405f3
|
/R/tolerance.eigen.R
|
f0e12877f856cc4724c5124cde17d1c92211bd04
|
[] |
no_license
|
diogo-almeida/GSVD
|
19803dde129b18cb0492d3e211e037e33efb7d31
|
90cb90497daf1efd38e1a3ab85439407f16ff46e
|
refs/heads/master
| 2020-12-18T15:34:15.768461
| 2019-11-14T21:28:24
| 2019-11-14T21:28:24
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,999
|
r
|
tolerance.eigen.R
|
#' @export
#'
#' @title \code{tolerance.eigen}: An eigenvalue decomposition to truncate potentially spurious (near machine precision) components.
#'
#' @description \code{tolerance.eigen} eliminates likely spurious components: any eigenvalue (squared singular value) below a tolerance level is elminated.
#' The (likely) spurious eigen values and vectors are then eliminated from \code{$vectors} and \code{$values}.
#' The use of a real positive value for \code{tol} will eliminate any small valued components.
#' With \code{tol}, \code{tolerance.eigen} will stop if any singular values are complex or negative.
#'
#' @param x A data matrix of size for input to the eigen value decomposition (\code{\link{eigen}})
#' @param tol Default is \code{sqrt(.Machine$double.eps)}. A tolerance level for eliminating near machine precision components.
#' Use of this parameter causes \code{tolerance.eigen} to stop if negative or complex eigen values are detected.
#' The use of \code{tol < 0}, \code{NA}, \code{NaN}, \code{Inf}, \code{-Inf}, or \code{NULL} passes through to \code{\link{eigen}}.
#' @param ... Further arguments to \code{\link{eigen}}. See \code{\link{eigen}}.
#'
#' @return A list with two elements (like \code{eigen}):
#' \item{values}{ A vector containing the eigen values of x > \code{tol}.}
#' \item{vectors}{ A matrix whose columns contain the right singular vectors of x, present if nv > 0. Dimension \code{min(c(ncol(x), nv, length(d))}.}
#'
#' @seealso \code{\link{eigen}}
#'
#' @author Derek Beaton
#' @keywords multivariate, diagonalization, eigen
tolerance.eigen <- function(x, tol = sqrt(.Machine$double.eps), ...) {
eigen_res <- eigen(x, ...)
# if tolerance is any of these values, just do nothing; send back the EVD results as is.
if( (is.null(tol) | is.infinite(tol) | is.na(tol) | is.nan(tol) | tol < 0) ){
return(eigen_res)
}
## once you go past this point you *want* the tolerance features.
if(any(unlist(lapply(eigen_res$values,is.complex)))){
stop("tolerance.eigen: eigen values ($values) are complex.")
}
# if( (any(abs(eigen_res$values) > tol) ) & (any(sign(eigen_res$values) != 1)) ){
# if( (any(abs(eigen_res$values) < tol) ) ){
if( any( (abs(eigen_res$values) > tol) & (sign(eigen_res$values)==-1) ) ){
stop("tolerance.eigen: eigen values ($values) are negative with a magnitude above 'tol'.")
}
evs.to.keep <- which(!(eigen_res$values < tol))
if(length(evs.to.keep)==0){
stop("tolerance.eigen: All eigen values were below 'tol'")
}
eigen_res$values <- eigen_res$values[evs.to.keep]
## this would happen if only.values=TRUE
if(!is.null(eigen_res$vectors)){
eigen_res$vectors <- eigen_res$vectors[,evs.to.keep]
rownames(eigen_res$vectors) <- colnames(x)
## force consistent directions as best as possible:
if( sign(eigen_res$vectors[1]) == -1 ){
eigen_res$vectors <- eigen_res$vectors * -1
}
}
class(eigen_res) <- c("list", "GSVD", "eigen")
return(eigen_res)
}
|
b75f1a115ed69306fd9141173a7784bb4a50912d
|
d80d2f9e911820898bb21bc9e2e2c7d10e8cfa59
|
/R/prompt-git.R
|
918088dae97a9c6187ed55b6c2a0ceb45ec03a38
|
[] |
no_license
|
Robinlovelace/prompt
|
d0d8f43d4459f2d8e67ed433e55cdf88ff28d07a
|
950124035700126412df8f5bc78cb583ee0555f6
|
refs/heads/master
| 2020-04-09T22:37:33.908096
| 2018-09-11T21:50:52
| 2018-09-11T21:51:44
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,681
|
r
|
prompt-git.R
|
#' An example 'git' prompt
#'
#' It shows the current branch, whether there are
#' commits to push or pull to the default remote,
#' and whether the working directory is dirty.
#'
#' @param ... Unused.
#'
#' @family example prompts
#' @export
#' @examples
#' \dontrun{
#' set_prompt(prompt_git)
#' }
prompt_git <- function(...) {
if (!is_git_dir()) return ("> ")
paste0(
git_branch(),
git_dirty(),
git_arrows(),
" > "
)
}
is_git_dir <- function() {
status <- git("status")
attr(status, "status") == 0
}
## It fails before the first commit, so we just return "master" there
git_branch <- function() {
status <- git("rev-parse --abbrev-ref HEAD")
if (attr(status, "status") != 0) "master" else status
}
#' @importFrom clisymbols symbol
git_arrows <- function() {
res <- ""
status <- git("rev-parse --abbrev-ref @'{u}'")
if (attr(status, "status") != 0) return(res)
status <- git("rev-list --left-right --count HEAD...@'{u}'")
if (attr(status, "status") != 0) return (res)
lr <- scan(text = status, quiet = TRUE)
if (lr[2] != 0) res <- paste0(res, symbol$arrow_down)
if (lr[1] != 0) res <- paste0(res, symbol$arrow_up)
if (res != "") paste0(" ", res) else res
}
git_dirty <- function() {
status <- git("diff --no-ext-diff --quiet --exit-code")
if (attr(status, "status") != 0) "*" else ""
}
git <- function(args, quiet = TRUE, path = ".") {
full <- paste0(shQuote(check_git_path()), " ", paste(args, collapse = ""))
if (!quiet) {
message(full)
}
result <- tryCatch(
suppressWarnings(
in_dir(path, system(full, intern = TRUE, ignore.stderr = quiet))
),
error = function(x) x
)
if (methods::is(result, "error")) {
result <- structure("", status = 1)
} else {
attr(result, "status") <- attr(result, "status") %||% 0
}
result
}
git_path <- function(git_binary_name = NULL) {
# Use user supplied path
if (!is.null(git_binary_name)) {
if (!file.exists(git_binary_name)) {
stop("Path ", git_binary_name, " does not exist", .call = FALSE)
}
return(git_binary_name)
}
# Look on path
git_path <- Sys.which("git")[[1]]
if (git_path != "") return(git_path)
# On Windows, look in common locations
if (os_type() == "windows") {
look_in <- c(
"C:/Program Files/Git/bin/git.exe",
"C:/Program Files (x86)/Git/bin/git.exe"
)
found <- file.exists(look_in)
if (any(found)) return(look_in[found][1])
}
NULL
}
check_git_path <- function(git_binary_name = NULL) {
path <- git_path(git_binary_name)
if (is.null(path)) {
stop("Git does not seem to be installed on your system.", call. = FALSE)
}
path
}
|
c8708248a3c9286ee6acb1d41a9556c777fbc3f2
|
109734b597c2d760725a1a050174a5d11b3c1a9b
|
/man/diameter.owin.Rd
|
cad0b6deaad18918e2a9ccb4160dd51a0d1680b0
|
[] |
no_license
|
rubak/spatstat
|
c293e16b17cfeba3e1a24cd971b313c47ad89906
|
93e54a8fd8276c9a17123466638c271a8690d12c
|
refs/heads/master
| 2020-12-07T00:54:32.178710
| 2020-11-06T22:51:20
| 2020-11-06T22:51:20
| 44,497,738
| 2
| 0
| null | 2020-11-06T22:51:21
| 2015-10-18T21:40:26
|
R
|
UTF-8
|
R
| false
| false
| 1,076
|
rd
|
diameter.owin.Rd
|
\name{diameter.owin}
\alias{diameter.owin}
\title{Diameter of a Window}
\description{
Computes the diameter of a window.
}
\usage{
\method{diameter}{owin}(x)
}
\arguments{
\item{x}{
A window whose diameter will be computed.
}
}
\value{
The numerical value of the diameter of the window.
}
\details{
This function computes the
diameter of a window of arbitrary shape,
i.e. the maximum distance between any two points
in the window.
The argument \code{x} should be a window (an object of class
\code{"owin"}, see \code{\link{owin.object}} for details)
or can be given in any format acceptable to \code{\link{as.owin}()}.
The function \code{diameter} is generic. This function is the
method for the class \code{"owin"}.
}
\seealso{
\code{\link{area.owin}},
\code{\link{perimeter}},
\code{\link{edges}},
\code{\link{owin}},
\code{\link{as.owin}}
}
\examples{
w <- owin(c(0,1),c(0,1))
diameter(w)
# returns sqrt(2)
data(letterR)
diameter(letterR)
}
\author{\adrian
and \rolf
}
\keyword{spatial}
\keyword{math}
|
e0a49b14aeaa0b04e94e3dadfd813d0d10d0543c
|
03d20ec52ea429d2bffdefa849044ab6d0ad7481
|
/03_stop_frisk/scripts/shiny/server.R
|
53321fcac699cc40920f5e00eae34ce5662d3954
|
[] |
no_license
|
GWarrenn/dc_data
|
3f679b28aa02f1cec7b9e887d66087d44ed40d7c
|
15b358d77210644dcdd908ef05d6e95930fbf62e
|
refs/heads/master
| 2021-11-17T07:36:55.337352
| 2021-09-30T21:44:56
| 2021-09-30T21:44:56
| 98,127,130
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,263
|
r
|
server.R
|
library(shiny)
library(DT)
shinyServer(function(input, output) {
filedata <- read.csv("sf_nbh_summary.csv")
format_cols <- c("Black.Diff","Hispanic.Latino.Diff","Juvenile.Diff","White.Diff")
numeric_cols <- c("Black.stop_and_frisk","Black.census","Black.Diff",
"Hispanic.Latino.stop_and_frisk","Hispanic.Latino.census","Hispanic.Latino.Diff",
"Juvenile.stop_and_frisk","Juvenile.census","Juvenile.Diff","White.stop_and_frisk",
"White.census","White.Diff")
output$tbl = renderDT(
datatable(filedata,rownames = FALSE, extensions ="FixedColumns",options = list(
scrollX=TRUE,
scrollY=500,
fixedColumns = list(leftColumns = 2),
autoWidth = TRUE,
columnDefs = list(list(width = '250px', targets = c(1)),
list(className = 'dt-center', targets = 0:13),
list(visible=FALSE, targets=c(0))))) %>%
formatStyle(format_cols,
backgroundColor = styleInterval(0, c('lightpink', 'lightgreen'))) %>%
#formatStyle("neighborhood","white-space"="nowrap") %>%
#formatStyle(columns = c(2), width='200%') %>%
formatPercentage(numeric_cols, 1)
)
})
# columnDefs = list(list(visible=FALSE, targets=c(4)
#
|
1a21e4fca73fddb89742615632cb67512929cca6
|
9d4e1ec7dd4128c99360e98b05de206661f3f130
|
/stoke_boost.R
|
d76d0f0fcffad9ffcdbfb1b7483876242c17740e
|
[] |
no_license
|
coderjones/stroke_prediction
|
df6afb8fc5681ea46e050a22a07456b6445cb89e
|
be5a1dbea2436c068b313dbf39d471e8df84591e
|
refs/heads/main
| 2023-06-08T05:17:47.580765
| 2021-06-27T20:07:01
| 2021-06-27T20:07:01
| 367,732,010
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,059
|
r
|
stoke_boost.R
|
# Using gbm for gradient boosting classification
library(tidyverse)
library(fastDummies)
library(rsample)
library(gbm)
# set working directory
setwd("/Users/jeremiahhamilton/code/stroke_prediction")
# read in data
df <- read.csv("healthcare-dataset-stroke-data.csv")
df <- dummy_cols(df, select_columns = c('smoking_status', 'ever_married','work_type', 'Residence_type', 'gender'))
df %>% select(-ever_married, -work_type, -Residence_type, -gender, -smoking_status, -id, -avg_glucose_level) -> df
# split into train/test groups
set.seed(22)
df_split <- initial_split(df, prop = .7, strata = "stroke")
train_df <- training(df_split)
test_df <- testing(df_split)
stroke_gbm1 <- gbm(
formula = stroke ~ .,
data = train_df,
distribution = "gaussian",
n.trees = 5000,
shrinkage = 0.1,
interaction.depth = 3,
n.minobsinnode = 10,
cv.folds = 10
)
# find index fornumber trees with minimum cv error
best <- which.min(stroke_gbm1$cv.error)
#get RMSE
sqrt(stroke_gbm1$cv.error[best])
# plot error curve
gbm.perf(stroke_gbm1, method = "cv")
|
9a9a6b4f313264e2921ba1a55a9e113ffc3df5ed
|
22da09a9095cbd13d25edff454c1b32972357ffc
|
/man/animate_series.Rd
|
152d9861ff8020765cc6fd1c31658782afaab7a4
|
[] |
no_license
|
ThoDah/rabbiTS-1
|
f49f516b72f4805b5d7ddb7899eb412ff6757e3d
|
ef7427619aeb5b6b174751dc1067b2cfe6d3d6f4
|
refs/heads/master
| 2020-03-17T18:03:51.299496
| 2018-05-17T13:02:27
| 2018-05-17T13:02:27
| 133,813,294
| 0
| 0
| null | 2018-05-17T12:55:20
| 2018-05-17T12:55:19
| null |
UTF-8
|
R
| false
| true
| 1,246
|
rd
|
animate_series.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/animate_series.R
\name{animate_series}
\alias{animate_series}
\title{Animate a time series of rasters}
\usage{
animate_series(r, dates, breaks, param = NULL, param.name = "Parameter",
file.name = tempfile(fileext = ".gif"), ...)
}
\arguments{
\item{r}{raster or stack of rasters, each representing a different acquisition time.}
\item{dates}{character vector of length \code{nlayers(r)}, representing corresponding times of \code{r}.}
\item{breaks}{numeric vector, value range to be displayed, e.g. \code{seq(1, 180, by = 1)}}
\item{param}{data.frame or \code{NULL}, optional parameter data.frame derived using \link{bands_param} to show the development of a paramater over time.}
\item{param.name}{character, name of the defined paramater. Default is "Parameter".}
\item{file.name}{character, path to the output file Default is a temporary file.}
\item{...}{arguments passed to \link{saveGIF}, e.g. interval=0.2, ani.width = 500, ani.height = 700 etc.}
}
\value{
List of plots that are used as frames for the animation. An animation file will be written to file.name.
}
\description{
\code{animate_series} animates a series of rasters in consecutive order.
}
|
a28b6d45d9b576b7d7fd1b692ab7c6579bce7ddf
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/rnr/tests/test-solve.R
|
7991f85e7dbd1be1bb33531a853d989d70bf0c16
|
[] |
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
| 640
|
r
|
test-solve.R
|
context("sensitize")
test_that("solve_closed() finds correct value for atomic vector", {
ps <- seq(0, 1, 0.01)
delta <- 2
theta <- 1
for (p in ps) {
lhs <- 1 - (p*inv_logit(theta) + (1 - p)*inv_logit(theta + delta))
generated <- solve_closed(p, delta, lhs)
expect_equal(generated, theta)
}
})
test_that("solve() finds correct value for vector of values", {
ps <- seq(0, 1, 0.01)
delta <- 2
theta <- (ps - mean(ps))*5 # Just some reasonable value should do
lhs <- 1 - (ps*inv_logit(theta) + (1 - ps)*inv_logit(theta + delta))
generated <- solve_closed(ps, delta, lhs)
expect_equal(generated, theta)
})
|
41ec86a3d14cb16a89dd7b1ca80144522889526f
|
e8524f6a0301d922ec18d6d017cfb223c9eceee4
|
/data-portraits/andy-challenge-02.R
|
fb515bf21f170f303fc060dbaf3c245ba255fd24
|
[] |
no_license
|
melodyaltschuler/tidytuesday
|
eff7713ff1cc36a9ea382954159e9f315f0c4691
|
6514ec95de2cc366be1b340050218819430963c4
|
refs/heads/master
| 2023-04-12T22:38:52.281026
| 2021-04-22T22:42:03
| 2021-04-22T22:42:03
| 296,454,761
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,524
|
r
|
andy-challenge-02.R
|
## DATA PORTRAITS - CHALLENGE 2
## ICD TIDY TUESDAY
## MARCH 2021
# Load library
library(tidyverse)
library(showtext) #To use googlefonts
library(patchwork)
# Import data
conjugal <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-16/conjugal.csv')
# Convert to long format
conjugal = conjugal %>%
pivot_longer(3:5, names_to = "Status", values_to = "Percentage") %>%
# Re-order factor levels to correspond to plot
mutate(
Population = factor(Population, levels = c("Negroes", "Germany"))
) %>%
# Compute x-coordinates for text in bars
group_by(Population, Age) %>%
mutate(
half_perc = 0.5*Percentage,
x_coord = half_perc + lag(Percentage, 2, default = 0) + lag(Percentage, 1, default = 0),
perc_text = paste0(Percentage, "%")
)
## Loading Google fonts (https://fonts.google.com/)
font_add_google(name = "Cutive Mono")
## Automatically use showtext to render google fonts
showtext_auto()
# Create plot for 15-40 age group
p1 = conjugal %>%
filter(Age == "15-40") %>%
ggplot(aes(x = Percentage, y = Population, fill = Status)) +
geom_bar(stat = "identity", width = 0.75) +
geom_text(aes(x = x_coord, label = perc_text), size = 2) +
theme_light() +
scale_x_continuous(name = "", breaks = NULL) +
scale_y_discrete(name = "") +
scale_fill_manual(name = "", values = c("#707a6a", "#e6b329", "#be324b")) +
ggtitle("CONJUGAL CONDITION") +
theme(
legend.position = "top",
legend.background = element_rect(fill = "#dfd2c1"),
legend.key = element_rect(fill = "#dfd2c1"),
plot.background = element_rect(fill = "#dfd2c1"),
panel.background = element_rect(fill = "#dfd2c1"),
panel.grid = element_blank(),
panel.border = element_blank(),
axis.text = element_text(family = "Cutive Mono"),
legend.text = element_text(family = "Cutive Mono"),
plot.title = element_text(hjust = 0.5, family = "Cutive Mono", face = "bold", size = 14),
plot.margin = margin(15, 0, 0, 50, "pt"), #trbl
strip.background = element_blank(),
strip.text = element_blank(),
aspect.ratio = .1
) +
coord_cartesian(xlim = c(0, 100), clip = "off") +
annotate("text", x = -26, y = "Germany", label = c("Age\n15-40"), vjust = 1, family = "Cutive Mono", size = 3) +
annotate("text", x = -20, y = "Germany", label = "{", vjust = 0.9, family = "Cutive Mono", size = 12, alpha = 0.6)
# Create plot for 40-60 age group
p2 = conjugal %>%
filter(Age == "40-60") %>%
ggplot(aes(x = Percentage, y = Population, fill = Status)) +
geom_bar(stat = "identity", width = 0.75) +
geom_text(aes(x = x_coord, label = perc_text), size = 2) +
theme_light() +
scale_x_continuous(name = "", breaks = NULL) +
scale_y_discrete(name = "") +
scale_fill_manual(name = "", values = c("#707a6a", "#e6b329", "#be324b")) +
theme(
legend.position = "top",
legend.background = element_rect(fill = "#dfd2c1"),
legend.key = element_rect(fill = "#dfd2c1"),
plot.background = element_rect(fill = "#dfd2c1"),
panel.background = element_rect(fill = "#dfd2c1"),
panel.grid = element_blank(),
panel.border = element_blank(),
axis.text = element_text(family = "Cutive Mono"),
legend.text = element_text(family = "Cutive Mono"),
plot.title = element_text(hjust = 0.5, family = "Cutive Mono", face = "bold", size = 14),
plot.margin = margin(15, 0, 0, 50, "pt"), #trbl
strip.background = element_blank(),
strip.text = element_blank(),
aspect.ratio = .1
) +
guides(fill = FALSE) +
coord_cartesian(xlim = c(0, 100), clip = "off") +
annotate("text", x = -26, y = "Germany", label = c("\n40-60"), vjust = 0.6, family = "Cutive Mono", size = 3) +
annotate("text", x = -20, y = "Germany", label = "{", vjust = 0.9, family = "Cutive Mono", size = 12, alpha = 0.6)
# Create plot for 60 and over age group
p3 = conjugal %>%
filter(Age == "60 and over") %>%
ggplot(aes(x = Percentage, y = Population, fill = Status)) +
geom_bar(stat = "identity", width = 0.75) +
geom_text(aes(x = x_coord, label = perc_text), size = 2) +
theme_light() +
scale_x_continuous(name = "", breaks = NULL) +
scale_y_discrete(name = "") +
scale_fill_manual(name = "", values = c("#707a6a", "#e6b329", "#be324b")) +
theme(
legend.position = "top",
legend.background = element_rect(fill = "#dfd2c1"),
legend.key = element_rect(fill = "#dfd2c1"),
plot.background = element_rect(fill = "#dfd2c1"),
panel.background = element_rect(fill = "#dfd2c1"),
panel.grid = element_blank(),
panel.border = element_blank(),
axis.text = element_text(family = "Cutive Mono"),
legend.text = element_text(family = "Cutive Mono"),
plot.title = element_text(hjust = 0.5, family = "Cutive Mono", face = "bold", size = 14),
plot.margin = margin(15, 0, 0, 50, "pt"), #trbl
strip.background = element_blank(),
strip.text = element_blank(),
aspect.ratio = .1
) +
guides(fill = FALSE) +
coord_cartesian(xlim = c(0, 100), clip = "off") +
annotate("text", x = -26, y = "Germany", label = c("60\nAND\nOVER"), vjust = 0.8, family = "Cutive Mono", size = 3) +
annotate("text", x = -20, y = "Germany", label = "{", vjust = 0.9, family = "Cutive Mono", size = 12, alpha = 0.6)
# Layout plots and fill in background between plots
p4 = p1 /p2 / p3 & theme(plot.background = element_rect(fill = "#dfd2c1", color = "#dfd2c1"))
# Output the plot
ggsave(p4, filename = "~/Desktop/challenge_02.png", width = 12, height = 3.7)
|
7a3dbdf4368c303fb0cafbd899712af6cf5ca0d4
|
3530fb409502ac4e55bfcf053daadf14573e5b08
|
/q14.R
|
ad2626bf18668adc582fa78232b0bf762f8c8ec9
|
[] |
no_license
|
nathandarmawan/rprog_quiz_week1
|
fa0ee5b46ea0ceb56f808739aacdb9eb095f2db6
|
2f239f96a8cdd683b5f9df23b7adb7998670b98d
|
refs/heads/master
| 2021-01-19T05:36:32.364798
| 2014-10-13T06:36:16
| 2014-10-13T06:36:16
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 277
|
r
|
q14.R
|
## Q 14
## Extract the last 2 rows of the data frame
## and print them to the console.
## What does the output look like?
## Reading Data
setwd("D:/GitHub/rprog_quiz_week1")
data <- read.csv("hw1_data.csv")
## Show the last 2 rows of the data frame
## Use tail()
tail(data,2)
|
3e397af706756bbde6d44b2845e1aed5af54847c
|
4a4cae45a127183fa4c58bd75737fb0980bd8bfd
|
/required_packages.R
|
91af782f392747c0430f8a9eeb697b1c7a3121c1
|
[
"MIT"
] |
permissive
|
klintkanopka/nn_workshop
|
7394a97a80cddb9a5a91e73e754f931b46884fde
|
bd2d857af0ebc98b83bebff1b266da6a6082cec0
|
refs/heads/master
| 2020-05-23T10:23:38.903759
| 2019-05-15T06:43:03
| 2019-05-15T06:43:03
| 186,719,123
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 59
|
r
|
required_packages.R
|
install.packages("tidyverse")
install.packages("neuralnet")
|
ec74b4bff2900a44e9be9a2956db480c1f4fa0de
|
5395cdc191ff5a30d1c59e68ca0f95a288892c8b
|
/man/M_el_mat.Rd
|
a5090a55249ad8698b59c2e7044182532cd445bc
|
[] |
no_license
|
nielsjdewinter/ShellTrace
|
fe16bb69b8981211bd24ef120627fc38d283db66
|
34dd076d72bb0812f251c986b1aad04b6849261b
|
refs/heads/master
| 2021-07-23T13:37:20.750368
| 2017-11-02T08:57:26
| 2017-11-02T08:57:26
| 105,881,428
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,852
|
rd
|
M_el_mat.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/M_el_mat.R
\docType{data}
\name{M_el_mat}
\alias{M_el_mat}
\title{Matrix of modelled mass accumulation rates per trace element}
\format{A data frame with 5 rows and 24 variables:
\describe{
\item{C}{Mass accumulation of C in subincrement}
\item{O}{Mass accumulation of O in subincrement}
\item{Na}{Mass accumulation of Na in subincrement}
\item{Mg}{Mass accumulation of Mg in subincrement}
\item{Al}{Mass accumulation of Al in subincrement}
\item{Si}{Mass accumulation of Si in subincrement}
\item{P}{Mass accumulation of P in subincrement}
\item{S}{Mass accumulation of S in subincrement}
\item{Cl}{Mass accumulation of Cl in subincrement}
\item{K}{Mass accumulation of K in subincrement}
\item{Ca}{Mass accumulation of Ca in subincrement}
\item{Ti}{Mass accumulation of Ti in subincrement}
\item{Cr}{Mass accumulation of Cr in subincrement}
\item{Mn}{Mass accumulation of Mn in subincrement}
\item{Fe}{Mass accumulation of Fe in subincrement}
\item{Ni}{Mass accumulation of Ni in subincrement}
\item{Cu}{Mass accumulation of Cu in subincrement}
\item{Zn}{Mass accumulation of Zn in subincrement}
\item{Br}{Mass accumulation of Br in subincrement}
\item{Rb}{Mass accumulation of Rb in subincrement}
\item{Sr}{Mass accumulation of Sr in subincrement}
\item{Rh}{Mass accumulation of Rh in subincrement}
\item{Ba}{Mass accumulation of Ba in subincrement}
\item{Pb}{Mass accumulation of Pb in subincrement}
}}
\source{
\url{https://doi.org/10.5194/gmd-2017-137-supplement}
}
\usage{
data(M_el_mat)
}
\description{
A dataset containing trace element accumulation modelled for every
based on the a phase map of the XRF mapped surface of the Crassostrea
gigas #1 oyster used as an example in de Winter (2017)
}
\keyword{datasets}
|
14990f5ce2aee8d498ee1d7e83b7972396f7a8be
|
caa9387f050ded3c5f1b9879eb1935a29f7db8ce
|
/code.R
|
b5ead6e513166d9c633d1f8d1dc107ec8d13ef4f
|
[] |
no_license
|
joebrew/map_plos
|
b4a3a901d7d1a4bc62aabbc75b2bca1c5d1f49b2
|
b1380ae0eb1efbda6a162396f4f3f21b2c195187
|
refs/heads/master
| 2021-01-10T09:54:17.699613
| 2016-03-08T10:40:53
| 2016-03-08T10:40:53
| 52,874,937
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,688
|
r
|
code.R
|
library(rgdal)
library(dplyr)
library(raster)
library(readxl)
library(RColorBrewer)
require(maptools)
library(ggrepel) # for avoiding overlapping labels in ggplot2
library(ggthemes)
##### Read in shapefiles
# arruamento <- readOGR('data/spatial/', 'arruamento')
bairros_e_zonas <- readOGR('data/spatial/', 'Bairros_e_Zonas')
# zonas_administrativas <- readOGR('data/spatial/', 'zonas_administrativas')
# For shape files we don't have, get spatial data from raster package
brazil0 <- getData('GADM', country = 'BRA', level = 0)
brazil1 <- getData('GADM', country = 'BRA', level = 1)
brazil2 <- getData('GADM', country = 'BRA', level = 2)
brazil3 <- getData('GADM', country = 'BRA', level = 3)
# save.image('~/Desktop/brazil.RData')
##### Read in data
# Counts by bairro
mulheres <- read_excel('data/spreadsheets/ECO.xls')
# Raw data
raw <- read_excel('data/spreadsheets/aaobserva.xls')
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
##### Figure 1 - bairros
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
##### Join data to spatial
# Define which names are good (ie, in map)
goods <- as.character(sort(unique(bairros_e_zonas@data$NOME)))
# Define which names don't need changing
ins <- as.character(sort(unique(mulheres$NOME))) %in%
as.character(sort(unique(bairros_e_zonas@data$NOME)))
# Define which names need changing
outs <- as.character(sort(unique(mulheres$NOME))[!ins])
# Define corrections
corrections <-
data.frame(mulheres = as.character(sort(unique(mulheres$NOME))),
stringsAsFactors = FALSE)
corrections$bairros <- ifelse(corrections$mulheres %in% goods,
corrections$mulheres,
NA)
# Make corrections
corrections$bairros[corrections$mulheres == 'COLÔNIA TERRA NOVA'] <-
'COL TERRA NOVA'
corrections$bairros[corrections$mulheres == 'LIRIO DO VALE'] <-
'LÍRIO DO VALE'
corrections$bairros[corrections$mulheres == 'NOSSA SENHORA DAS GRAÇAS'] <-
'N SRA DAS GRAÇAS'
corrections$bairros[corrections$mulheres == 'PARQUE 10 DE NOVEMBRO'] <-
'PARQUE DEZ DE NOVEMBRO'
corrections$bairros[corrections$mulheres == 'TANCREDO NEVES '] <-
'TANCREDO NEVES'
corrections$bairros[corrections$mulheres == 'ZUMBI'] <-
'ZUMBI DOS PALMARES'
# THE FOLLOWING WE ARE JUST TAKING OUT
# corrections$bairros[corrections$mulheres == 'NOVO ALEIXO'] <-
# 'ALEIXO'
# corrections$bairros[corrections$mulheres == 'COLONIA ANTONIO ALEIXO'] <-
# 'ALEIXO'
# corrections$bairros[corrections$mulheres == 'CAMPOS SALES'] <-
# 'SANTA ETELVINA'
# corrections$bairros[corrections$mulheres == 'CIDADE DE DEUS'] <-
# 'CIDADE NOVA'
# corrections$bairros[corrections$mulheres == 'LAGO AZUL'] <-
# 'TARUMÃ'
# corrections$bairros[corrections$mulheres == 'PARQUE DAS LARANJEIRAS'] <-
# 'FLORES'
# Implement the corrections
names(corrections) <- c('NOME', 'new_name')
mulheres <- left_join(mulheres, corrections, by = 'NOME')
mulheres$NOME <- mulheres$new_name; mulheres$new_name <- NULL
# Make the join
bairros_e_zonas@data <-
left_join(x = bairros_e_zonas@data,
y = mulheres,
by = 'NOME')
# Set to 0 the NAs
bairros_e_zonas@data$MULHERES[is.na(bairros_e_zonas@data$MULHERES)] <- 0
# Define a color vector
# cols <- rev(brewer.pal(n = 9, name = 'Spectral'))
cols <- brewer.pal(n = 9, name = 'Greens')
colors <- colorRampPalette(cols)(max(bairros_e_zonas@data$MULHERES, na.rm = TRUE))
colors <- adjustcolor(colors, alpha.f = 0.6)
bairros_e_zonas@data$color <- 'white'
# bairros_e_zonas@data$color[bairros_e_zonas@data$MULHERES > 0] <-
# colors[bairros_e_zonas@data$MULHERES[bairros_e_zonas@data$MULHERES > 0]]
#
# bairros_e_zonas@data$color <- ifelse(
# bairros_e_zonas@data$MULHERES == 0,
# 'white',
# ifelse(bairros_e_zonas@data$MULHERES > 0,
# colors[bairros_e_zonas@data$MULHERES],
# 'orange'))
for (i in 1:nrow(bairros_e_zonas@data)){
if(bairros_e_zonas@data$MULHERES[i] > 0){
bairros_e_zonas@data$color[i] <-
# colors[bairros_e_zonas@data$MULHERES[i]]
colors[bairros_e_zonas@data$MULHERES[i]]
}
}
# # PLOT GG STYLE
#
# # # fortify map
# bairros_e_zonas@data$place_id <- row.names(bairros_e_zonas@data)
# row.names(bairros_e_zonas@data) <- NULL
# bairros_e_zonas_f <- fortify(bairros_e_zonas, region = 'place_id')
# # bring in number of women
#
# bairros_e_zonas_f <- left_join(bairros_e_zonas_f,
# bairros_e_zonas@data %>%
# mutate(OBJECTID = as.character(OBJECTID)),
# by = c('id' = 'OBJECTID'))
#
# Create a labeling dataframe
label_df <- bairros_e_zonas@data[,c('NOME', 'MULHERES')]
label_df <- label_df[!duplicated(label_df$NOME),]
# add lat long
label_df$long <- coordinates(bairros_e_zonas)[,1]
label_df$lat <- coordinates(bairros_e_zonas)[,2]
# Keep only those with > 0 women
label_df <- label_df[label_df$MULHERES > 0,]
# Replace spaces with line breaks
label_df$NOME <- gsub(' ', '\n', label_df$NOME)
#
#
# ggplot() +
# coord_map() +
# geom_polygon(data = bairros_e_zonas_f,
# aes(x = long, y =lat, group = group,
# fill = MULHERES), color = 'grey') +
# geom_label_repel(data = label_df,
# aes(long, lat,
# #fill = factor(NOME),
# label = factor(NOME)),
# fontface = 'bold',
# color = 'black',
# size = 1.5,
# box.padding = unit(1.75, 'lines')) +
# theme_tufte() +
# theme(axis.ticks.length = unit(0.001, "mm")) + labs(x=NULL, y=NULL) +
# theme(axis.line=element_blank(),
# axis.text.x=element_blank(),
# axis.text.y=element_blank(),
# axis.ticks=element_blank(),
# axis.title.x=element_blank(),
# axis.title.y=element_blank(),
# legend.position="none",
# panel.background=element_blank(),
# panel.border=element_blank(),
# panel.grid.major=element_blank(),
# panel.grid.minor=element_blank(),
# plot.background=element_blank()) +
# scale_fill_manual(guide = guide_legend(title = 'Area'),
# values = cols)
# Add dataframe for accessory labeling
acc <- bairros_e_zonas@data
acc$lng <- coordinates(bairros_e_zonas)[,1]
acc$lat <- coordinates(bairros_e_zonas)[,2]
# Keep only those areas that have hospitals
acc <- acc[acc$NOME %in% c('JORGE TEIXEIRA', 'DOM PEDRO'),]
acc$label <- c('Jap Clinic', 'FMT HVD')
# Give them special colors / symbols
acc$color <- adjustcolor(c('blue', 'red'), alpha.f = 0.6)
acc$pch <- c(15, 17)
# acc <-
# data.frame(label = c('Jap Clinic', 'FMT-HVD'),
# lng = as.numeric(coordinates(bairros_e_zonas[bairros_e_zonas@data$NOME == 'JORGE TEIXEIRA',])[,1]),
# lat = as.numeric(coordinates(bairros_e_zonas[bairros_e_zonas@data$NOME == 'JORGE TEIXEIRA',])[,2]))
# Plot
pdf('figure_1.pdf', width = 10, height = 8)
plot(bairros_e_zonas,
col = bairros_e_zonas@data$color,
border = adjustcolor('black', alpha.f = 0.3)
# border = NA
)
points(acc$lng, acc$lat,
pch = acc$pch,
col = acc$color,
cex = 2)
text(x = label_df$long,
y = label_df$lat,
label = label_df$NOME,
cex = 0.3,
col = adjustcolor('black', alpha.f = 0.6))
legend('right',
fill = colors,
ncol = 1,
cex = 0.8,
border = NA,
col = colors,
legend = 1:length(colors),
title = 'Women')
# text(x = acc$lng,
# y = acc$lat,
# label = gsub(' ', '\n', acc$label),
# cex = 0.6)
legend('topright',
pch = acc$pch,
col = acc$color,
legend = acc$label)
# Add compass rose
compassRose(x = -60.1, y = -3.12)
# Add scale
maps::map.scale(x =-59.91, y = -3.15, relwidth = 0.2,
metric = TRUE, ratio = TRUE,
col = adjustcolor('black', alpha.f = 0.6),
cex = 0.6)
dev.off()
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
##### Figure 2 - bairros
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
amazonas <- brazil2[brazil2@data$NAME_1 == 'Amazonas',]
amazonas@data$mulheres <- 0
amazonas@data$mulheres[amazonas@data$NAME_2 == 'São Gabriel de Cahoeira'] <- 1
amazonas@data$mulheres[amazonas@data$NAME_2 == 'Barcelos'] <- 1
amazonas@data$mulheres[amazonas@data$NAME_2 == 'Presidente Figueiredo'] <- 1
amazonas@data$mulheres[amazonas@data$NAME_2 == 'Tapauá'] <- 1
amazonas@data$mulheres[amazonas@data$NAME_2 == 'Rio Preto da Eva'] <- 2
amazonas@data$color <- adjustcolor(
ifelse(amazonas@data$mulheres == 0, 'white',
ifelse(amazonas@data$mulheres == 1, 'lightblue',
ifelse(amazonas@data$mulheres == 2, 'darkblue', 'black'))),
alpha.f = 0.6)
# Make a labeling vector
label_df <- amazonas@data
label_df$long <- coordinates(amazonas)[,1]
label_df$lat <- coordinates(amazonas)[,2]
label_df <- label_df[label_df$mulheres > 0,]
label_df$NAME_2 <- gsub(' ', '\n', label_df$NAME_2)
pdf('figure_2.pdf', width = 10, height = 8)
plot(amazonas,
col = amazonas@data$color,
border = adjustcolor('black', alpha.f = 0.3))
text(x = label_df$long,
y = label_df$lat,
label = label_df$NAME_2,
cex = 0.4,
col = adjustcolor('black', alpha.f = 0.6))
legend('bottomright',
fill = adjustcolor(c('white', 'lightblue', 'darkblue'), alpha.f = 0.6),
legend = c(0, 1, 2),
cex = 0.8,
border = NA,
title = 'Women')
# Add compass rose
compassRose(x = -72, y = -1.4)
# Add scale
maps::map.scale(x =-64, y = -9.5, relwidth = 0.2,
metric = TRUE, ratio = TRUE,
col = adjustcolor('black', alpha.f = 0.6),
cex = 0.6)
dev.off()
|
8fd04719dd11319c33c7835ca12d0729516e6622
|
021fa1134701528153dab7dd4c24ed145d15af06
|
/Template.R
|
12fd9c8bdf56d63a4b4b84502051c42697854615
|
[] |
no_license
|
a30123/R_Handy
|
924802172fdd1dab1f965f5f90c029ebe992db26
|
059abe4df07bbcd1c76d02eef2befe16b04f3c59
|
refs/heads/master
| 2021-01-19T12:36:20.663018
| 2015-07-05T11:42:35
| 2015-07-05T11:42:35
| 38,416,196
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,051
|
r
|
Template.R
|
#### created date:
#### last modified date:
#### author:A30123
#### description:
#########################################################################################################
### ##### ##### ##### ############### # ### ### ### ################
### ######### ######## ######## #################### # # ### ### ### ### ### ################
### ######### ######## ######## #################### #### ### ### ### ### ### ################
### ##### ######## ######## ############### #### ### ### ### ################
#########################################################################################################
#########################################################################################################
####################################### IMPORT LIBRARIES ###########################################
#########################################################################################################
#########################################################################################################
######################################## FUNCTIONS ##########################################
#########################################################################################################
#########################################################################################################
####################################### INITIALIZING ###########################################
#########################################################################################################
#########################################################################################################
######################################## MAIN PROGRAM ##########################################
#########################################################################################################
#start timer###
ptm<-proc.time()
proc.time()-ptm
|
8f8aae35fe32f7dfb78e8093d74f73109b2add4c
|
f99326be507c62c63b91a45ec3246aa1b3a55f30
|
/RandForest.R
|
c1207b7e7049b4d78f82df18c5eacbef2d1169d9
|
[] |
no_license
|
sherryxhu/wesad
|
3fa6b5190bef1a517bde46e0c6e38f8e51f088d8
|
1695af914b638a6f809e17599e4fc22b0c9bc524
|
refs/heads/master
| 2022-11-08T22:25:54.747305
| 2020-06-20T17:09:51
| 2020-06-20T17:09:51
| 272,251,557
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,934
|
r
|
RandForest.R
|
# install packages
install.packages('nnet', repos = "http://cran.us.r-project.org")
install.packages('tidyverse', repos = "http://cran.us.r-project.org")
install.packages('dplyr', repos = "http://cran.us.r-project.org")
install.packages('arm', repos = "http://cran.us.r-project.org")
install.packages('plyr', repos = "http://cran.us.r-project.org")
install.packages('randomForest', repos = "http://cran.us.r-project.org")
install.packages('caTools', repos = "http://cran.us.r-project.org")
install.packages('partykit', repos = "http://cran.us.r-project.org")
install.packages('caret', repos = "http://cran.us.r-project.org")
# load packages
library(tidyverse)
library(dplyr)
library(nnet) # for multinom
library(plyr) # for count
library(knitr) # for kable
library(randomForest) # for random forest
library(caTools) # for random forest
library(partykit) # for ctree
library(caret)
#load data
load("data/df.RData")
load("data/df_train.RData")
load("data/df_test.RData")
mcr=NULL
# function to compute missclassification rate
avclassifyrate=function(data, model){
rating=l1=l2=l3=l4=l0=NULL
for (i in 1:5){
tf=c(sample(as.numeric(rownames(subset(df, label==0))),nrow(subset(df, label==0))*0.8), sample(as.numeric(rownames(subset(df, label==1))),nrow(subset(df, label==1))*0.8),sample(as.numeric(rownames(subset(df, label==2))),nrow(subset(df, label==2))*0.8), sample(as.numeric(rownames(subset(df, label==3))),nrow(subset(df, label==3))*0.8), sample(as.numeric(rownames(subset(df, label==4))),nrow(subset(df, label==4))*0.8))
t_rain <- data[tf,]
t_est <- data[-tf,]
m1<- model
# column of predicted classes
t_est$predictions <- predict(m1, t_est)
# 0-1 loss (overall)
loss <- ifelse(t_est$label != t_est$predictions, 1, 0)
rating <- c(rating, sum(loss==1)/length(loss)) #overall misclassification rate
#missclassified0
sub0<- subset(t_est, label==0)
loss0 <- ifelse(sub0$label != sub0$predictions, 1, 0)
l0<- c(l0, sum(loss0==1)/length(loss0))
#missclassified1
sub1<- subset(t_est, label==1)
loss0 <- ifelse(sub1$label != sub1$predictions, 1, 0)
l1<- c(l1, sum(loss0==1)/length(loss0))
#missclassified2
sub2<- subset(t_est, label==2)
loss0 <- ifelse(sub2$label != sub2$predictions, 1, 0)
l2<- c(l2, sum(loss0==1)/length(loss0))
#missclassified3
sub3<- subset(t_est, label==3)
loss0 <- ifelse(sub3$label != sub3$predictions, 1, 0)
l3<- c(l3, sum(loss0==1)/length(loss0))
#missclassified4
sub4<- subset(t_est, label==4)
loss0 <- ifelse(sub4$label != sub4$predictions, 1, 0)
l4<- c(l4, sum(loss0==1)/length(loss0))
}
mcr=as.data.frame(rbind(l0,l1,l2,l3,l4,rating))
mcr$Average=rowMeans(mcr)
return(mcr)
}
#final model
#random forest
rftrain=df_train
#single effects
rf <- randomForest(as.factor(label) ~ chest_ACC_X + chest_ACC_Y + chest_ACC_Z + chest_ECG + chest_EMG + chest_EDA + chest_Temp + chest_Resp + wrist_ACC_X + wrist_ACC_Y + wrist_ACC_Z + wrist_BVP + wrist_EDA + wrist_Temp,data=rftrain, ntree=5000, nodesize=15)
rftrain$rfpred=predict(rf, df_test)
#plot actual vs. predicted
#actual predictions
rftrain$finpred=predict(mod1, df_test)
#Get missclass rate
avsens=as.data.frame(avclassifyrate(df, rf)$Average)
print(avsens)
save(avsens, file="MisclassRandFor.RData")
#Make percentage classification table
pclass=merge(count(rftrain$finpred), count(rftrain$rfpred), by="x", all=T)
pclass=pclass[,-1]
pclass=(pclass/nrow(rftrain))*100
#Calculate differences in classification relative to final model
diffrf=ifelse(rftrain$finpred==rftrain$rfpred,0,1)
pclass=rbind(pclass, c(0,sum(diffrf)))
rownames(pclass)=c("% Classified as 0","% Classified as 1","% Classified as 2","% Classified as 3","% Classified as 4", "Absolute Diff in Classification")
colnames(pclass)=c("Final Model","Random Forest")
pclass[is.na(pclass)]=0
print(pclass)
save(pclass, file="Classification_Table_for Forest.RData")
|
d0b6c777b1310244f3d4289feb19afc95893b080
|
8629ad85edfb2293280f0820c27c933739bebc5a
|
/submissions/01_r4ds-data-transformation-hl2da.R
|
957b9a18a885c03769a0ed618c8810c46f2369ee
|
[] |
no_license
|
GCOM7140/r4ds-exercises
|
ce94ac4f3a4a7c5d3038db76ae54cacbad6ad22d
|
a5fefe1bfcca6ae0d4d231a4b3e2222cb963ce17
|
refs/heads/master
| 2021-05-01T15:03:27.315763
| 2019-07-29T22:31:46
| 2019-07-29T22:31:46
| 121,028,562
| 1
| 1
| null | 2018-04-11T13:10:41
| 2018-02-10T15:43:46
|
HTML
|
UTF-8
|
R
| false
| false
| 2,628
|
r
|
01_r4ds-data-transformation-hl2da.R
|
<<<<<<< HEAD
library(tidyverse)
library(nycflights13)
# Question1
# How many flights flew into LAX
filter(flights, dest == "LAX")
nrow(filter(flights, dest == "LAX"))
flights %>%
filter(dest =="LAX") %>%
nrow()
#HOW many flights flew out of LAX
flights %>%
filter(origin =="LAX") %>%
nrow()
#How many flights were longer than or equal to 2,000 miles in distance?
flights %>%
filter(distance >= 2000) %>%
nrow()
# How many flights were destined for airports in the Los Angeles area (LAX, ONT, SNA, PSP, SBD, BUR, or LGB), but did not originate out of JFK?
flights %>%
filter(
dest %in% c("LAX", "ONT", "SNA", "PSP", "SBD", "BUR", "LGB"),
origin != "JFK"
) %>%
nrow()
# Question2
flights %>%
filter(!is.na(dep_time), is.na(arr_time)) %>%
nrow()
# Question3
flights %>%
arrange(desc(is.na(arr_time)))
# Question4
select(flights, contains("TIME"))
# Contains() is case sensitive so this code won't select any column names.
select(flights, contains("TIME", ignore.case = T))
# Question5
flights %>%
filter(distance >= 2000, arr_delay > 0) %>%
group_by(dest) %>%
summarize(arr_delay_mins = sum(arr_delay)) %>%
mutate(arr_delay_pct_of_total = arr_delay_mins / sum(arr_delay_mins)) %>%
arrange(desc(arr_delay_pct_of_total)) %>%
head(3)
=======
library(tidyverse)
library(nycflights13)
# Question1
# How many flights flew into LAX
filter(flights, dest == "LAX")
nrow(filter(flights, dest == "LAX"))
flights %>%
filter(dest =="LAX") %>%
nrow()
#HOW many flights flew out of LAX
flights %>%
filter(origin =="LAX") %>%
nrow()
#How many flights were longer than or equal to 2,000 miles in distance?
flights %>%
filter(distance >= 2000) %>%
nrow()
# How many flights were destined for airports in the Los Angeles area (LAX, ONT, SNA, PSP, SBD, BUR, or LGB), but did not originate out of JFK?
flights %>%
filter(
dest %in% c("LAX", "ONT", "SNA", "PSP", "SBD", "BUR", "LGB"),
origin != "JFK"
) %>%
nrow()
# Question2
flights %>%
filter(!is.na(dep_time), is.na(arr_time)) %>%
nrow()
# Question3
flights %>%
arrange(desc(is.na(arr_time)))
# Question4
select(flights, contains("TIME"))
# Contains() is case sensitive so this code won't select any column names.
select(flights, contains("TIME", ignore.case = T))
# Question5
flights %>%
filter(distance >= 2000, arr_delay > 0) %>%
group_by(dest) %>%
summarize(arr_delay_mins = sum(arr_delay)) %>%
mutate(arr_delay_pct_of_total = arr_delay_mins / sum(arr_delay_mins)) %>%
arrange(desc(arr_delay_pct_of_total)) %>%
head(3)
>>>>>>> 333a6c412c7a689d232b5af0e23de7fa80cf517c
|
43a8f805d63f9f365ca2e07dc37044c60d710f2e
|
db9a558fe2273bcaa88d5c0c47633857766492fa
|
/Chapter 1 Updated.R
|
e361f67523eb5d09107ab6fb4da1d49038e70bfd
|
[] |
no_license
|
uvonpunkfarm/que
|
e0058061a286107e6b638aa487ff902ff1b0a4ea
|
fe375fd65a12e363d7a96ed118b49b560a6a9158
|
refs/heads/master
| 2020-12-26T20:25:45.186586
| 2020-02-09T21:33:09
| 2020-02-09T21:33:09
| 237,631,400
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 900
|
r
|
Chapter 1 Updated.R
|
x <- c(5,10,15,20,25,30,35,40)
x
sum(x)
mean(x)
x
x
y <- seq(5,40,13)
y
z <- seq(2,6,2)
z
hypoteneuse <- function(a,b){
hyp <- sqrt(a^2+b^2)
return(hyp)
}
Raptors <-c("Lowry", "DeRozan", "Bosh", "Kawhi")
Long ass number <- 5:200
quadrifecta <- c(1,2,3,4)
repeated_quadrifecta <- rep(quadrifecta,5)
repeated_quadrifecta
repeating <-c(2,1,2,1)
rep_vector <- rep(quadrifecta, repeating)
rep_vector
num_matrix <- seq(5,100,5)
dim(num_matrix) <c(6,4)
num_matrix
num_matrix[3,1]
num_matrix2 <-seq(1,10,1)
dim(num_matrix2) <c(3,2)
num_matrix2
Raptors <-c("Lowry", "DeRozan", "Bosh", "Kawhi")
ages <-c(34, 29, 35, 27)
Raptorsages <-list(names=Raptors, currentage=ages)
Raptorsages
Raptorsages$currentage[3]
Raptorsages$names[Raptorsages$currentage>=28]
xx <-seq(1,6,1)
yy <-NULL
for(i in 1:length(xx))
{if(xx[i]%% 2 ==0){yy[i] <-"EVEN"}
else{yy[i] <- "ODD"}
}
yy
xx
|
73496ffcce43e5246e8427b8aef8c5fa932e4f80
|
57d6bac4eae56c4efcddcd212eedf47eaafa142d
|
/practical_machine_learning/project_scratchpad_june.R
|
32089cc6daadb6a0fbe87ceed69ad435a88f3aeb
|
[] |
no_license
|
sdevine188/coursera_code
|
14e8ef7e74e02c50fca76636f7cb4ed7f47af6bf
|
f2069acec6a247746c178ee706bd85fa4d550479
|
refs/heads/master
| 2021-01-25T06:40:03.171925
| 2016-01-25T16:15:19
| 2016-01-25T16:15:19
| 31,866,660
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,404
|
r
|
project_scratchpad_june.R
|
# read in data
setwd("C:/Users/Steve/Desktop/Coursera/Practical Machine Learning")
full_training <- read.csv("pml-training.csv")
full_testing <- read.csv("pml-testing.csv")
# split full_training into training and testing
in_train <- createDataPartition(full_training$classe, p = .7, list = FALSE)
training <- full_training[in_train, ]
testing <- full_training[-in_train, ]
# remove non-predictors
training1 <- training[ , -1]
training1 <- training1[ , -c(2:6)]
testing1 <- testing[ , -1]
testing1 <- testing1[ , -c(2:6)]
# find NAs
missing <- lapply(training1, function(x) length(which(is.na(x))))
missing <- lapply(testing1, function(x) length(which(is.na(x))))
# convert NAs to blanks
training2 <- training1
training2[is.na(training2)] <- ""
testing2 <- testing1
testing2[is.na(testing2)] <- ""
# convert #DIV/0! to blanks
# all the data remains accurate after conversion, but all variables are inexplicably turned into factors
training3 <- training2
training3 <- as.data.frame(lapply(training3, function(x) str_replace(x, "#DIV/0!", "")))
# turn factors into numeric variables to speed processing time
# all variable columns except 1 and 154 can be converted to numeric
# column 1 is user_name, column 154 is classe
# but it converts blanks to NA, so we'll need to reconvert NAs to blanks again
training4 <- training3
# more efficient version of as.numeric(as.character(x))
# http://stackoverflow.com/questions/3418128/how-to-convert-a-factor-to-an-integer-numeric-without-a-loss-of-information
# training4 <- as.data.frame(lapply(training4, function(x) as.numeric(as.character(x))))
training4 <- as.data.frame(lapply(training4[ , -c(1, 154)], function(x) as.numeric(levels(x))[x]))
# re-add columns 1 and 154
training4 <- cbind(training3[ , 1], training4)
training4 <- cbind(training4, training3[ , 154])
names(training4)[154] <- "classe"
# reconvert NAs to blanks
training5 <- training4
training5[is.na(training5)] <- ""
# small dataset to try rf model
sample_index <- sample(1:nrow(training5), 500)
sample <- training3[sample_index, ]
str(sample)
rf_mod <- train(classe ~ ., method = "rf", data = sample2)
# i think this is a slower version bc it limits number of k in k-fold cv to 3, instead of default 10
rf_mod <- train(classe ~ ., method = "rf", data = sample, trControl = trainControl(method = "cv"), number = 3)
# try gbm
gbm_mod <- train(classe ~ ., method = "gbm", data = sample)
|
94c011b1344d5fcf6d08962b94adfacb2c030402
|
7fb8caee598f0d71598f3f022d9552c6b9b862f6
|
/sentiment.r
|
9c9bd112e6128bb878f132d98261ec30c23d7a6b
|
[] |
no_license
|
Nivas138/Predicting-Social-Nexus
|
6525f6d8a68de5775ff02f08ee20e8876386d051
|
3077bd25a22e0600a2e4879dca82f593e52e1e3d
|
refs/heads/master
| 2020-04-13T22:26:56.306650
| 2019-03-20T08:27:48
| 2019-03-20T08:27:48
| 163,479,473
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,600
|
r
|
sentiment.r
|
setwd('C:\\Users\\Nivas\\Documents\\')
getwd()
install.packages("ggplot2")
install.packages("tm")
install.packages("wordcloud")
install.packages("syuzhet")
?tm
library(ggplot2)
library(tm)
library(wordcloud)
library(syuzhet)
texts = readLines("chat1.txt")
print(texts)
docs = Corpus(VectorSource(texts))
docs
trans=content_transformer(function(x,pattern) gsub(pattern," ",x))
docs=tm_map(docs,trans,"/")
docs=tm_map(docs,trans,"@")
docs=tm_map(docs,trans,"\\|")
docs=tm_map(docs,content_transformer(tolower))
docs=tm_map(docs,removeNumbers)
docs=tm_map(docs,removeWords,stopwords("english"))
docs=tm_map(docs,removePunctuation)
docs=tm_map(docs,stripWhitespace)
docs=tm_map(docs,stemDocument)
docs
dtm=TermDocumentMatrix(docs)
mat=as.matrix(dtm)
mat
v=sort(rowSums(mat),decreasing=TRUE)
print(v)
d = data.frame(word=names(v),freq=v)
head(d)
set.seed(1056)
wordcloud(words=d$word,freq=d$freq,min.freq=1,max.words=200,random.order=FALSE,
rot.per=0.45,colors=brewer.pal(8,"Dark2"))
?get_nrc_sentiment
sentiment=get_nrc_sentiment(texts)
print(sentiment)
text=cbind(texts,sentiment)
head(text)
TotalSentiment = data.frame(colSums(text[,c(2:11)]))
TotalSentiment
names(TotalSentiment)="count"
TotalSentiment=cbind("sentiment" = rownames(TotalSentiment),TotalSentiment)
print(TotalSentiment)
rownames(TotalSentiment)
ggplot(data=TotalSentiment, aes(x = sentiment,y=count)) + geom_bar(aes(fill=sentiment),stat="identity")+theme(legend.position="none")+xlab("sentiment")+ylab("Total Count")+ggtitle("Total semtiment Score")
|
7894c5bcf78e793c74074f125630c74445c6b2e9
|
da4c8b3a0201143378037766966603e8d5e4598d
|
/plot1.R
|
f98d286f62a24e738e771bb829fda6bfdfd765f6
|
[] |
no_license
|
hmoralesos/ExData_Plotting1
|
1b0e9e089b19c7a699acd3959097c2053a8723d6
|
3dc08ee4931e6dc1ca57722ca593ecafc4931f99
|
refs/heads/master
| 2021-01-17T20:24:59.835872
| 2016-08-15T19:11:38
| 2016-08-15T19:11:38
| 65,756,123
| 0
| 0
| null | 2016-08-15T18:43:22
| 2016-08-15T18:43:19
| null |
UTF-8
|
R
| false
| false
| 1,257
|
r
|
plot1.R
|
################################################################################
# Read complete dataset #
################################################################################
dataset<-read.table("household_power_consumption.txt",header=TRUE,sep=";")
head(dataset,5)
str(dataset)
dim(dataset)
################################################################################
# Extract data from the dates 2007-02-01 and 2007-02-02 #
################################################################################
data<-subset(dataset,Date=="1/2/2007"|Date=="2/2/2007")
head(data,5)
(n<-nrow(data))
################################################################################
# Plot 1 #
################################################################################
# Column Global_active_power
plot1<-as.numeric(as.character(data$Global_active_power))
# Save plot
png(filename = "plot1.png",width = 480, height = 480, units = "px", pointsize =
12,bg = "white")
hist(plot1,main="Global Active Power", xlab="Global Active Power (kilowatts)",
col="red")
dev.off() # close png
|
779d56e0eac0986e7a411fe7ff6dc307e14c419e
|
714e7c6736a2e3d8fd07634427c4a8bb3cef2d61
|
/man/plot_avg_dot.Rd
|
9c313e4ce776c5e8b33c5b7c4d2e58312fc89a51
|
[
"MIT"
] |
permissive
|
flaneuse/llamar
|
da7cb58a03b2adbffb6b2fe2e57f3ffeede98afb
|
ea46e2a9fcb72be872518a51a4550390b952772b
|
refs/heads/master
| 2021-01-18T00:10:00.797724
| 2017-10-24T13:41:21
| 2017-10-24T13:41:21
| 48,335,371
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 3,444
|
rd
|
plot_avg_dot.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_avg_dot.R
\name{plot_avg_dot}
\alias{plot_avg_dot}
\title{Plot a dot plot after averaging the values}
\usage{
plot_avg_dot(df, by_var = "region", value_var = "avg", incl_x_axis = TRUE,
x_label = NULL, x_limits = NULL, x_breaks = waiver(),
include_n = TRUE, n_shape = "square", low_colour = grey10K,
high_colour = grey70K, use_weights = FALSE, strata_var = "strata",
psu_var = "psu", weight_var = "weight", na.rm = TRUE,
sort_asc = FALSE, sort_by = "avg", plot_ci = TRUE, ci_factor = 2,
lb_var = "lb", ub_var = "ub", ci_colour = grey25K, ci_alpha = 0.6,
ci_size = 2, ref_line = TRUE, ref_text = "sample average",
label_ref = TRUE, nudge_ref_label = NULL, ref_label_y = 1,
ref_arrow = arrow(length = unit(0.007, "npc")), ref_stroke = 0.5,
ref_colour = grey75K, lollipop = FALSE, lollipop_stroke = 0.25,
lollipop_colour = grey75K, facet_var = NULL, ncol = NULL, nrow = NULL,
scales = "fixed", dot_size = 6, dot_shape = 21,
dot_fill_cont = brewer.pal(9, "YlGnBu"), label_vals = TRUE,
label_size = 3, label_colour = grey75K, label_digits = 1,
percent_vals = FALSE, value_label_offset = NULL, sat_threshold = 0.5,
horiz = TRUE, file_name = NULL, width = 10, height = 6,
saveBoth = FALSE, font_normal = "Lato", font_semi = "Lato",
font_light = "Lato Light", panel_spacing = 1, font_axis_label = 12,
font_axis_title = font_axis_label * 1.15, font_facet = font_axis_label *
1.15, font_legend_title = font_axis_label,
font_legend_label = font_axis_label * 0.8, font_subtitle = font_axis_label
* 1.2, font_title = font_axis_label * 1.3, legend.position = "none",
legend.direction = "horizontal", grey_background = FALSE,
background_colour = grey10K, projector = FALSE)
}
\description{
Plot a dot plot after averaging the values
}
\examples{
# generate random data
library(dplyr)
df = data.frame(avg = sample(1:100, 10), region = letters[1:10], ci = sample(1:100, 10)/10) \%>\% mutate(lb = avg - ci, ub = avg + ci)
# sans confidence intervals
plot_dot(df, by_var = 'region', value_var = 'avg')
# with confidence intervals, no labels
plot_dot(df, by_var = 'region', value_var = 'avg', plot_ci = TRUE, label_vals = FALSE)
# as lollipops
df2 = data.frame(avg = sample(-100:100, 10)/100, region = letters[1:10], ci = sample(1:100, 20)/1000) \%>\% mutate(lb = avg - ci, ub = avg + ci)
library(RColorBrewer)
plot_dot(df2, by_var = 'region', value_var = 'avg', lollipop = TRUE, dot_fill_cont = brewer.pal(10, 'RdYlBu'))
# percent labels
plot_dot(df2, by_var = 'region', value_var = 'avg', percent_vals = TRUE, lollipop = TRUE, dot_fill_cont = brewer.pal(10, 'RdYlBu'))
# with reference line
plot_dot(df2, by_var = 'region', value_var = 'avg', ref_line = 0, ref_text = 'no change', label_ref = FALSE, lollipop = TRUE, dot_fill_cont = brewer.pal(10, 'RdYlBu'), percent_vals = TRUE)
# horizontal
plot_dot(df2, by_var = 'region', value_var = 'avg', horiz = FALSE, ref_line = 0, ref_text = 'no change', lollipop = TRUE, plot_ci = TRUE, dot_fill_cont = brewer.pal(10, 'RdYlBu'))
# in-built facet_wrap. Note: may screw up ordering, since will sort based on ALL the data.
df3 = data.frame(avg = sample(-100:100, 20), region = rep(letters[1:10], 2), group = c(rep('group1', 10), rep('group2', 10)))
plot_dot(df3, by_var = 'region', value_var = 'avg', facet_var = 'group', lollipop = TRUE, dot_fill_cont = brewer.pal(10, 'RdYlBu'))
}
|
8de5be52896a330d9c1aa8306d1d06d4cd921b38
|
109681dbabeb2ba82dc1ef895a28d40f03033ccb
|
/man/ontologyLogPage-methods.Rd
|
16bed29bf67ea25f428bbb7494345174f15e23f4
|
[] |
no_license
|
frenkiboy/cellexalvrR
|
97cc210f47c0fcff998704200adfcd549ffabbcf
|
cf9ab9e8c5fd519d0db2dd98b7ccbc84812cba77
|
refs/heads/master
| 2020-12-22T11:00:05.728789
| 2020-01-28T14:49:13
| 2020-01-28T14:49:13
| 236,758,585
| 0
| 0
| null | 2020-01-28T14:47:26
| 2020-01-28T14:47:25
| null |
UTF-8
|
R
| false
| true
| 842
|
rd
|
ontologyLogPage-methods.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ontologyLogPage.R
\docType{methods}
\name{ontologyLogPage}
\alias{ontologyLogPage}
\alias{ontologyLogPage,cellexalvrR-method}
\title{description of function ontologyLogPage}
\usage{
ontologyLogPage(cellexalObj, genes, grouping = NULL, ontology = "BP",
topNodes = 10, ...)
}
\arguments{
\item{cellexalObj}{the cellexalvrR object}
\item{genes}{a list of gene symbols (IMPORTANT)}
\item{grouping}{the grouping this gene list originated on (default = NULL; use last grouping)}
\item{ontology}{which GO ontology to choose from (default = "BP")}
\item{topNodes}{how many GO terms to report (default 10)}
\item{...}{unused}
}
\description{
creates the GO analysis for a gene list and puts it into the report.
}
\details{
The ontology analysis for the log files.
}
|
bd7440d20b5875b1e8ca57116b8ff91c92b9d6da
|
58f4573bc3e9efbc14ff9ebbf089231c246cf066
|
/man/inlineModel.Rd
|
895753bad6ac628462b0402af0c548a93d1eddea
|
[] |
no_license
|
Anathawa/mlxR
|
1a4ec2f277076bd13525f0c1d912ede3d20cb1cc
|
7e05119b78b47c8b19126de07c084e7d267c4baf
|
refs/heads/master
| 2021-01-19T09:17:35.765267
| 2017-04-05T18:00:39
| 2017-04-05T18:00:39
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 311
|
rd
|
inlineModel.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/inlineModel.R
\name{inlineModel}
\alias{inlineModel}
\title{inline model}
\usage{
inlineModel(str, filename = NULL)
}
\arguments{
\item{str}{model}
\item{filename}{where to write the temporary model}
}
\description{
inline model
}
|
fc1a2cb14010fca4dbefa9f9828c2477cb486e0c
|
f25c5405790cf17a2b6e78b4ef58654810c8bb7b
|
/R/piechart.R
|
32fff8197d5b8c9a0f0e970e21c6e576b6017c2e
|
[] |
no_license
|
moturoa/shintodashboard
|
15ad881ea4c72549b616a3021852a0db8c25f6fd
|
80385da221d370a563eb1cfe8946964acfacfe15
|
refs/heads/master
| 2023-05-31T07:05:11.026309
| 2021-06-28T12:55:32
| 2021-06-28T12:55:32
| 312,505,839
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,174
|
r
|
piechart.R
|
# #' @importFrom waffle geom_waffle
# #' @importFrom waffle theme_enhance_waffle
piechart <- function(data,
xvar,
yvar,
xlab = NULL,
ylab = NULL,
glab = NULL,
type = "Pie",
na.rm=FALSE){
data <- as.data.frame(data)
data$xv <- data[,xvar]
data$yv <- data[,yvar]
if(na.rm){
data <- dplyr::filter(data, !is.na(xv))
}
type <- match.arg(type)
dat <- group_by(data, xv) %>%
summarize(n = sum(yv, na.rm=TRUE))
if(is.null(xlab))xlab <- xvar
if(is.null(ylab))ylab <- ""
if(is.null(glab))glab <- ""
if(type == "Pie"){
ggplot(dat, aes(fill = xv, y = n, x = "")) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start=0) +
theme_minimal() +
labs(x = xlab, y = ylab, fill = glab)
}
# else if(type == "Waffle"){
#
# ggplot(dat, aes(fill = xv, values = n, x = "")) +
# geom_waffle(n_rows = 25, size = 0.33, colour = "white", flip = FALSE) +
# theme_minimal() +
# labs(x = xlab, y = ylab)
# # theme_enhance_waffle() +
#
# }
}
|
d81082b025b15834cce818b8dc6c2de6f4c8c166
|
d8da5c909feddfa679dceb2aa79da8483b607ada
|
/models/stacked_model/stacked_model_draft.R
|
560a31eb4f1ed8da430a84ee0a719d79f3245241
|
[] |
no_license
|
edouardArgenson/house_prices
|
9af379fba6378a123227239c1e56a0fb991bac5f
|
6c348f2d02359ba2790ae9f3e23dc287aabb2d9a
|
refs/heads/master
| 2021-01-20T08:41:40.694968
| 2017-05-03T18:25:11
| 2017-05-03T18:25:11
| 70,399,835
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 15,188
|
r
|
stacked_model_draft.R
|
library('lattice')
library('ggplot2')
library('caret')
library('data.table')
library('Metrics')
library('MASS')
library('e1071')
library('kernlab')
library('gbm')
library('survival')
library('splines')
library('parallel')
library('plyr')
train = fread('~/kaggle/house_prices/data/train.csv',
colClasses=c('MiscFeature'='character','PoolQC'='character','Alley'='character'))
# Rename columns 1stFlrSF, 2ndFlrSF, and 3SsnPorch
FirstFlrSF=train$'1stFlrSF'
SecondFlrSF=train$'2ndFlrSF'
ThreeSsnPorch=train$'3SsnPorch'
new_names = names(train)[-which(names(train)=='1stFlrSF'|names(train)=='2ndFlrSF'|names(train)=='3SsnPorch')]
to_add = data.table(FirstFlrSF,SecondFlrSF,ThreeSsnPorch)
train = cbind(train[,new_names,with=FALSE],to_add)
# Transform categorical arguments KitchenQual, ExterQual, BsmtQual, GarageFinish, into numerical
# KitchenQual
nKitchenQual = numeric(length(train$KitchenQual))
nKitchenQual[train$KitchenQual=='TA']=1.0
nKitchenQual[train$KitchenQual=='Gd']=2.0
nKitchenQual[train$KitchenQual=='Ex']=3.0
train=cbind(train,nKitchenQual)
# ExterQual
nExterQual = numeric(length(train$ExterQual))
nExterQual[train$ExterQual=='TA']=1.0
nExterQual[train$ExterQual=='Gd']=2.0
nExterQual[train$ExterQual=='Ex']=3.0
train=cbind(train,nExterQual)
# BsmtQual
nBsmtQual = numeric(length(train$BsmtQual))
nBsmtQual[train$BsmtQual=='TA']=1.0
nBsmtQual[train$BsmtQual=='Gd']=2.0
nBsmtQual[train$BsmtQual=='Ex']=3.0
train=cbind(train,nBsmtQual)
# GarageFinish
nGarageFinish = numeric(length(train$GarageFinish))
nGarageFinish[train$GarageFinish=='Unf']=1.0
nGarageFinish[train$GarageFinish=='RFn']=2.0
nGarageFinish[train$GarageFinish=='Fin']=3.0
train=cbind(train,nGarageFinish)
# Full and half bathrooms
train$Bath = train$FullBath + train$HalfBath
train$BsmtBaths = train$BsmtFullBath + train$BsmtHalfBath
# TotalBsmtSF_on_GRLivArea (for SVR)
train$TotalBsmtSF_on_GrLivArea = train$TotalBsmtSF/train$GrLivArea
# MSSubClassCat
train$MSSubClassCat = train[,.(MSSubClassCat=sapply(MSSubClass,toString)),with=TRUE]
# Deal with missing values
LotFrontage_mean = round(mean(train$LotFrontage,na.rm=TRUE))
train[which(is.na(LotFrontage)),'LotFrontage'] <- LotFrontage_mean
train=cbind(train,"IsGarage"=1+numeric(nrow(train)))
train[which(is.na(GarageYrBlt)),'GarageYrBlt'] <- 1900
#train[which(is.na(GarageQual)),'IsGarage'] <- 0
train[which(is.na(MasVnrArea)),'MasVnrArea'] <- 0
train[which(is.na(BsmtCond)),'BsmtCond'] <- 'MISSING'
train[which(is.na(BsmtFinType1)),'BsmtFinType1'] <- 'MISSING'
train[which(is.na(BsmtFinType2)),'BsmtFinType2'] <- 'MISSING'
train[which(is.na(BsmtFinSF1)),'BsmtFinSF1'] <- 0
train[which(is.na(BsmtFinSF2)),'BsmtFinSF2'] <- 0
train[which(is.na(TotalBsmtSF)),'TotalBsmtSF'] <- 0
train[which(is.na(GarageCars)),'GarageCars'] <- 0
train[which(is.na(GarageArea)),'GarageArea'] <- 0
train[which(is.na(BsmtUnfSF)),'BsmtUnfSF'] <- 0
train[which(is.na(BsmtFullBath)),'BsmtFullBath'] <- 0
train[which(is.na(BsmtHalfBath)),'BsmtHalfBath'] <- 0
train[which(is.na(MSZoning)),'MSZoning'] <- 'RL'
train[which(is.na(SaleType)),'SaleType'] <- 'Oth'
train[which(is.na(Exterior1st)),'Exterior1st'] <- 'Other'
train[which(is.na(Exterior2nd)),'Exterior2nd'] <- 'Other'
train[which(is.na(Functional)),'Functional'] <- 'Typ'
## Deal with missing values
#test[which(is.na(LotFrontage)),'LotFrontage'] <- LotFrontage_mean
#test=cbind(test,"IsGarage"=1+numeric(nrow(test)))
#test[which(is.na(GarageYrBlt)),'GarageYrBlt'] <- 1900
##test.sample[which(is.na(GarageQual)),'IsGarage'] <- 0
#test[which(is.na(MasVnrArea)),'MasVnrArea'] <- 0
#test[which(is.na(BsmtCond)),'BsmtCond'] <- 'MISSING'
#test[which(is.na(BsmtFinType1)),'BsmtFinType1'] <- 'MISSING'
#test[which(is.na(BsmtFinType2)),'BsmtFinType2'] <- 'MISSING'
#test[which(is.na(BsmtFinSF1)),'BsmtFinSF1'] <- 0
#test[which(is.na(BsmtFinSF2)),'BsmtFinSF2'] <- 0
#test[which(is.na(TotalBsmtSF)),'TotalBsmtSF'] <- 0
#test[which(is.na(GarageCars)),'GarageCars'] <- 0
#test[which(is.na(GarageArea)),'GarageArea'] <- 0
#test[which(is.na(BsmtUnfSF)),'BsmtUnfSF'] <- 0
#test[which(is.na(BsmtFullBath)),'BsmtFullBath'] <- 0
#test[which(is.na(BsmtHalfBath)),'BsmtHalfBath'] <- 0
#test[which(is.na(MSZoning)),'MSZoning'] <- 'RL'
#test[which(is.na(SaleType)),'SaleType'] <- 'Oth'
#test[which(is.na(Exterior1st)),'Exterior1st'] <- 'Other'
#test[which(is.na(Exterior2nd)),'Exterior2nd'] <- 'Other'
#test[which(is.na(Functional)),'Functional'] <- 'Typ'
#test$BsmtBaths = test$BsmtFullBath + test$BsmtHalfBath
#train.kept = train[,kept_features,with=FALSE]
#test.kept = test[,kept_features[-which(kept_features=="SalePrice")],with=FALSE]
# separate train set in two parts: train_a and train_b
# train_a for fitting base models
# train_b for fitting stage 2 model
set.seed(10)
train_a_part = createDataPartition(train$SalePrice,p=.80,list=FALSE)
#train.sample = train.kept[inTrain,-"SalePrice",with=FALSE]
#train.target = train.kept[inTrain,.(SalePrice=as.numeric(SalePrice))]
#test.sample = train.kept[-inTrain,-"SalePrice",with=FALSE]
#test.target = train.kept[-inTrain,.(SalePrice=as.numeric(SalePrice))]
# fit SVR model on train_a
# meta params: C=1.25, sigma=0.015
kept_features_svr = c("LotArea","OverallQual","YearBuilt","YearRemodAdd","nKitchenQual","nExterQual",
"nBsmtQual","GrLivArea","Bath","nGarageFinish",
"BsmtFinSF1","GarageCars","TotalBsmtSF","KitchenAbvGr","BedroomAbvGr","TotRmsAbvGrd","OverallCond",
"TotalBsmtSF_on_GrLivArea")
train.kept_svr = train[,c(kept_features_svr,"SalePrice"),with=FALSE]
train_a.sample = train.kept_svr[train_a_part,-"SalePrice",with=FALSE]
train_a.target = train.kept_svr[train_a_part,.(SalePrice=as.numeric(SalePrice))]
bootControl <- trainControl(number = 10, verboseIter=TRUE)
tuneGrid = expand.grid(C=c(1.25),sigma=c(0.015)) # mandatory
svrFit_a = train(x=train_a.sample,y=train_a.target$SalePrice,method='svmRadial',trControl=bootControl,
tuneGrid=tuneGrid, preProcess=c("center","scale"))
# predict train_b with SVR model
train_b.sample = data.table(scale(train.kept_svr[-train_a_part,-"SalePrice",with=FALSE]))
train_b.target = train.kept_svr[-train_a_part,.(SalePrice=as.numeric(SalePrice))]
svrFit_a.predict_b = predict(svrFit_a$finalModel,newdata=train_b.sample)
print("train_b.sample SalePrice predicted with model svrFit_a")
# print rmsle
print("rmsle:")
print(rmsle(train_b.target$SalePrice,svrFit_a.predict_b))
# fit gbm model on train_a
# meta parameters: 1950 trees, depth=4, shrinkage=.03
kept_num_features_gbm = c("LotFrontage", "LotArea", "OverallQual", "OverallCond",
"YearBuilt", "YearRemodAdd", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF",
"TotalBsmtSF", "FirstFlrSF", "SecondFlrSF", "LowQualFinSF", "GrLivArea", "BsmtFullBath",
"BsmtHalfBath", "FullBath", "HalfBath", "BedroomAbvGr", "KitchenAbvGr", "TotRmsAbvGrd",
"Fireplaces", "GarageYrBlt", "GarageCars", "GarageArea", "WoodDeckSF", "OpenPorchSF",
"EnclosedPorch", "ThreeSsnPorch", "ScreenPorch", "PoolArea", "MiscVal", "MoSold", "YrSold")
kept_cat_features_gbm = c("Neighborhood","ExterQual","HeatingQC","CentralAir","KitchenQual","SaleType",
"SaleCondition","IsGarage")
kept_features_gbm = c(kept_num_features_gbm,kept_cat_features_gbm)
train.kept_gbm = train[,c(kept_features_gbm,"SalePrice"),with=FALSE]
# Separate numeric and categorical features for conversion (as numeric and factor)
train_a.sample.num_features = train[train_a_part,kept_num_features_gbm,with=FALSE]
train_a.sample.cat_features = train[train_a_part,kept_cat_features_gbm,with=FALSE]
# Change class of data and merge back numeric and categorical
train_a.sample.num_features.toFit = train_a.sample.num_features[,lapply(.SD,as.numeric)]
train_a.sample.cat_features.toFit = train_a.sample.cat_features[,lapply(.SD,as.factor)]
train_a.sample = cbind(train_a.sample.num_features.toFit,train_a.sample.cat_features.toFit)
train_a.target = train.kept_gbm[train_a_part,.(SalePrice=as.numeric(SalePrice))]
bootControl <- trainControl(number = 10, verboseIter=TRUE)
gbmGrid = expand.grid(interaction.depth = (3:5),n.trees = c(1950),shrinkage=c(.02,.03,.04),
n.minobsinnode=10)
gbmFit_a = train(train_a.sample,train_a.target$SalePrice,method='gbm',trControl=bootControl,verbose=TRUE,
bag.fraction=.8,tuneGrid=gbmGrid,metric='RMSE')
# .1353
# predict train_b with gbm model
# Separate numeric and categorical features for conversion (as numeric and factor)
train_b.sample.num_features = train[-train_a_part,kept_num_features_gbm,with=FALSE]
train_b.sample.cat_features = train[-train_a_part,kept_cat_features_gbm,with=FALSE]
# Change class of data and merge back numeric and categorical
train_b.sample.num_features.toFit = train_b.sample.num_features[,lapply(.SD,as.numeric)]
train_b.sample.cat_features.toFit = train_b.sample.cat_features[,lapply(.SD,as.factor)]
train_b.sample = cbind(train_b.sample.num_features.toFit,train_b.sample.cat_features.toFit)
train_b.target = train.kept_gbm[-train_a_part,.(SalePrice=as.numeric(SalePrice))]
gbmFit_a.predict_b = predict(gbmFit_a$finalModel,newdata=train_b.sample,n.trees=1950)
print("train_b.sample SalePrice predicted with model gbmFit_a")
# print rmsle
print("rmsle:")
print(rmsle(train_b.target$SalePrice,gbmFit_a.predict_b))
# Create new data.table with predictions on train_b, for level 1 model training
#train_2 = data.table(preds_svr=svrFit_a.predict_b,
# preds_gbm=gbmFit_a.predict_b,SalePrice=train[-train_a_part,SalePrice])
train_2.sample = data.table(preds_svr=svrFit_a.predict_b,preds_gbm=gbmFit_a.predict_b)
train_2.target = data.table(SalePrice=train[-train_a_part,SalePrice])
#head(train_2.sample)
#head(train_2.target)
# Fitting a gbm as level 1 model
gbmGrid <- expand.grid(interaction.depth = (1:3),n.trees = (30:40)*5,
shrinkage = c(.02,.03,.04,.05,.06,.07,.08),n.minobsinnode = (2:10))
bootControl <- trainControl(number = 10, verboseIter=TRUE)
gbmFit_2 = train(train_2.sample,train_2.target$SalePrice,method='gbm',trControl=bootControl,verbose=TRUE,
bag.fraction=.6,tuneGrid=gbmGrid,metric='RMSE')
# grid-search result:
# n.trees = 165, interaction.depth = 1, shrinkage = 0.05, n.minobsinnode = 5
# load test file
test = fread('~/kaggle/house_prices/data/test.csv',
colClasses=c('MiscFeature'='character','PoolQC'='character','Alley'='character'))
# Il faut renommer les colonnes 1stFlrSF, 2ndFlrSF, et 3SsnPorch pour pas avoir d'emmerdes
FirstFlrSF=test$'1stFlrSF'
SecondFlrSF=test$'2ndFlrSF'
ThreeSsnPorch=test$'3SsnPorch'
new_names = names(test)[-which(names(test)=='1stFlrSF'|names(test)=='2ndFlrSF'|names(test)=='3SsnPorch')]
to_add = data.table(FirstFlrSF,SecondFlrSF,ThreeSsnPorch)
test = cbind(test[,new_names,with=FALSE],to_add)
# Transform categorical arguments KitchenQual, ExterQual, BsmtQual, GarageFinish, into numerical
# KitchenQual
nKitchenQual = numeric(length(test$KitchenQual))
nKitchenQual[test$KitchenQual=='TA']=1.0
nKitchenQual[test$KitchenQual=='Gd']=2.0
nKitchenQual[test$KitchenQual=='Ex']=3.0
test=cbind(test,nKitchenQual)
# ExterQual
nExterQual = numeric(length(test$ExterQual))
nExterQual[test$ExterQual=='TA']=1.0
nExterQual[test$ExterQual=='Gd']=2.0
nExterQual[test$ExterQual=='Ex']=3.0
test=cbind(test,nExterQual)
# BsmtQual
nBsmtQual = numeric(length(test$BsmtQual))
nBsmtQual[test$BsmtQual=='TA']=1.0
nBsmtQual[test$BsmtQual=='Gd']=2.0
nBsmtQual[test$BsmtQual=='Ex']=3.0
test=cbind(test,nBsmtQual)
# GarageFinish
nGarageFinish = numeric(length(test$GarageFinish))
nGarageFinish[test$GarageFinish=='Unf']=1.0
nGarageFinish[test$GarageFinish=='RFn']=2.0
nGarageFinish[test$GarageFinish=='Fin']=3.0
test=cbind(test,nGarageFinish)
# Full and half bathrooms
test$Bath = test$FullBath + test$HalfBath
test$BsmtBaths = test$BsmtFullBath + test$BsmtHalfBath
# TotalBsmtSF_on_GrLivArea
test$TotalBsmtSF_on_GrLivArea = test$TotalBsmtSF/test$GrLivArea
# MSSubClassCat
test$MSSubClassCat = test[,.(MSSubClassCat=sapply(MSSubClass,toString)),with=TRUE]
# Deal with missing values
test[which(is.na(LotFrontage)),'LotFrontage'] <- LotFrontage_mean
test=cbind(test,"IsGarage"=1+numeric(nrow(test)))
test[which(is.na(GarageYrBlt)),'GarageYrBlt'] <- 1900
#test.sample[which(is.na(GarageQual)),'IsGarage'] <- 0
test[which(is.na(MasVnrArea)),'MasVnrArea'] <- 0
test[which(is.na(BsmtCond)),'BsmtCond'] <- 'MISSING'
test[which(is.na(BsmtFinType1)),'BsmtFinType1'] <- 'MISSING'
test[which(is.na(BsmtFinType2)),'BsmtFinType2'] <- 'MISSING'
test[which(is.na(BsmtFinSF1)),'BsmtFinSF1'] <- 0
test[which(is.na(BsmtFinSF2)),'BsmtFinSF2'] <- 0
test[which(is.na(TotalBsmtSF)),'TotalBsmtSF'] <- 0
test[which(is.na(GarageCars)),'GarageCars'] <- 0
test[which(is.na(GarageArea)),'GarageArea'] <- 0
test[which(is.na(BsmtUnfSF)),'BsmtUnfSF'] <- 0
test[which(is.na(BsmtFullBath)),'BsmtFullBath'] <- 0
test[which(is.na(BsmtHalfBath)),'BsmtHalfBath'] <- 0
test[which(is.na(MSZoning)),'MSZoning'] <- 'RL'
test[which(is.na(SaleType)),'SaleType'] <- 'Oth'
test[which(is.na(Exterior1st)),'Exterior1st'] <- 'Other'
test[which(is.na(Exterior2nd)),'Exterior2nd'] <- 'Other'
test[which(is.na(Functional)),'Functional'] <- 'Typ'
test$BsmtBaths = test$BsmtFullBath + test$BsmtHalfBath
test$TotalBsmtSF_on_GrLivArea = test$TotalBsmtSF/test$GrLivArea
#train.kept = train[,kept_features,with=FALSE]
#test.kept = test[,kept_features[-which(kept_features=="SalePrice")],with=FALSE]
# predict test with lvl0 SVR and gbm models
#---------------------------------
# SVR
test.sample_svr = data.table(scale(test[,kept_features_svr,with=FALSE])) # don't forget to scale
svrFit_a.test_preds = predict(svrFit_a$finalModel,newdata=test.sample_svr)
#---------------------------------
# gbm
# Separate numeric and categorical features for conversion (as numeric and factor)
test.sample.num_features_gbm = test[,kept_num_features_gbm,with=FALSE]
test.sample.cat_features_gbm = test[,kept_cat_features_gbm,with=FALSE]
# Change class of data and merge back numeric and categorical
test.sample.num_features_gbm.tp = test.sample.num_features_gbm[,lapply(.SD,as.numeric)]
test.sample.cat_features_gbm.tp = test.sample.cat_features_gbm[,lapply(.SD,as.factor)]
test.sample_gbm = cbind(test.sample.num_features_gbm.tp,test.sample.cat_features_gbm.tp)
gbmFit_a.test_preds = predict(gbmFit_a$finalModel,newdata=test.sample_gbm,n.trees=1950)
# build lvl1 test set
test_2.sample = data.table(test_preds_svr=svrFit_a.test_preds,test_preds_gbm=gbmFit_a.test_preds)
# predict test with lvl1 gbm aka gbmFit_2
gbmFit_2.test_preds = predict(gbmFit_2$finalModel,newdata=test_2.sample,n.trees=165)
# write submission file
test.sample_submission = fread('~/kaggle/house_prices/data/sample_submission.csv')
test.sample_submission = test.sample_submission[,.(Id)]
test.sample_submission.new = cbind(test.sample_submission,SalePrice=gbmFit_2.test_preds)
write.csv(test.sample_submission.new,'~/kaggle/house_prices/data/my_submission_stacked.csv',row.names=FALSE)
# leaderboard score = 0.13956 (with train_a=60% of dataset)
|
71768805f8dbbd444da86772f916489c730959db
|
2e3c6f281490f908608c19e1841fdfdbeb081c21
|
/stuff.R
|
7f200f2a7cf1c26a52d07300b38499777f693b79
|
[] |
no_license
|
bdeonovic/binsum1
|
ad81ccf90be3097c6a929946f92855c9a68aa5a9
|
a9f501e4aeaf5dc49fdd9c88153392ef029e71c9
|
refs/heads/master
| 2020-04-25T06:54:55.202443
| 2017-07-13T19:50:04
| 2017-07-13T19:50:04
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,228
|
r
|
stuff.R
|
# stuff
# Stirling2 is (n,m) where m is upstairs in brackets
# ie first parameter greater than second
Stirling2(4,2)
choose(4,2)
clji(6,5,6)
ans=data.frame()
for (l in 1:6) {
for (j in 1:l) {
for (i in j:l) {
ans=rbind(ans,c(l=l,j=j,i=i,clji=clji(l,j,i)))
}
}
}
ans
kolmo(c(2,3),c(0.2,0.1),5,0.1)
kolmo2(c(2,3),c(0.2,0.1),5,0.1)
kolmogorov(c(2,3),c(0.2,0.1))
# examples in paper
# example 1
n=rep(5,5)
p=seq(0.02,0.10,0.02)
n
p
kolmogorov(n,p)
# check
# example 2
n=rep(100,5)
p=seq(0.01,0.03,0.005)
n
p
kk=kolmogorov(n,p)
kk %>% filter(s==19)
# check; checked several
# example 3
n=seq(500,100,-100)
p=1/n
n
p
kk=kolmogorov(n,p)
kk %>% filter(s==5)
kk %>% filter(s==8)
kk %>% filter(s==10)
kk %>% filter(s==14)
# example 4
n=seq(50,250,50)
p=seq(0.1,0.5,0.1)
n
p
kk=kolmogorov(n,p)
kk %>% filter(s==275)
kk %>% filter(s==296)
kk %>% filter(s==305)
kk %>% filter(s==320)
# example 5
n=c(3,6,2,7)
p=c(0.016,0.071,0.093,0.035)
n
p
kk=kolmogorov(n,p)
kk %>% filter(s==0)
kk %>% filter(s==3)
kk %>% filter(s==5)
# example 5
n=c(12,14,4,2,20,17,11,1,8,11)
p=c(0.074,0.039,0.095,0.039,0.053,0.043,0.067,0.018,0.099,0.045)
n
p
kk=kolmogorov(n,p)
kk %>% filter(s==0)
kk %>% filter(s==5)
kk %>% filter(s==8)
|
a17bc682a2aaf5955f8b7fb31007ccfe1b03f84d
|
fea181071db54de2be82d3d669e7c8048400a84b
|
/Assignment 2.R
|
fedf153c277ada871baffe2cbabdab1ccbcf8d00
|
[] |
no_license
|
CoHae/First-Repository-USF-R-Class
|
16c5e03205588d3a7a9ccce7b2eccd2ee2be04d4
|
997d21c8ac8a7e7941c52eff618237e2000dc03a
|
refs/heads/master
| 2020-12-10T17:27:53.385416
| 2020-04-04T16:25:23
| 2020-04-04T16:25:23
| 233,659,658
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 459
|
r
|
Assignment 2.R
|
# original/faulty Assignment 2
# it shows that neither "assignment" nor "someData" exist
assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22)
myMean <- function(assignment2) { return(sum(assignment)/length(someData)) }
myMean(assignment2)
someData
# corrected code for Assignment 2
assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22)
myMean <- function(assignment2) { return(sum(assignment2)/length(assignment2)) }
myMean(assignment2)
|
e23ecb72cacc054bf9e5641b7ae0dd0d3c6e95cf
|
8f7320c10f2c5fc8475753dc5256d1a66067e15c
|
/rkeops/man/ternaryop.LazyTensor.Rd
|
79e4fd52c367bfd2900deb4aabd33ec5edb75a31
|
[
"MIT"
] |
permissive
|
getkeops/keops
|
947a5409710379893c6c7a46d0a256133a6d8aff
|
52ed22a7fbbcf4bd02dbdf5dc2b00bf79cceddf5
|
refs/heads/main
| 2023-08-25T12:44:22.092925
| 2023-08-09T13:33:58
| 2023-08-09T13:33:58
| 182,054,091
| 910
| 69
|
MIT
| 2023-09-03T20:35:44
| 2019-04-18T09:04:07
|
Python
|
UTF-8
|
R
| false
| true
| 1,865
|
rd
|
ternaryop.LazyTensor.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lazytensor_preprocess.R
\name{ternaryop.LazyTensor}
\alias{ternaryop.LazyTensor}
\title{Build a ternary operation}
\usage{
ternaryop.LazyTensor(x, y, z, opstr, dim_check_type = "sameor1", dim_res = NA)
}
\arguments{
\item{x}{A \code{LazyTensor}, a \code{ComplexLazyTensor}, a vector of numeric values,
or a scalar value.}
\item{y}{A \code{LazyTensor}, a \code{ComplexLazyTensor}, a vector of numeric values,
or a scalar value.}
\item{z}{A \code{LazyTensor}, a \code{ComplexLazyTensor}r, a vector of numeric values,
or a scalar value.}
\item{opstr}{A text string corresponding to an operation.}
\item{dim_check_type}{A string to specify if, and how, we should check input
dimensions.
Supported values are:
\itemize{
\item {\strong{"same"}:}{ \strong{x} and \strong{y} should have the same inner dimension;}
\item {\strong{"sameor1"} (default):}{ \strong{x} and \strong{y} should have the same
inner dimension or at least one of them should be of dimension 1;}
\item {\strong{NA}:}{ no dimension restriction.}
}}
\item{dim_res}{NA (default) or an integer corresponding to the inner
dimension of the output \code{LazyTensor}. If NA, \strong{dim_res} is set to the
maximum between the inner dimensions of the three input \code{LazyTensor}s.}
}
\value{
An object of class "LazyTensor".
}
\description{
Symbolically applies \strong{opstr} operation to \strong{x}, \strong{y} and \strong{z}.
}
\examples{
\dontrun{
# basic example
D <- 3
M <- 100
N <- 150
P <- 200
x <- matrix(runif(M * D), M, D)
y <- matrix(runif(N * D), N, D)
z <- matrix(runif(P * D), P, D)
x_i <- LazyTensor(x, index = 'i')
y_j <- LazyTensor(y, index = 'j')
z_i <- LazyTensor(z, index = 'i')
# symbolic matrix:
tern_xyz <- ternaryop.LazyTensor(x_i, y_j, z_i, "IfElse")
}
}
\author{
Chloe Serre-Combe, Amelie Vernay
}
|
cea2b0ede00b21627fbb3d5d5ce963f294a20af9
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/traitdataform/examples/cast.traitdata.Rd.R
|
38ed9c56d63859476c8762bceb44e44f7d3824cf
|
[] |
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
| 1,056
|
r
|
cast.traitdata.Rd.R
|
library(traitdataform)
### Name: cast.traitdata
### Title: Cast long-table trait data into wide-table format
### Aliases: cast.traitdata
### ** Examples
pulldata("arthropodtraits")
head(arthropodtraits)
dataset3 <- as.traitdata(arthropodtraits,
taxa = "SpeciesID",
traits = c("Body_Size", "Dispersal_ability",
"Feeding_guild","Feeding_guild_short",
"Feeding_mode", "Feeding_specialization",
"Feeding_tissue", "Feeding_plant_part",
"Endophagous_lifestyle", "Stratum_use",
"Stratum_use_short"),
units = c(Body_Size = "mm"),
keep = c(measurementRemark = "Remark"),
metadata = as.metadata(
license = "http://creativecommons.org/publicdomain/zero/1.0/"
)
)
head(dataset3)
dd3 <-cast.traitdata(dataset3)
head(dd3)
|
2dd02c35be9e211e0f4a5cf903af2c9e32509371
|
8823b744fa8328268704c81fcbd23644cc65a271
|
/R/simulate_data.R
|
78db8dcabc88f0e74b7ad67d418d1d3e611cc3e5
|
[
"MIT"
] |
permissive
|
zosob/RiskAssessment
|
53c1b21f499fd2e82d1ab56096dbfbf2f8a81c0b
|
412a4c4d1f89fbe011e185f26b4f22f379bbb23b
|
refs/heads/master
| 2022-01-26T20:17:22.056667
| 2019-07-18T09:05:10
| 2019-07-18T09:05:10
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 14,522
|
r
|
simulate_data.R
|
#' @title Simulate Data
#' @description Function that simulate complete data, incomplete data and also approximate the top event probability distribution by simulation.
#' The data is a matrix type in R with the number of rows equal to the number of observation and the number
#' of columns equal to the number of nodes in the tree. The primary nodes must be listed as
#' the first columns and the top event node must be the last column.
#'
#' Each element of the matrix is either a 1 (that event was observed to occur), 0 (that event
#' was observed not to occur) or NA (that event was not observed)
#' @param n_simulated_data: number of observations to be generated.
#' @param tree_definition is the fault tree structure to be used, as defined above in the create_fault_tree function.
#' @param data_type: is one of "complete" (all nodes are observed), "top_only" (only the top node is
#' observed, all others are NA) or "incomplete" (each node is randomly observed with
#' probability p_obs, otherwise it is NA)
#' @param true_primary_p is an array of the true probabilities of each primary event occurring.
#' Obviously must be of the same dimension as the number of primary events in tree.
#' @param p_obs: in the case that data_type="incomplete", it is the probability that a node is observed.
#' @return A Matrix with number of rows equal to the number of simulated data and number of columns equal to the number of nodes in the tree.
#' @examples data <- simulate_data(n_simulated_data = 5,
#' tree_definition = tree,
#' data_type = "complete",
#' true_primary_p = c(0.02,0.05,0.05,0.1),
#' p_obs = 0.5)
#' @export simulate_data
# ####################################################################################################################################
# Monte Carlo simulation functions
# Contains routines that simulate complete data, incomplete data and also approximate the top event probability distribution by simulation
# ####################################################################################################################################
# ###############################################################################################################
# Data
# Either it's in the matrix data or, if we're simulating data, simulate it now
# ###############################################################################################################
simulate_data <- function (n_simulated_data, tree_definition, data_type, true_primary_p, p_obs) {
if (data_type == "top_only") { # Simulate data that consists of the top event only
data <- simulate_top_event_data(n=n_simulated_data,p=true_primary_p,tree_definition)
} else if (data_type=="incomplete") { # Simulate data where each event is observed independently with probability p_obs
data <- simulate_incomplete_data(n=n_simulated_data,p=true_primary_p,p_obs=p_obs,tree_definition)
} else if (data_type=="intermediate") { # Simulate data that consists of the intermediate and top events only
data <- simulate_intermediate_data(n=n_simulated_data,p=true_primary_p,tree_definition)
} else { # Simulate data where all events observed
data <- simulate_complete_data(n=n_simulated_data,p=true_primary_p,tree_definition)
}
return (data)
}
# ####################################################################################################################################
# simulate_complete_data
# ####################################################################################################################################
# Simulate complete data
# Simulate n values. Each value is a set of indept. Bernoulli values with sucess probabilities given in the vector p
# ####################################################################################################################################
simulate_complete_data <- function (n, p, tree) {
data <- matrix(NA,nrow=n,ncol=tree$n_nodes)
# Simulate the primary events
for (i in 1:tree$n_primary) {
data[,i] <- rbinom(n,size=1,prob=p[i])
}
# Infer the values of the intermediate and top events
start <- tree$n_primary+1
for (j in start:tree$n_nodes) {
if (tree$nodes[[j]]$Logic == "and") {
data[,j] <- and_logic (d=data, child=tree$children[(j-tree$n_primary),])
}
else {
data[,j] <- or_logic (d=data, child=tree$children[(j-tree$n_primary),])
}
}
return(data)
}
# ####################################################################################################################################
# ####################################################################################################################################
# simulate_intermediate_data
# ####################################################################################################################################
# Simulate data from all non-primary e.g. intermediate and top events
# Simulate n values. Each value is a set of indept. Bernoulli values with sucess probabilities given in the vector p
# ####################################################################################################################################
simulate_intermediate_data <- function(n, p, tree) {
data <- matrix(NA,nrow=n,ncol=tree$n_nodes)
# Simulate the primary events
for (i in 1:tree$n_primary) {
data[,i] <- rbinom(n,size=1,prob=p[i])
}
# Infer the values of the intermediate and top events
start <- tree$n_primary+1
for (j in start:tree$n_nodes) {
if (tree$nodes[[j]]$Logic == "and") {
data[,j] <- and_logic (d=data, child = tree$children[(j-tree$n_primary),])
}
else {
data[,j] <- or_logic (d=data, child = tree$children[(j-tree$n_primary),])
}
}
data[,1:tree$n_primary] <- NA
return(data)
}
# ####################################################################################################################################
# simulate_incomplete_data
# ####################################################################################################################################
# Simulate incomplete data with each observation observed with probability p_obs
# Simulate n values. Each value is a set of indept. Bernoulli values with sucess
# probabilities given in the vector p
# ####################################################################################################################################
simulate_incomplete_data <- function(n,p,p_obs,tree) {
# Generate complete data
data <- matrix(NA,nrow=n,ncol=tree$n_nodes)
for (i in 1:length(p)) {
data[,i] <- rbinom(n,size=1,prob=p[i])
}
# Each element of the matrix is observed with probability p_obs, otherwise it is replaced by NA
start <- tree$n_primary+1
for (j in start:tree$n_nodes) {
if (tree$nodes[[j]]$Logic == "and") {
data[,j] <- and_logic (d=data, child = tree$children[(j-tree$n_primary),])
}
else {
data[,j] <- or_logic (d=data, child = tree$children[(j-tree$n_primary),])
}
}
# Each element of the matrix is observed with probability p_obs, otherwise it is replaced by NA
observed_flag <- matrix(rbinom(n*tree$n_nodes,size=1,prob=p_obs),nrow=n)
# Replace unobserved elements with NA
data[observed_flag==0] <- NA
return(data)
}
# ####################################################################################################################################
# ####################################################################################################################################
# simulate_top_event_data
# ####################################################################################################################################
# Simulate data where the top event only is observed
# Simulate n values. Each value is a set of indept. Bernoulli values with sucess probabilities given in the vector p
# ####################################################################################################################################
simulate_top_event_data <- function(n, p, tree) {
# Generate complete data
data <- matrix(NA,nrow=n,ncol=tree$n_nodes)
# Simulate the primary events
for (i in 1:length(p)) {
data[,i] <- rbinom(n,size=1,prob=p[i])
}
# Infer the values of the intermediate and top events
start <- tree$n_primary+1
for (j in start:tree$n_nodes) {
if (tree$nodes[[j]]$Logic == "and") {
data[,j] <- and_logic (d=data, child=tree$children[(j-tree$n_primary),])
}
else {
data[,j] <- or_logic (d=data, child = tree$children[(j-tree$n_primary),])
}
}
if (length(dim(data)[2])==0) { # Is the tree just the top event?
data_top_event <- data
} else {
# Return n observations of the top event only with all other events recorded as NA
n_events <- dim(data)[2]
data_top_event <- cbind(matrix(NA,nrow=n,ncol=n_events-1),data[,n_events])
}
return(data_top_event)
}
# ####################################################################################################################################
# ####################################################################################################################################
# simulate_top_event_probability
# ####################################################################################################################################
# Simulate the top event probability prior in a fault tree
# Assume independent beta(2,2) priors on primary event probabilities
# Function inputs are: the number of simulations n_simulations to use, and a n x 2 matrix consisting of n beta parameter pairs,
# one for each of the n primary event probabilities. Output is a KDE of the prior density of the top event probability
# ####################################################################################################################################
simulate_top_event_probability <- function(n_simulations, beta_params, tree, x_limits, lty) {
x_min <- 0
x_max <- 1
simulated_p <- matrix(nrow=tree$n_nodes,ncol=n_simulations)
# Simulate the primary event probabilities from their beta prior
for (i in 1:tree$n_primary) {
simulated_p[i,] <- rbeta(n_simulations,shape1=beta_params[i,1],shape2=beta_params[i,2])
}
start <- tree$n_primary+1
for (j in start:tree$n_nodes){
if (tree$nodes[[j]]$Logic == "and") {
simulated_p[j,] <- calculate_and_scalar(child=tree$children[(j-tree$n_primary),], mysim=simulated_p)
}
else {
simulated_p[j,] <- calculate_or_scalar(child=tree$children[(j-tree$n_primary),], mysim=simulated_p)
}
}
# Create an estimate of the density of the top event probability (which we assume is the last-indexed event)
p_top <- density(simulated_p[tree$n_nodes,],from=x_min,to=x_max)
plot_y_max <- max(p_top$y)
par(mfrow=c(1,1))
plot(p_top$x,p_top$y,type="l",xlab="TOP EVENT PROBABILITY",ylab="DENSITY",main="",
xlim=x_limits,ylim=c(0,1.05*plot_y_max),lwd=2,lty=lty)
grid()
cat("Prior probability of top event:","\n")
cat(" Mean is", mean(simulated_p[tree$n_nodes,]), "\n") # modified for ATV example
cat(" Standard deviation is", sd(simulated_p[tree$n_nodes,]), "\n")
cat(" Central 95% probability interval is (",quantile(simulated_p[tree$n_nodes,],0.025),
", ",quantile(simulated_p[tree$n_nodes,],0.975),")", "\n",sep="")
}
# ###########################################################################################################
# ###########################################################################################################
# and_logic. Performs the AND function of all the children of a node.
# ###########################################################################################################
and_logic <- function(d, child) {
first<-TRUE
for (k in 1:(ncol(d)-1)) {
if (child[k]==1) {
if (first==TRUE) {
aux<-d[,k]
first<-FALSE
}
else {
aux <- aux & d[,k]
}
}
}
return (as.integer(aux))
}
# ################################################################################
# or_logic. Performs the OR function of all the children of a node.
# ################################################################################
or_logic <- function(d, child) {
first<-TRUE
for (k in 1:(ncol(d)-1)) {
if (child[k]==1) {
if (first==TRUE) {
aux<-d[,k]
first<-FALSE
}
else {
aux <- aux | d[,k]
}
}
}
return (as.integer(aux))
}
# #####################################################################################################
# and_scalar. Calculates the probability of a "and" tree node based its children's probabilities
# #####################################################################################################
calculate_and_scalar <- function(child, mysim){
if (length(dim(mysim))==0) { # just passing a vector of probabilities
just_a_vector <- TRUE
} else { # else we're passing a matrix
just_a_vector <- FALSE
}
aux <- 1
for (j in 1:length(child)) {
if (child[j]==1) {
if (just_a_vector) {
aux <- aux * mysim[j]
} else {
aux <- aux * mysim[j,]
}
}
}
return (aux)
}
# #####################################################################################################
# and_scalar. Calculates the probability of a "or" tree node based its children's probabilities.
# #####################################################################################################
calculate_or_scalar <- function(child, mysim){
if (length(dim(mysim))==0) { # just passing a vector of probabilities
just_a_vector <- TRUE
} else { # else we're passing a matrix
just_a_vector <- FALSE
}
aux <- 1
for (j in 1:length(child)) {
if (child[j]==1) {
if (just_a_vector) {
aux <- aux * (1-mysim[j])
} else {
aux <- aux * (1-mysim[j,])
}
}
}
aux <- (1-aux)
return (aux)
}
|
c4910451f175881fe1f6a54d71053db446ae261f
|
8b2a91990c0d78af91ccae2939985bf0a1eed858
|
/part3.R
|
31de6151b9b8e28e0729e8ba0ceb0d75e6f27020
|
[] |
no_license
|
i94u/Lab1_603410031
|
ceaae5ff38196e0dbfb4e90947396454bff11250
|
bacf8a73c6b774c49184163c2f9d106e4878f5c4
|
refs/heads/master
| 2021-01-10T10:13:02.200693
| 2015-09-24T04:08:19
| 2015-09-24T04:08:19
| 43,038,216
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 480
|
r
|
part3.R
|
Wingcrd <- c(59, 55, 53.5, 55, 52.5, 57.5, 53, 55)
mean(Wingcrd); median(Wingcrd); min(Wingcrd); max(Wingcrd)
Tarsus <- c(22.3, 19.7, 20.8, 20.3, 20.8, 21.5, 20.6, 21.5)
mean(Tarsus); median(Tarsus); min(Tarsus); max(Tarsus)
Head <- c(31.2, 30.4, 30.6, 30.3, 30.3, 30.8, 32.5, NA)
mean(Head, na.rm = TRUE); median(Head, na.rm = TRUE); min(Head, na.rm = TRUE); max(Head, na.rm = TRUE)
Wt <- c(9.5, 13.8, 14.8, 15.2, 15.5, 15.6, 15.6, 15.7)
mean(Wt); median(Wt); min(Wt); max(Wt)
|
2ec01c7a58c12b7dd8eeda96b25b00e19e2a29a3
|
f3dffcb0cd531bb61c12e68e38dc8b4d6192d4c0
|
/plot1.R
|
6f40d08b4a102e0ac2533150615045388f6e0f26
|
[] |
no_license
|
joekieffer/EDA---PM2.5
|
e9d8e81f9beae6d5702005ece013e520de81e846
|
be9d5a210fc1997c033b1bae7df56a5686d1e89e
|
refs/heads/master
| 2021-01-10T21:11:11.771063
| 2014-10-26T18:44:19
| 2014-10-26T18:44:19
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 314
|
r
|
plot1.R
|
#importing data
source('Class_Project2.R')
#data manipulation
totalE <- aggregate(Emissions ~ year, NEI, sum)
#printing of plot
png(file="plot1.png", bg="white")
barplot(height=totalE$Emissions, names.arg=totalE$year, ylab="Total emissions", xlab="Years",main="Total fine particulate matter emission")
dev.off()
|
d765ec52059a123c4e9060ee9fcaccd7f36d9684
|
37cbbbbfc95eda55dc99f5637b39dba59bbddc6a
|
/tests/testthat/test_dCModel.R
|
c27c32194d952f86407f1137f31098f20f7538b4
|
[
"MIT"
] |
permissive
|
djinnome/rtedem
|
f2f080e1eabfc01b3e448ac610aee16c6d12b547
|
7a3232d46410e9f29b42209165a990ce1e0bb934
|
refs/heads/master
| 2021-01-12T13:17:07.189860
| 2016-09-24T16:11:10
| 2016-09-24T16:11:10
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,705
|
r
|
test_dCModel.R
|
# Testing code for the RCMIP5 'dCModel.R' script
# Uses the testthat package
# See http://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf
library(testthat)
# To run this code:
# source("R/dCModel.R")
# library(testthat)
# test_file("tests/testthat/test_dCModel.R")
context("dCModel")
test_that('dCModel produces expected errors',{
expect_error(dCModel(t=0, parms=c(a=1, b=2), reactionNetwork=data.frame(from=c('C1'), to='C2', reaction=c('a+c'))))
expect_error(dCModel(t=0, parms=c(a=1, b=2), reactionNetwork=data.frame(from=c('C1'), to='C2', reaction=c('a+C3'))))
})
test_that('dCModel runs with and without factos', {
reactionNetwork1 <- data.frame(from=c('C1', 'C1', 'C2', 'C2'),
to=c(NA, 'C2', NA, 'C1'),
reaction=c('1/tau1*(1-trans2)*C1', '1/tau1*trans2*C1',
'1/tau2*(1-trans3)*C2', '1/tau2*trans3*C2'),
type=c('decay', 'transfer', 'decay', 'transfer'),
stringsAsFactors=FALSE)
reactionNetwork2 <- data.frame(from=c('C1', 'C1', 'C2', 'C2'),
to=c(NA, 'C2', NA, 'C1'),
reaction=c('1/tau1*(1-trans2)*C1', '1/tau1*trans2*C1',
'1/tau2*(1-trans3)*C2', '1/tau2*trans3*C2'),
type=c('decay', 'transfer', 'decay', 'transfer'),
stringsAsFactors=TRUE)
y <- c(C1=1, C2=3)
parms <- c(tau1=10, tau2=100, trans2=0.5, trans3=0.1)
expect_equal(dCModel(t=0, y=y, parms=parms, reactionNetwork=reactionNetwork1),
dCModel(t=0, y=y, parms=parms, reactionNetwork=reactionNetwork2))
})
test_that('dCModel reproduces a simple first order model', {
expect_equal(dCModel(t=0), list(unlist(list(C1=-0.097, C2=0.020))))
reactionNetwork <- data.frame(from=c('C1', 'C1', 'C2', 'C2'),
to=c(NA, 'C2', NA, 'C1'),
reaction=c('1/tau1*(1-trans2)*C1', '1/tau1*trans2*C1',
'1/tau2*(1-trans3)*C2', '1/tau2*trans3*C2'),
type=c('decay', 'transfer', 'decay', 'transfer'),
stringsAsFactors=FALSE)
y <- c(C1=1, C2=3)
parms <- c(tau1=10, tau2=100, trans2=0.5, trans3=0.1)
decayMatrix <- matrix(c(-1/parms['tau1'], 1/parms['tau1']*parms['trans2'],
1/parms['tau2']*parms['trans3'], -1/parms['tau2']), nrow=2)
ans <- as.numeric(t(decayMatrix%*%matrix(y, nrow=2)))
names(ans) <- c('C1', 'C2')
expect_equal(list(ans),
dCModel(t=0, y=y, parms=parms, reactionNetwork=reactionNetwork))
})
test_that('dCModel produces expected errors',{
expect_error(dCModel(parms=list(not=1, real=2), t=1, y=c(1,1,1,1)))
expect_error(dCModel(parms=list(not=1, real=2), t=1, y=1))
par <- unlist(list('v_enz'=0.2, 'km_enz'=10,
'v_up'=1, 'km_up'=2,
'cue'=0.5, 'basal' = 0.01,
'turnover_b'=0.5, 'turnover_e'=0.1))
expect_error(dCModel(parms=par, t=1, y=1))
par <- unlist(list('v_enz'=0.2, 'km_enz'=10, turnover_e=0.1))[1:2]
y0 <- unlist(list(simple=1, complex=2, enzyme=3))
poolAssignment <- list(simple=1, complex=2, enzyme=3)
expect_error(dCModel(parms=par, y=y0, rateFlags=list(enz='MM'), poolAssignment=poolAssignment))
})
test_that('dC.biomassModel returns correct enzyme kinetics',{
par <- unlist(list('v_enz'=0.2, 'km_enz'=10, turnover_e=0.1))
y0 <- unlist(list(simple=1, complex=2, enzyme=3))
renet <- data.frame(from=c('complex', 'enzyme'),
to=c('simple', 'complex'),
reaction=c('complex*enzyme*v_enz/(km_enz+complex)',
'turnover_e*enzyme'), stringsAsFactors=FALSE)
expect_equal(unlist(dCModel(t=0, y=y0, parms=par,reactionNetwor=renet )),
unlist(list(simple=as.numeric(par['v_enz']*y0['complex']*y0['enzyme']/(par['km_enz']+y0['complex'])),
complex=as.numeric(par['turnover_e']*y0['enzyme']-par['v_enz']*y0['complex']*y0['enzyme']/(par['km_enz']+y0['complex'])),
enzyme=as.numeric(-par['turnover_e']*y0['enzyme']))))
renet <- data.frame(from=c('complex', 'enzyme'),
to=c('simple', 'complex'),
reaction=c('complex*enzyme*v_enz/(km_enz+enzyme)',
'turnover_e*enzyme'), stringsAsFactors=FALSE)
expect_equal(unlist(dCModel(t=0, y=y0, parms=par,reactionNetwor=renet )),
unlist(list(simple=as.numeric(par['v_enz']*y0['complex']*y0['enzyme']/(par['km_enz']+y0['enzyme'])),
complex=as.numeric(par['turnover_e']*y0['enzyme']-par['v_enz']*y0['complex']*y0['enzyme']/(par['km_enz']+y0['enzyme'])),
enzyme=as.numeric(-par['turnover_e']*y0['enzyme']))))
par <- unlist(list('v_enz'=0.2, turnover_e=0.1))
renet <- data.frame(from=c('complex', 'enzyme'),
to=c('simple', 'complex'),
reaction=c('complex*enzyme*v_enz',
'turnover_e*enzyme'), stringsAsFactors=FALSE)
expect_equal(unlist(dCModel(t=0, y=y0, parms=par,reactionNetwor=renet )),
unlist(list(simple=as.numeric(par['v_enz']*y0['complex']*y0['enzyme']),
complex=as.numeric(par['turnover_e']*y0['enzyme']-par['v_enz']*y0['complex']*y0['enzyme']),
enzyme=as.numeric(-par['turnover_e']*y0['enzyme']))))
})
|
6531935d91b9eb1d701f9f649be1fa229e34fc0c
|
95a0aaef3033adc33dee58dc00742c526bf67f95
|
/RProgramming/Assignment2/cachematrix.R
|
b7636cfb42d96aac18a5ddec4242eaaa191c7520
|
[
"MIT"
] |
permissive
|
skyguy94/datasciencecoursera
|
9add9f8e6df3e0837e8817f99ad2bfd4a8f91d39
|
236892664c25f65f8f45c1290aa84d137e1e890d
|
refs/heads/master
| 2021-01-10T04:11:51.147895
| 2015-08-23T22:27:33
| 2015-08-23T22:27:33
| 36,816,880
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,094
|
r
|
cachematrix.R
|
## This code attempts to optimize the handling the computation of inverse
## matrices by caching the result of previous computations in memory
## and checking that cache before computing an existing result.
## This function creates a special "matrix" object that
## can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inverse <- NULL
set <- function(y) {
x <<- y
inverse <<- NULL
}
get <- function() x
setinverse <- function(rval) inverse <<- rval
getinverse <- function() inverse
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## This function computes the inverse of the special "matrix"
## returned by makeCacheMatrix above. If the inverse has already m
## been calculated (and the matrix has not changed), then the
## cacheSolve should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
inverse <- x$getinverse()
if(!is.null(inverse)) {
message("getting cached data")
return (inverse)
}
data <- x$get()
inverse <- solve(data)
x$setinverse(inverse)
inverse
}
|
d428d73de4c8da9a9f63d56a80d58cbe81d3440c
|
2bee25fa7cd8961eed2336183284b768a575e4d6
|
/R/plot.samplesize.R
|
3a729796b1f713f556560230b0ec927ed5476412
|
[] |
no_license
|
annaheath/EVSI
|
d2a6adb3a15b4013d3e7a82de815e51a016377a3
|
11accaca10816c0eb0cc32cdb0ae74829ed41c01
|
refs/heads/master
| 2022-07-18T02:00:08.761837
| 2022-06-24T13:02:55
| 2022-06-24T13:02:55
| 102,010,781
| 8
| 1
| null | 2019-03-21T20:17:17
| 2017-08-31T14:40:52
|
R
|
UTF-8
|
R
| false
| false
| 3,468
|
r
|
plot.samplesize.R
|
##plot.samplesize###########################################################
plot.samplesize <- function(evsi,wtp=NULL,pos=c("bottomright"),CI=NULL){
##'Calculating the EVSI for a specific WTP giving the uncertainty bands across the different
##'samples sizes
##INPUTS
##'@param evsi Output of the comp.evsi.N function
##'@param wtp The willingness to pay value that the graphic should be produced for - it will
##' be chosen if wtp=NULL.
##'@param pos The position where the legend will be printed (default='bottomright')
##'@param CI The indexes that we would like to take from the CI in the evsi object.
##'
##OUTPUTS
##'@return EVSI The EVSI calculated for a specific wtp with uncertainty estimates.
##'@return A graphical representation of the uncertainty.
alt.legend <- pos
if (is.numeric(alt.legend) & length(alt.legend) == 2) {
temp <- ""
if (alt.legend[2] == 0)
temp <- paste0(temp, "bottom")
else if (alt.legend[2] != 0.5)
temp <- paste0(temp, "top")
if (alt.legend[1] == 1)
temp <- paste0(temp, "right")
else temp <- paste0(temp, "left")
alt.legend <- temp
if (length(grep("^((bottom|top)(left|right)|right)$",
temp)) == 0)
alt.legend <- FALSE
}
if (is.logical(alt.legend)) {
if (!alt.legend)
alt.legend = "topright"
else alt.legend = "topleft"
}
#Pick wtp threshold if not selected.
if(class(wtp)!="numeric"){
wtp.select<-which.min(abs(evsi$he$kstar-evsi$attrib$wtp))
wtp<-evsi$attrib$wtp[which.min(abs(evsi$he$kstar-evsi$attrib$wtp))]
}
if(class(wtp)=="numeric"){
wtp.select<-which.min(abs(wtp-evsi$attrib$wtp))
wtp<-evsi$attrib$wtp[which.min(abs(wtp-evsi$attrib$wtp))]
}
if(class(CI)=="numeric"){
CI.select<-CI
CI<-evsi$attrib$CI[CI]
}
if(is.null(CI)){
CI.select<-1:length(evsi$attrib$CI)
CI<-evsi$attrib$CI
}
if(class(evsi$attrib$N)=="character"){
stop("This plot gives the EVSI for increasing sample size. Do not use on a single design.")
}
CI.length<-length(CI)
#Extracting the EVSI values for the wtp of interest
EVSI<-array(NA,dim=c(length(evsi$attrib$N),1,CI.length))
EVSI[,1,(1:CI.length)]<-rbind(evsi$evsi[,wtp.select,CI.select])
#Set up the plot
plot(1,1,ylim=c(min(EVSI)*0.95,max(EVSI)*1.05),xlim=c(min(evsi$attrib$N),max(evsi$attrib$N)),
col="white",xlab=expression("Sample Size"),ylab="Per Person EVSI",oma=c(0,0,-1,0),main="Expected Value of Sample Information across Sample Size")
if(CI.length%%2==1){
lwd<-c(1:ceiling(CI.length/2),(ceiling(CI.length/2)-1):1,1)
lty<-c(ceiling(CI.length/2):1,2:ceiling(CI.length/2),1)
}
if(CI.length%%2==0){
lwd<-c(1:(CI.length/2),(CI.length/2):1,1)
lty<-c((CI.length/2):1,2:(CI.length/2),1)
}
if(length(evsi$attrib$N)<15){
for(l in 1:CI.length){
points(evsi$attrib$N,EVSI[,,l],pch=19,
lwd=lwd[l],lty=lty[l])
}
}
if(length(evsi$attrib$N)>=15){
for(l in 1:CI.length){
points(evsi$attrib$N,EVSI[,,l],type="l",
lwd=lwd[l],lty=lty[l])
}
}
fitted.PI<--(wtp*evsi$evppi$fitted.e-evsi$evppi$fitted.c)
abline(h=mean(apply(fitted.PI,1,max))-max(apply(fitted.PI,2,mean)),col="springgreen",lwd=lwd[CI.length+1],lty=lty[CI.length+1])
legend(alt.legend,c(as.character(CI),"EVPPI"),
col=c(rep("black",CI.length),"springgreen"),lwd=lwd,lty=lty,
box.lwd = 0,box.col = "white",bg = "white")
box()
}
|
c4a83149e6d22334ebe3636808b34810dba0863a
|
af982fba9c4fab24bf06e810720de721a7c43bd2
|
/data-raw/weather data/make weather data.R
|
5735741262d425469607cfc692bd0c3f57f3cff2
|
[
"Apache-2.0"
] |
permissive
|
Schmidtpk/Covid
|
398dcc70bc12d91bb588939eedd3e7248c547e23
|
0efcfe093be2b8b66799930185b580bee1dfe529
|
refs/heads/master
| 2021-04-11T09:36:33.532750
| 2020-04-25T08:27:00
| 2020-04-25T08:27:00
| 249,008,243
| 2
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,079
|
r
|
make weather data.R
|
library(httr)
vars <- c("cloud","tMax","tMin","precip","humidity","wind")
df <- NULL
for(var.cur in vars)
{
df.cur <-as_tibble(read.csv(
text=as.character(
GET(
paste0("https://raw.githubusercontent.com/imantsm/COVID-19/master/csv/",
var.cur,
".csv")),
header=T)))
cat(var.cur,sum(grepl("Other",df.cur$Province.State)))
#sum(grepl("ther",df.cur$Province.State)>
df.cur.long <- df.cur %>% tidyr::pivot_longer(starts_with("X"),names_to = "date")
df.cur.long$date <- as.Date(df.cur.long$date, "X%m.%d.%y")
df.cur.long$var <- var.cur
df <- rbind(df.cur.long,df)
}
weather <- df %>%
rename(
region = Province.State,
country = Country.Region
)
weather <- weather %>%
tidyr::pivot_wider(
names_from = "var",
values_from = "value"
)
#summary(weather$region)
weather$country<-as.character(weather$country)
weather$region<-as.character(weather$region)
weather$region[weather$region==""]<-NA
weather$region[weather$region==weather$country] <- NA
#use_data(weather,overwrite = T)
|
b3d2ca6ad251c578d9327d6ce91a605c579e7fee
|
a55c6e1f121a7114d238437cb3ff7002e31c4d42
|
/tests/testthat/test-overscope.R
|
9a596f5a45f3b70744022303f5afc4ba9e656aee
|
[
"MIT"
] |
permissive
|
mohamedndiaye/dplyr
|
93af925b8144d462cbeb3d94ff5ca9b0e9c94b99
|
12e76215b01cea302d26d600a17549d5019026d3
|
refs/heads/master
| 2020-03-06T16:02:24.867469
| 2017-04-28T14:28:47
| 2017-04-28T14:28:47
| 126,966,112
| 1
| 1
| null | 2018-03-27T09:56:37
| 2018-03-27T09:56:36
| null |
UTF-8
|
R
| false
| false
| 309
|
r
|
test-overscope.R
|
context("overscope")
test_that(".data has strict matching semantics (#2591)", {
expect_error(
data_frame(a = 1) %>% mutate(c = .data$b),
"Column `b`: not found in data"
)
expect_error(
data_frame(a = 1:3) %>% group_by(a) %>% mutate(c = .data$b),
"Column `b`: not found in data"
)
})
|
aa53f30a8b749cced8084d6fe6f5fb375650623e
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/igraph/examples/vertex_attr.Rd.R
|
c3a9c580aabad670b314007a92f5b0edc3b7179a
|
[] |
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
| 337
|
r
|
vertex_attr.Rd.R
|
library(igraph)
### Name: vertex_attr
### Title: Query vertex attributes of a graph
### Aliases: vertex_attr get.vertex.attribute vertex.attributes
### ** Examples
g <- make_ring(10) %>%
set_vertex_attr("color", value = "red") %>%
set_vertex_attr("label", value = letters[1:10])
vertex_attr(g, "label")
vertex_attr(g)
plot(g)
|
8a0ccce6475661429a346bdbad4b25a2ea5ad341
|
1462094b01791141a5e21727aec8c15c205ee28f
|
/ui.R
|
dff75b82ecaf1903385d93694a3146a9d3c2ce49
|
[] |
no_license
|
dcarvalho/analise_dados_abertos
|
f668b58d007f8b316236c5d81aa048863d738237
|
c2b2cf6c9be48fa88111e4461720dd4db122e255
|
refs/heads/master
| 2020-04-15T11:49:49.358424
| 2019-04-03T17:59:10
| 2019-04-03T17:59:10
| 164,646,914
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,077
|
r
|
ui.R
|
library(shiny)
library(DT)
library(plotly)
d<- as.POSIXlt(Sys.Date())
data_fim<-d
d$year<-d$year-5
data_inicio<-as.Date(d)
shinyUI(
fluidPage(style = "text-align:center;",
# Application title
titlePanel("Matriz de Informação Social - Dados Abertos"),
fluidRow(style = "background-color:lightgray;text-align:left;",
column(7,
selectInput("campo",
"Indicador:",
choices = campos,
width = '100%'
)
) ,
column(2,
selectInput("uf",
"UF:",
choices = c(
'BRASIL'='*',
'AC'='12',
'AL'='27',
'AM'='13',
'AP'='16',
'BA'='29',
'CE'='23',
'DF'='53',
'ES'='32',
'GO'='52',
'MA'='21',
'MG'='31',
'MS'='50',
'MT'='51',
'PA'='15',
'PB'='25',
'PE'='26',
'PI'='22',
'PR'='41',
'RJ'='33',
'RN'='24',
'RO'='11',
'RR'='14',
'RS'='43',
'SC'='42',
'SE'='28',
'SP'='35',
'TO'='17'
)
)
) ,
column(3,
dateRangeInput('ano',label = "Período da análise: ",format = "yyyy",language="pt",start = data_inicio, end=data_fim,startview = "year",separator = " - ")
)
),
hr(),
fluidRow(style = "text-align:left;",
column(12,
plotlyOutput("plotly")
)
),
hr(),
fluidRow(style = "text-align:left;",
column(12,
dataTableOutput('tbl')
)
)
)
)
|
9d56c21c3bab72a41eb6987f6dfe53b8f96b1acf
|
e2f262ced6cc36bebd9ff8e142ca082f8904d8a2
|
/R/uscb_acs_5ye.R
|
c94aaec453f421ca14f2655ff94f8df234bdb343
|
[
"MIT"
] |
permissive
|
josesamos/geogenr
|
408949b2801df306ac8e0c209a09c737c11fbae3
|
448e4c09e052da5d34e2539fd5ad2735c4cbc8fc
|
refs/heads/master
| 2023-01-22T02:00:39.706397
| 2020-11-19T11:32:37
| 2020-11-19T11:32:37
| 303,603,346
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,061
|
r
|
uscb_acs_5ye.R
|
#' `uscb_acs_5ye` S3 class
#'
#' Internal low-level constructor that creates new objects with the correct
#' structure.
#'
#' @param folder A string.
#'
#' @importFrom magrittr %>%
#' @name %>%
#'
#' @return A `uscb_acs_5ye` object.
#'
#' @keywords internal
new_uscb_acs_5ye <- function(folder = "") {
years <- 2010:2018
url <- "https://www2.census.gov/geo/tiger/TIGER_DP/%dACS/"
extension <- ".gdb.zip"
legal_and_administrative_areas <-
data.frame(
type = 1,
name = c(
"American Indian/Alaska Native/Native Hawaiian Area",
"Alaska Native Regional Corporation",
"Congressional District (116th Congress)",
"County",
"Place",
"Elementary School District",
"Secondary School District",
"Unified School District",
"State",
"State Legislative Districts - Upper Chamber",
"State Legislative Districts - Lower Chamber",
"Zip Code Tabulation Area"
),
url = c(
"ACS_%d_5YR_AIARES",
"ACS_%d_5YR_ANRC",
"ACS_%d_5YR_CD_116",
"ACS_%d_5YR_COUNTY",
"ACS_%d_5YR_PLACE",
"ACS_%d_5YR_SDE",
"ACS_%d_5YR_SDS",
"ACS_%d_5YR_SDU",
"ACS_%d_5YR_STATE",
"ACS_%d_5YR_SLDU",
"ACS_%d_5YR_SLDL",
"ACS_%d_5YR_ZCTA"
)
)
statistical_areas <-
data.frame(
type = 2,
name = c(
"Tribal Block Group",
"Tribal Census Tract",
"New England City and Town Area",
"New England City and Town Area Division",
"Combined New England City and Town Area",
"Metropolitan/Micropolitan Statistical Area",
"Metropolitan Division",
"Combined Statistical Area",
"Public Use Microdata Area",
"Urban Area"
),
url = c(
"ACS_%d_5YR_TBG",
"ACS_%d_5YR_TTRACT",
"ACS_%d_5YR_NECTA",
"ACS_%d_5YR_NECTADIV",
"ACS_%d_5YR_CNECTA",
"ACS_%d_5YR_MSA",
"ACS_%d_5YR_METDIV",
"ACS_%d_5YR_CSA",
"ACS_%d_5YR_PUMA",
"ACS_%d_5YR_UA"
)
)
acs <-
list(
folder = folder,
years = years,
url = url,
extension = extension,
variables = rbind(legal_and_administrative_areas, statistical_areas)
)
structure(acs,
class = "uscb_acs_5ye")
}
#' `uscb_acs_5ye` S3 class
#'
#' A `uscb_acs_5ye` object is created from a given local folder.
#'
#' @param folder A string.
#'
#' @return A `uscb_acs_5ye` object.
#'
#' @family data collection functions
#' @seealso
#'
#' @examples
#'
#' folder <- "../geodimension/data/us/"
#' ua <- uscb_acs_5ye(folder = folder)
#'
#' folder <- system.file("extdata", package = "geogenr")
#' folder <- stringr::str_replace_all(paste(folder, "/", ""), " ", "")
#' ua <- uscb_acs_5ye(folder = folder)
#'
#' @export
uscb_acs_5ye <- function(folder = "") {
new_uscb_acs_5ye(folder)
}
# -----------------------------------------------------------------------
#' url_file_exists
#'
#' https://stackoverflow.com/questions/60318926/how-to-check-if-file-exists-in-the-url-before-use-download-file-in-r
#'
#' @param mdr A string.
#'
#' @return A boolean
#'
#' @keywords internal
url_file_exists <- function(url) {
head_url <- httr::HEAD(url)
(head_url$all_headers[[1]]$status == 200)
}
# -----------------------------------------------------------------------
#' get_geodatabase_url
#'
#'
#' @param mdr A string.
#'
#' @return A boolean
#'
#' @keywords internal
get_geodatabase_url <- function(url, extension, names, name, year) {
name <- names[names$name == name, "url"]
url <- paste(url, name, extension, sep = "")
sprintf(url, year, year)
}
# -----------------------------------------------------------------------
#' get_geodatabase_file
#'
#'
#' @param mdr A string.
#'
#' @return A boolean
#'
#' @keywords internal
get_geodatabase_file <- function(folder, extension, names, name, year) {
name <- names[names$name == name, "url"]
file <- paste(folder, name, extension, sep = "")
sprintf(file, year)
}
|
fb0a0a40d6f321941c78f0743c60a7e762585498
|
9ee587651e82c3efdf58036364c197829ffa57e1
|
/Chapter1_FineScaleAcousticSurvey/nmds_birds_v.laptopt.R
|
ef6f8da4081bbd470eefc41f87aea477d837cbd7
|
[
"Apache-2.0"
] |
permissive
|
QutEcoacoustics/spatial-acoustics
|
7f0fd2af6663200ab529a2f8979eec56a0bf2e40
|
5e8eaba29576a59f85220c8013d0b083ddb70592
|
refs/heads/master
| 2023-04-15T09:50:44.063038
| 2023-03-14T23:36:36
| 2023-03-14T23:36:36
| 222,621,976
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,105
|
r
|
nmds_birds_v.laptopt.R
|
#NMDS#
#Marina Scarpelli#
#07.01.2020#
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(stringi)
library(car)
library(data.table)
library(MuMIn)
library(plotly)
#Reading and preparing the data ####
getDataPath <- function (...) {
return(file.path("C:/Users/scarp/OneDrive - Queensland University of Technology/Documents/PhD/Project", ...))
}
chapter <- "Chapter1_FineScaleAcousticSurvey"
#NMDS to decrease number of land variables
library(vegan)
set.seed(123)
nmds_df <- read.csv(getDataPath(chapter, "27.02.2021_CompleteData.csv")) %>%
mutate(., period = case_when(hour > 4 & hour <= 11 ~ "morning",
hour > 11 & hour <= 19 ~ "afternoon",
T ~ "night"))
land_var <- read.csv(getDataPath("Fieldwork_Bowra", "26.02.2021_dataSAVINDVI_v.laptop.csv"))
land_var$aug_ndvi_avg <- as.numeric(land_var$aug_ndvi_avg)
land_var <- filter(land_var, NewVegDescription != "") %>%
mutate_at(., vars(NT_N_DIST, NT_W_DIST, NT_S_DIST, NT_E_DIST, NS_N_DIST, NS_W_DIST, NS_S_DIST, NS_E_DIST), ~ replace(., is.na(.), 100)) %>%
mutate_at(., vars(NT_N_HEIGHT, NT_S_HEIGHT, NT_W_HEIGHT, NT_E_HEIGHT, NS_N_HEIGHT, NS_S_HEIGHT, NS_E_HEIGHT, NS_W_HEIGHT), ~replace(., is.na(.), 0)) %>%
mutate_at(., vars(GC_NF_W, Slope, Aspect, Elevation, DistWater, CanopyCover, ShrubCover, CanopyHeight, SubcanopyHeight, aug_ndvi_avg, aug_savi_avg), ~replace(., is.na(.), 0)) %>%
mutate(NT_DIST_AVG = (NT_N_DIST + NT_S_DIST + NT_E_DIST + NT_W_DIST)/4) %>%
mutate(NT_HEIGHT_AVG = (NT_N_HEIGHT + NT_S_HEIGHT + NT_E_HEIGHT + NT_W_HEIGHT)/4) %>%
mutate(NS_DIST_AVG = (NS_N_DIST + NS_S_DIST + NS_E_DIST + NS_W_DIST)/4) %>%
mutate(NS_HEIGHT_AVG = (NS_N_HEIGHT + NS_S_HEIGHT + NS_E_HEIGHT + NS_W_HEIGHT)/4) %>%
mutate(GC_NG_AVG = (GC_NG_N + GC_NG_S + GC_NG_E + GC_NG_W)/4) %>%
mutate(GC_NF_AVG = (GC_NF_N + GC_NF_S + GC_NF_E + GC_NF_W)/4) %>%
mutate(GC_BS_AVG = (GC_BS_N + GC_BS_S + GC_BS_E + GC_BS_W)/4) %>%
mutate(GC_LT_AVG = (GC_LT_N + GC_LT_S + GC_LT_E + GC_LT_W)/4) %>%
mutate(GC_SH_AVG = (GC_SH_N + GC_SH_S + GC_SH_E + GC_SH_W)/4) %>%
select(., NT_DIST_AVG, NT_HEIGHT_AVG, NS_DIST_AVG, NS_HEIGHT_AVG, GC_NG_AVG, GC_NF_AVG, GC_BS_AVG, GC_SH_AVG, aug_ndvi_avg, CanopyCover, ShrubCover, CanopyHeight, SubcanopyHeight, Slope, Aspect, Elevation, DistWater, Point, NewVegDescription, VegDescription2) %>%
droplevels(.)
df_newveg1 <- select(land_var, Point, NewVegDescription, VegDescription2) %>%
merge(., nmds_df, by.x = "Point", by.y = "point") %>%
mutate_at(c(75:82, 89, 91, 95, 97, 99:103), decostand, method = "range")
rownames(land_var) <- land_var$Point
# #####
#
#
#
# bird_df <- filter(nmds_df, class_model == "bird") %>%
# group_by(point) %>%
# summarise(., mean_bird = mean(mean), sd_bird = mean(sd), mean_bird_temp = mean(mean_temp)) %>%
# merge(., land_var, by.x = "point", by.y = "Point", all.x = T, all.y = F) %>%
# filter(., point != "WAA2O" | point != "WBA2O") %>%
# #mutate_at(c(2:22), decostand, "range") %>%
# droplevels(.)
#
# insect_df <- filter(nmds_df, class_model == "insect") %>%
# group_by(point) %>%
# summarise(., mean_insect = mean(mean), sd_insect = mean(sd), mean_insect_temp = mean(mean_temp)) %>%
# merge(., bird_df, by.x = "point", by.y = "point", all.x = T, all.y = T) %>%
# filter(., point != "WAA2O" | point != "WBA2O") %>%
# mutate_at(c(2:22), decostand, "range") %>%
# droplevels(.)
#
# #####
#
# rownames(nmds_df) <- nmds_df$id
# df_test <- select(nmds_df, mean, sd, NT_DIST_AVG, SubcanopyHeight) %>%
# mutate_at(c(1:ncol(.)), decostand, "range") %>%
# droplevels(.)
#
# #NT_DIST_AVG, NS_DIST_AVG, GC_NG_AVG, GC_NF_AVG, GC_BS_AVG, GC_SH_AVG, aug_ndvi_avg, CanopyCover, ShrubCover, CanopyHeight, SubcanopyHeight, Slope, Aspect, Elevation, DistWater, mean, sd) %>%
#
#
# nmds_mean <- metaMDS(df_test, k = 2, trymax = 100)
#
# nmds_mean
# stressplot(nmds_mean)
#
# plot(nmds_mean)
# ordiplot(nmds_mean,type="n")
# ordihull(nmds_mean, groups=nmds_df$class_model, lty=2) col=cores2,
# #orditorp(nmds_mean,display="species",col="red",air=0.01)
# #orditorp(nmds_mean,display="sites",cex=0.9,air=0.01)
#
# plot(nmds_mean, type="n")
# points(resultado.nmds, col=cores2[dados$Bloco_Amostral], pch=16)
# ordihull(resultado.nmds, groups=dados$Bloco_Amostral, col=cores2, lty=2)
# text(resultado.nmds, labels = row.names(bio), pos=4)
#
# scores <- nmds_mean[["species"]]
#
# adonis(nmds_mean[["dist"]]~nmds_df$class_model)
# nmds_df$aug_ndvi_avg <- as.numeric(nmds_df$aug_ndvi_avg)
#A PERMANOVA:
# colours <- c("#CCFF00", "#CCCC00", "#CC9900", "#CC6600", "#CC3300", "#FF00FF", "#660000", "#663399", "#666600", "#669900", "#66CC00", "#66FF00", "#009999", "#0066FF", "#000000")
# rownames(insect_df) <- insect_df$point
#
# df_test <- select(insect_df, mean_insect, mean_bird, NT_DIST_AVG, NT_HEIGHT_AVG, CanopyHeight, Elevation)
#
# nmds_mean <- metaMDS(df_test, k = 2, try = 100)
#
# nmds_mean
# stressplot(nmds_mean)
#
# plot(nmds_mean)
# ordiplot(nmds_mean,type="n")
# orditorp(nmds_mean,display="species",col="red",air=0.01)
# orditorp(nmds_mean,display="sites",cex=0.9,air=0.01)
#
# scores <- nmds_mean[["species"]]
#
# points(nmds_mean, col= colours[land_var$NewVegDescription], pch=16)
# #ordihull(nmds_mean, groups= land_var$NewVegDescription, lty=2)
# #legend("topleft", legend = as.factor(land_var$NewVegDescription), fill = colours, cex = 0.5)
#
# scores <- nmds_mean[["species"]]
#
# adonis(df_test~insect_df$NewVegDescription)
# anosim(df_test, insect_df$NewVegDescription)
#
# ######
#
# rownames(insect_df) <- insect_df$point
#
# df_test <- select(insect_df, sd_insect, sd_bird, NT_DIST_AVG, NT_HEIGHT_AVG, CanopyHeight, Elevation)
#
# nmds_sd <- metaMDS(df_test, k = 3, try = 100)
#
# nmds_sd
# stressplot(nmds_sd)
#
# plot(nmds_sd)
# ordiplot(nmds_sd,type="n")
# orditorp(nmds_sd,display="species",col="red",air=0.01)
# orditorp(nmds_sd,display="sites",cex=0.9,air=0.01)
#
# points(nmds_sd, col= colours[insect_df$NewVegDescription], pch=16)
# #ordihull(nmds_mean, groups= land_var$NewVegDescription, lty=2)
# legend("topleft", legend = as.factor(insect_df$NewVegDescription), fill = colours, cex = 0.5)
#
# scores <- nmds_sd[["species"]]
#
# adonis(df_test~insect_df$NewVegDescription)
# anosim(df_test, insect_df$NewVegDescription)
#Complete model - birds and insects
df_newveg <- filter(df_newveg1, class_model == "bird")
row.names(df_newveg) <- df_newveg$id
colours <- c("#fdae61", "#8c510a", "#b8e186", "#f46d43", "#4d9221")
dep_var <- select(df_newveg, mean_temp, SubcanopyHeight, DistWater, aug_ndvi_avg, Elevation, GC_NF_AVG)
all <- plyr::ldply(1:6, function(x)t(combn(colnames(dep_var), x)))
all <- rename(all, col1 = 1, col2 = 2, col3 = 3, col4 = 4, col5 = 5, col6 = 6)
for (c in 1:ncol(all)) {
all[,c] <- as.character(all[,c])
}
write.csv(all, getDataPath(chapter, "Fig1", "NMDS_BIRDS_OPT", "key_birds_nmds_take2.csv"))
test <- as.list(all[seq(1,nrow(all), 16),])
scores <- data.frame( model_var = NA,
conv = NA,
stress = NA,
permanova_veg_F = NA,
permanova_veg_R2 = NA,
permanova_veg_p = NA,
permanova_class_F = NA,
permanova_class_R2 = NA,
permanova_class_p = NA)
scores_temp <- data.frame( model_var = NA,
conv = NA,
stress = NA,
permanova_veg_F = NA,
permanova_veg_R2 = NA,
permanova_veg_p = NA,
permanova_class_F = NA,
permanova_class_R2 = NA,
permanova_class_p = NA)
colours <- c("#fdae61", "#8c510a", "#b8e186", "#f46d43", "#4d9221")
line_type <- c(5, 4, 3, 2, 1)
colours2 <- c("#542788", "#b35806")
line_type2 <- c(1, 2)
summary(df_newveg)
df_newveg$VegDescription2 <- as.factor(df_newveg$VegDescription2)
rm(outcome)
#jahs <- all[c(1, 1001, 10001, 20001, 30001, 40001, 50001, 60001, 65099, 65534),]
for (i in 1:nrow(all)){
outcome <- NULL
model <- NULL
PERMANOVA <- NULL
perm <- NULL
outcome <- as.character(all[i,]) %>%
na.exclude(.) %>%
paste(., sep = ",")
skip_to_next <- FALSE
tryCatch({
model <- metaMDS(df_newveg[outcome],
dist = "bray",
k = 2,
try = 100)
PERMANOVA <- adonis(df_newveg[outcome]~df_newveg$VegDescription2)
png(filename=getDataPath(chapter, "Fig1", "NMDS_BIRDS_OPT", paste(rownames(all[i,]), "veg_TAKE2", ".png", sep = "")))
plot(model)
ordiplot(model,type="n")
orditorp(model,display="sites",cex=0.9,air=0.01, labels = F)
points(model, col= colours[df_newveg$VegDescription2], pch = 16)
orditorp(model, display="species",col="red",air=0.5)
ordihull(model, groups= df_newveg$VegDescription2, lty = line_type)
legend("topleft", legend = unique(df_newveg$VegDescription2), fill = colours, cex = 0.6)
legend("bottomleft", legend = unique(df_newveg$VegDescription2), lty = line_type, cex = 0.6)
dev.off()
#This one only for the complete models - with all groups
# perm<-adonis(df_newveg[outcome]~df_newveg$class_model)
#
#
#
# png(filename=getDataPath("Chapter1_FineScaleAcousticSurvey", "Fig1", "NMDS_birds", paste(rownames(all[i,]), "class", ".png", sep = "")))
#
# plot(model)
# ordiplot(model,type="n")
# orditorp(model,display="sites",cex=0.9,air=0.01, labels = F)
# points(model, col= colours2[df_newveg$class_model], pch=16)
# ordihull(model, groups= df_newveg$class_model, lty=line_type2[df_newveg$class_model])
# legend("topleft", legend = unique(df_newveg$class_model), fill = colours2, cex = 0.6)
# legend("bottomleft", legend = unique(df_newveg$class_model), lty = line_type2, cex = 0.6)
# dev.off()
scores_temp$model_var <- as.character(rownames(all[i,]))
scores_temp$conv <- as.character(model$converged)
scores_temp$stress <- as.numeric(model$stress)
scores_temp$permanova_veg_F <- as.numeric(PERMANOVA$aov.tab$F.Model[1])
scores_temp$permanova_veg_R2 <- as.numeric(PERMANOVA$aov.tab$R2[1])
scores_temp$permanova_veg_p <- as.numeric(PERMANOVA$aov.tab$Pr[1])
# scores_temp$permanova_class_F <- as.numeric(perm$aov.tab$F.Model[1])
# scores_temp$permanova_class_R2 <- as.numeric(perm$aov.tab$R2[1])
# scores_temp$permanova_class_p <- as.numeric(perm$aov.tab$Pr[1])
write.csv(scores_temp, getDataPath("Chapter1_FineScaleAcousticSurvey", "Fig1", "NMDS_BIRDS_OPT", paste(rownames(all[i,]), "veg_TAKE2", ".csv", sep = ""))) },
#scores <- rbind(scores, scores_temp) },
error = function(e) {skip_to_next <<-TRUE })
if(skip_to_next) { next }
}
write.csv(scores, getDataPath(chapter, "Fig1", "NMDS_BIRDS_OPT", "scores_nmds_birds.csv"))
files <- list.files(getDataPath(chapter, "Fig1", "NMDS_BIRDS_OPT"), pattern = "veg_TAKE4.csv", full.names = T)
results <- lapply(files, read.csv) %>%
map(., select, model_var, conv, stress, permanova_veg_F, permanova_veg_R2, permanova_veg_p) %>%
do.call(rbind, .) %>%
filter(., conv == "TRUE") %>%
filter(., stress < 0.1) %>%
write.csv(getDataPath(chapter, "Fig1", "NMDS_BIRDS_OPT", "filtered_nmds_birds4.csv"), row.names = F)
#Birds:
#Best model
df_birds <- filter(df_newveg1, class_model == "bird") %>%
select(., SubcanopyHeight, NS_DIST_AVG)
best_birds <- metaMDS(df_birds,
dist = "bray",
k = 2,
try = 100)
df_birds <- filter(df_newveg1, class_model == "bird")
data.scores <- as.data.frame(scores(best_birds))
data.scores$id <- rownames(best_birds)
data.scores$veg <- df_birds$VegDescription2
data.scores$point <- df_birds$Point
head(data.scores$veg)
species.scores <- as.data.frame(scores(best_birds, "species"))
species.scores$landvar <- rownames(species.scores)
head(species.scores)
p_bestbirds <- plot_ly()
p_bestbirds <- add_trace(p_bestbirds, name = data.scores$veg, type = "scatter", x = data.scores$NMDS1, y = data.scores$NMDS2, text = data.scores$point)
p_bestbirds <- add_trace(p_bestbirds, name = "Landscape attributes", mode = "text", x = species.scores$NMDS1, y = species.scores$NMDS2, text = species.scores$landvar)
p_bestbirds <- layout(p_bestbirds, title = "Birds - best model (Stress: 0.07)")
#Plotly - birds all land variables
df_birds <- filter(df_newveg1, class_model == "bird") %>%
select(., SubcanopyHeight, DistWater, aug_ndvi_avg , NS_DIST_AVG, GC_NF_AVG, GC_BS_AVG)
complete_birds <- metaMDS(df_birds,
dist = "bray",
k = 2,
try = 100)
df_birds <- filter(df_newveg1, class_model == "bird")
data.scores <- as.data.frame(scores(complete_birds))
data.scores$id <- rownames(complete_birds)
data.scores$veg <- df_birds$VegDescription2
data.scores$point <- df_birds$Point
head(data.scores$veg)
species.scores <- as.data.frame(scores(complete_birds, "species"))
species.scores$landvar <- rownames(species.scores)
head(species.scores)
p_completebirds <- plot_ly()
p_completebirds <- add_trace(p_completebirds, name = data.scores$veg, type = "scatter", x = data.scores$NMDS1, y = data.scores$NMDS2, text = data.scores$point)
p_completebirds <- add_trace(p_completebirds, name = "Landscape attributes", mode = "text", x = species.scores$NMDS1, y = species.scores$NMDS2, text = species.scores$landvar)
p_completebirds <- layout(p_completebirds, title = "Birds - all landscape variables (Stress:0.19)")
#Insects:
#Best model
df_insects <- filter(df_newveg1, class_model == "insect") %>%
select(., SubcanopyHeight, NS_DIST_AVG)
best_insects <- metaMDS(df_insects,
dist = "bray",
k = 2,
try = 100)
df_insects <- filter(df_newveg1, class_model == "bird")
data.scores <- as.data.frame(scores(best_insects))
data.scores$id <- rownames(best_insects)
data.scores$veg <- df_insects$VegDescription2
data.scores$point <- df_insects$Point
head(data.scores$veg)
species.scores <- as.data.frame(scores(best_insects, "species"))
species.scores$landvar <- rownames(species.scores)
head(species.scores)
p_bestinsects <- plot_ly()
p_bestinsects <- add_trace(p_bestinsects, name = data.scores$veg, type = "scatter", x = data.scores$NMDS1, y = data.scores$NMDS2, text = data.scores$point)
p_bestinsects <- add_trace(p_bestinsects, name = "Landscape attributes", mode = "text", x = species.scores$NMDS1, y = species.scores$NMDS2, text = species.scores$landvar)
p_bestinsects <- layout(p_bestinsects, title = "Insects - best model (Stress: 0.07)")
#Plotly - insects all land variables
df_insects <- filter(df_newveg1, class_model == "insect") %>%
select(., SubcanopyHeight, GC_BS_AVG, DistWater, GC_NF_AVG, NS_DIST_AVG, GC_SH_AVG)
complete_insects <- metaMDS(df_insects,
dist = "bray",
k = 2,
try = 100)
df_insects <- filter(df_newveg1, class_model == "insect")
data.scores <- as.data.frame(scores(complete_insects))
data.scores$id <- rownames(complete_insects)
data.scores$veg <- df_insects$VegDescription2
data.scores$point <- df_insects$Point
head(data.scores$veg)
species.scores <- as.data.frame(scores(complete_insects, "species"))
species.scores$landvar <- rownames(species.scores)
head(species.scores)
p_completeinsects <- plot_ly()
p_completeinsects <- add_trace(p_completeinsects, name = data.scores$veg, type = "scatter", x = data.scores$NMDS1, y = data.scores$NMDS2, text = data.scores$point)
p_completeinsects <- add_trace(p_completeinsects, name = "Landscape attributes", mode = "text", x = species.scores$NMDS1, y = species.scores$NMDS2, text = species.scores$landvar)
p_completeinsects <- layout(p_completeinsects, title = "Insects - all landscape variables (Stress:0.18)")
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