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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
061c9f389d253a2d45146c411f1b1e158059695c
|
f971b00d50be32357c35ab530be3a6a8baf70318
|
/1_simulate_phenotypes.R
|
865ef0a9d635b7db940444fea3a2a81ff3927863
|
[] |
no_license
|
joegage/GWAS_AUC
|
34439470dfe8739f76e1820d9778472d6f05dcdc
|
5b10420143c58bf9bb3b152cdf13bca2cda6fc91
|
refs/heads/master
| 2020-03-12T16:49:41.264086
| 2018-04-24T16:09:59
| 2018-04-24T16:09:59
| 130,724,447
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,685
|
r
|
1_simulate_phenotypes.R
|
source("simPheno.R")
genoFile <- "~/Dropbox/papers/tassel_GWAS_paper/analyses/widiv/genos_phenos_farmCPU.rda"
load(genoFile)
rm(allPhenos); gc()
GM$chrom <- as.character(GM$chrom)
GM$pos <- as.character(GM$pos)
h2 <- seq(0.1, 1, 0.1)
nCausativeValues <- c(10,100, 1000)
nReps <- 10
GDfreqs <- colMeans(GD[,-1])
GDfreqs <- apply(cbind(GDfreqs, 1-GDfreqs), 1, min)
keep <- GDfreqs > 0.02 & GM$chrom %in% 1:10
print(table(keep))
GD <- GD[,c(TRUE, keep)]
GM <- GM[keep, ]
set.seed(345)
for(nCausative in nCausativeValues){
causativeSNPs <- sample(2:ncol(GD), nCausative)
genotypes <- t(as.matrix(GD[,causativeSNPs]))
alleleFreqs <- rowMeans(genotypes)
simulated <- simPheno(h2, nCausative, nrow(GD), alleleFreqs, genotypes, nReps)
simulated$effects <- data.frame(SNP=colnames(GD)[causativeSNPs],
chr=GM[causativeSNPs, "chrom"],
pos=GM[causativeSNPs, "pos"],
effect=simulated$effects)
params <- expand.grid(1:nReps, h2*100)
colnames(simulated$pheno) <- paste0("c", nCausative, "h", params[,2], "r", params[,1])
assign(paste0("c", nCausative), simulated)
}
allPhenos <- data.frame(taxa=GD$taxa, c10$pheno, c100$pheno, c1000$pheno)
save("allPhenos", "GD", "GM", file="simulated_phenos_genos.rda")
write.table(allPhenos, "all_simulated_phenos.txt", row.names=FALSE, col.names=TRUE, sep="\t", quote=FALSE)
for(nCausative in nCausativeValues){
fileName <- paste0("c", nCausative, "_effects.txt")
outDat <- get(paste0("c", nCausative))$effects
write.table(outDat, fileName, row.names=FALSE, col.names=TRUE, quote=FALSE, sep="\t")
}
|
2495ca5b29c1335d9d054bbed7aaf955488b6421
|
94fa6de1769db0c8bc2862c67f4e7d80674fd395
|
/Part 4 - Clustering/Section 25 - Hierarchical Clustering/data_preprocessing_template.R
|
4dda8560fa35c41593ba3f51e99c7544d9c2b65d
|
[] |
no_license
|
samehamin/Machine-Learning-A-Z
|
76d6f04f25c0c7a22f70004027d7212b794a9ac7
|
d83bff72bf7da1181fd9a70725010842285ee756
|
refs/heads/master
| 2021-05-06T15:46:35.964520
| 2018-11-17T20:23:16
| 2018-11-17T20:23:16
| 113,660,183
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 492
|
r
|
data_preprocessing_template.R
|
# Data Preprocessing Template
# Importing the dataset
dataset = read.csv('Mall_Customers.csv')
X = dataset[4:5]
# Using the dendrogram to find the optimal number of clusters
dendrogram = hclust(dist(X, method = 'euclidean'), method = 'ward.D')
plot(dendrogram,
main = paste('Dendrogram'),
xlab = 'Customers',
ylab = 'Euclidean distances')
# Fitting heirachical clustering to the mall dataset
hc = hclust(dist(X, method = 'euclidean'), method = 'ward.D')
y_hc = cutree(hc, 5)
|
c50afb252a3dea2aa7e67e26f463f13497ed8854
|
66539bfe3ccea078550d60b55b1d20b8b484dc90
|
/R/lis3.R
|
949e0d2de1ed21f6d898cc51888d0910e218849d
|
[] |
no_license
|
xinyue-L/PML
|
ee1e189f32ecbaad4f24d710d517e818d9a231b6
|
79f67a8d3443b514e4536daa4579524881096292
|
refs/heads/master
| 2020-07-05T17:48:39.537834
| 2020-05-07T03:59:40
| 2020-05-07T03:59:40
| 202,718,263
| 0
| 1
| null | 2020-05-07T04:01:11
| 2019-08-16T11:46:35
|
R
|
UTF-8
|
R
| false
| false
| 645
|
r
|
lis3.R
|
#' Activity data for three individuals
#'
#' A synthetic data list of three elements, each consisting of an activity matrix for one individual, and
#' each column of an activity matrix is one-day activity observation. Therefore, an activity matrix is
#' \code{nob} by \code{nday}. It is a named list, the name of which is the individual ID. It is for illustration
#' purposes of the functions in the package \code{PML} only.
#'
#' @docType data
#'
#' @usage data(lis3)
#'
#' @format An object of class \code{list}.
#'
#' @keywords datasets
#'
#' @examples
#' data(lis3)
#' pa3 <- form(lis3)
#'
#' @seealso \code{\link{form}}, \code{\link{pa3}}
|
0db6f5eae5976bd339237cc6a54c9d171399f90c
|
8228230ee94076f3afaa84a8cd1ad7fa6bb3e7cf
|
/e5_reshape_if_else.R
|
955da2c308fa92c3e180f815bfa186127aabc593
|
[] |
no_license
|
agaspard98/borealis
|
36f1b95889ebf71f2fba8469c6903bc42225ef23
|
5ad23bfa83fec14b8d2737322889e524a57b72fa
|
refs/heads/master
| 2023-09-05T03:33:07.154842
| 2021-10-31T11:02:50
| 2021-10-31T11:02:50
| 411,773,526
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,943
|
r
|
e5_reshape_if_else.R
|
#Exercise #5
#Practice the following skills:
# reshape data frames
# if_else statements
#Part 1: version control --------------------------------------------
# Please track changes to your script using version control. Include in your homework the URL to your completed homework assignment available on your
#GitHub repository
#URL: https://github.com/agaspard98/borealis.git
#Part 2: reshaping data frames ---------------------------------------------
#Using the 'batting.2008.Rdata' dataset, please do the following
setwd("~/2021fallclasses/Intro to R/assignments/assignment 5")
load("~/2021fallclasses/Intro to R/assignments/assignment 5/batting.2008.Rdata")
library(tidyverse)
head(d)
#1) Using the spread() function, please create a data frame that shows the total number of home runs (HR) for each birthYear for
#each team (use the teamID column) aggregated by player
da <- d[, c("teamID", "HR", "nameLast", "nameFirst")]
dab <- data.frame(r1=names(da$birthYear), t(da)
dac <- spread(data = dab, key = teamID, value = HR)
#2) Subset the data for the Houston Astros (HOU). Using the gather() function, create a new data frame that has 3 columns:
# (1) playerID, (2) variable containing (AB, R, H, 2B, 3B) and (3) column with the corresponding values for the stats.
ha = d[d$teamID == "HOU", c("playerID", "AB", "R", "H", "2B", "3B")]
hb = gather(data = ha, key = player.stat, value = stat.value, 2:6)
#3) Repeat the process for Question 2. However, this time, please use the melt() function from the 'reshape2' package.
hamelt <- melt(data = ha, id = c("playerID"), variable.name = c("player.stat"))
#4) Using the dcast() function from the 'reshape2 package', find the mean number of AB, R, H, 2B, 3B for each player (use 'playerID' to aggregate).
hadcast <- dcast(data = ha, formula = playerID~3B, fun.aggregate = mean)
#Part 3: if_else statements -------------------------------
# one condition
#5) generate an if_else statment to test if the value of a numeric object is positive number
x = 7
if(x > 0){
print("positive number")
} else {
print("negative number")
}
#6) using the two objects below, please generate an if_else statement that uses logical operator (i.e, !=, ==, >, etc.)
# in the test
x <- 5
y <- 8
if(x != y){
print("True")
} else {
print("False")
}
#7) Hamlet's quandry: In Shakespeare's play Hamlet, the lead character has a famous speech "to be or not to be".
browseURL("https://www.poetryfoundation.org/poems/56965/speech-to-be-or-not-to-be-that-is-the-question")
# Write an if_else statement using the "to be' or 'not to be' for outcomes of the 'yes' and 'no' arguments respectively.
suffer = 7
take.arms = 12
if(suffer != take.arms){
print("To Be")
} else {
print("Not to be")
}
#two or more conditions
#8) create an 'if else' statement that returns (in order of preference) your four ideal pizza toppings
pepercini = 1
peperoni = 2
sausage = 3
cheese = 4
if(pepercini = 1 & peperoni = 2) {
print("Pepercini, peperoni, sausage, cheese")
} else if(pepercini = 3 & peperoni =4){
print("Sausage, cheese, pepercini, peperoni")
}
#two or more conditions joined (new content)
#To join two or more conditions into a single if statement, use logical operators viz. && (and), || (or) and ! (not).
#example:
x <- 7
y <- 5
z <- 2
if(x > y && x > z) {
print("x is greater")
}
#9) generate your own 'if' statement using multiple conditions in one line
if(z < y && z < x) {
print("z is less than")
}
}
#New content: understanding a common warning/error message when using if_else.
# The follow set of code will generate a warning message. Explain in a comment what the warning is telling you.
v <- 1:6
if(v %% 2) {
print("odd")
} else {
print("even")
}
# V has 6 numbers within it, but the if else statement is not the same length. R will evaluate the if else statement for only the first value in the v vector.
|
b6148da056da8043b030ed43c8376742449dd0b8
|
54a538a87ce7a0bf2313e96cfee6776fdc26227e
|
/LTLABview_collector.R
|
eb968086799e0313850d0708ed4e9d4f43e92482
|
[] |
no_license
|
ubbikas/LTLABcollector
|
7d6e360094d2944199691f7e6b6d3501db8825c9
|
17c8027c37ad417e7e673fd780d18a9be5574a24
|
refs/heads/master
| 2021-01-01T05:15:42.035641
| 2016-05-14T10:33:02
| 2016-05-14T10:33:02
| 56,992,986
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13,675
|
r
|
LTLABview_collector.R
|
# loading required libraries
suppressPackageStartupMessages(require(dplyr))
suppressPackageStartupMessages(require(xml2))
suppressPackageStartupMessages(require(methods))
if (Sys.info()[[4]] == "ANDRIUS-PC")
setwd("D:/-=Works=-/R/GitHub/LTLABview")
if (Sys.info()[[4]] == "LTLAB-DEV")
setwd("C:/R/GitHub/LTLABview")
refresh_rate <- 5
last_db_update_time <- 0
update_count <- 0
current_conveyor_ids <- NA
current_project <- "No production"
last_project <- "Noname"
current_conveyor <- data.frame()
# choosing between real or testing tracedata file names
#tracedata_DB_name <- "//M85399/1_m85399/tracedata.db3"
tracedata_DB_name <- "tracedata.db3"
ltlab_smt_db <- "LTLAB_SMT_DB.sqlite"
conn_status <- list(AX_DB_TBL = "NOT_OK",
SMT_DB = "NOT_OK",
SMT_PROJ = "NOT_OK",
PP_DATA = "NOT_OK")
last_status <- data.frame()
# functions saves current connectios status to csv file
status_to_csv <- function(date = 0) {
data <- data.frame(AX_DB_TBL = conn_status$AX_DB_TBL,
SMT_DB = conn_status$SMT_DB,
SMT_PROJ = conn_status$SMT_PROJ,
PP_DATA = conn_status$PP_DATA)
last_status <- data.frame(AX_DB_TBL = last_status$AX_DB_TBL,
SMT_DB = last_status$SMT_DB,
SMT_PROJ = last_status$SMT_PROJ,
PP_DATA = last_status$PP_DATA)
if (!identical(data, last_status)) {
last_status <<- data
data["Update"] <- date
print("Status change:")
print(data)
write.csv(data, "status.csv", row.names = FALSE)
}
}
status_to_csv(date = Sys.time())
# conveyorIDlist - list of the previous IDs in production, if some of them
# changes it means they are finished and are writen to finished roduction
# csv file with PCBfinishedtoCSV
conveyorIDlist <- c()
PCBfinished <- function(){
conveyorIDlist <- c(conveyorIDlist, current_conveyor_ids) %>%
tail(2)
conveyorIDlist <<- conveyorIDlist
if (length(conveyorIDlist) < 2){
return()
} else {
finished <- setdiff(unlist(conveyorIDlist[1]),
unlist(conveyorIDlist[2]))
print(paste("Finished: ", finished))
if (length(finished) > 0) {
tryCatch({
PCBfinishedtoCSV(finished)
print("All data writen!!!!")
}, error = function(e) {
print(e)
status_to_csv(date = Sys.time())
next()
})
}
}
}
# function to write data of finished PCBs to csv file;
# name of the file - current date
PCBfinishedtoCSV <- function(idlist) {
for (id in idlist) {
proj <- current_project
date <- format(Sys.time(), format = "%Y-%m-%d")
time <- strftime(Sys.time(), format="%H:%M:%S")
PCBquant <- current_project_PCB_quantity
PCBcomp <- current_project_comp_quantity
fileName <- file.path("data", paste(date, "csv", sep = "."))
all <- data.frame(ID = id,
PROJECT = proj,
DATE = date,
TIME = time,
QUANTITY = PCBquant,
COMPONENTS = PCBcomp,
row.names = NULL,
stringsAsFactors = FALSE)
if (file.exists(fileName)) {
conn_status$UPDATE <- time
write.table(all,
file = fileName,
sep = ",",
append = TRUE,
row.names = FALSE,
col.names = FALSE)
} else {
conn_status$UPDATE <- time
write.table(all,
file = fileName,
sep = ",",
row.names = FALSE,
col.names = TRUE)
}
}
}
# next section needs to be looped forever -------------------------------------
while (TRUE) {
Sys.sleep(refresh_rate)
print("-----------------------------------------------------")
system.time({
# checking if tracedata DB from production exists
if (file.exists(tracedata_DB_name)) {
if (conn_status$AX_DB_TBL == "NOT_OK") {
tryCatch({
tracedata_DB <- src_sqlite(tracedata_DB_name)
}, error = function(e) {
print("DB is not available")
conn_status$AX_DB_TBL <- "NOT_OK"
status_to_csv(date = Sys.time())
e
}
) -> tracedata_DB_tryCatch
if(inherits(tracedata_DB_tryCatch, "error")) next()
conn_status$AX_DB_TBL <- "OK"
print("AX_DB_TBL - OK")
}
} else {
conn_status$AX_DB_TBL <- "NOT_OK"
status_to_csv(date = Sys.time())
print("AX_DB_TBL - NOT OK!!!!")
next()
}
if (last_db_update_time == 0) {
tryCatch({
print("First update")
last_db_update_time <- file.info(tracedata_DB_name)$mtime
transportmap <- tbl(tracedata_DB, sql("SELECT * FROM transportmap"))
current_conveyor <- as.data.frame(
transportmap %>%
filter(lPos %in% c(1:49)) %>%
select(idPCB, lPos, strPP) %>%
arrange(lPos) %>%
mutate(lPos2 = lPos - 1.2))
if (!dim(current_conveyor)[1] == 0) {
current_project <- as.data.frame(current_conveyor %>%
select(strPP))[1,]
}
}, error = function(e) {
print("DB is busy 1")
print(e)
conn_status$AX_DB_TBL <- "NOT_OK"
status_to_csv(date = Sys.time())
e
}
) -> transportmap_tryCatch
if(inherits(transportmap_tryCatch, "error")) next()
}
# checking if local SMT DB exists
if(file.exists(ltlab_smt_db)) {
if (conn_status$SMT_DB == "NOT_OK") {
tryCatch({
smt_DB <- src_sqlite(ltlab_smt_db)
projects <- tbl(smt_DB, sql("SELECT * FROM PROJEKTAI"))
all_projects_names <- projects %>% select(PROJEKTAS)
conn_status$SMT_DB <- "OK"
print("SMT_DB - OK")
}, error = function(e) {
print("SMT DB is busy")
print(e)
conn_status$SMT_DB <- "NOT_OK"
status_to_csv(date = Sys.time())
e
}
) -> smt_DB_tryCatch
if(inherits(smt_DB_tryCatch, "error")) next()
}
} else {
conn_status$SMT_DB <- "NOT_OK"
status_to_csv(date = Sys.time())
print("SMT_DB - NOT OK!!!!")
next()
}
print(paste("Current project:", current_project))
tryCatch({
db_update_time <- file.info(tracedata_DB_name)$mtime
if ((as.numeric(Sys.time()) - as.numeric(db_update_time) > 120) &&
(dim(current_conveyor)[1] == 0)) {
current_project <- "No production"
conn_status$SMT_PROJ <- "NOT_OK"
conn_status$PP_DATA <- "NOT_OK"
if (last_project != current_project) {
current_project_data <- data.frame(current_project = NA,
current_project_name = NA,
current_project_side = NA,
current_project_comp_quantity = NA,
current_project_PCB_quantity = NA,
CycleTime = NA,
PCBLength = NA,
PCBWidth = NA,
BoardPitch = NA,
MaximumNumberOfBoards = NA,
StopperInX = NA)
print(current_project_data)
write.csv(current_project_data,
"current_project_data.csv",
row.names = FALSE)
last_project <- current_project
}
print("No production!!!!")
status_to_csv(date = Sys.time())
next()
}
db_update_time_diff <- as.numeric(db_update_time) - as.numeric(last_db_update_time)
last_db_update_time <- db_update_time
update_count <- c(update_count, db_update_time_diff) %>%
tail(2)
if(update_count[1] > 0 && update_count[2] == 0){
print("DB update found")
transportmap_new <- as.data.frame(
transportmap %>%
filter(lPos %in% c(1:49)) %>%
select(idPCB, lPos, strPP) %>%
arrange(lPos) %>%
select(idPCB, lPos))
current_conveyor_pcb_pos <- current_conveyor %>%
select(idPCB, lPos)
if(!identical(transportmap_new, current_conveyor_pcb_pos)) {
print("Transport IDs changed!")
current_conveyor <- as.data.frame(
transportmap %>%
filter(lPos %in% c(1:49)) %>%
select(idPCB, lPos, strPP) %>%
arrange(lPos) %>%
mutate(lPos2 = lPos - 1.2))
current_conveyor_ids <- current_conveyor %>%
select(idPCB) %>%
as.data.frame()
if (!dim(current_conveyor)[1] == 0) {
current_project <- as.data.frame(current_conveyor %>%
select(strPP))[1,]
}
write.csv(current_conveyor,
"current_conveyor.csv",
row.names = FALSE)
print(current_conveyor)
} else {
print("No transport change!!!!")
status_to_csv(date = Sys.time())
next()
}
} else {
print("No DB update!!!!")
status_to_csv(date = Sys.time())
next()
}
}, error = function(e) {
print("DB is busy 2")
conn_status$AX_DB_TBL <- "NOT_OK"
status_to_csv(date = Sys.time())
e
}
) -> current_conveyor_tryCatch
if(inherits(current_conveyor_tryCatch, "error")) next()
if (last_project != current_project) {
tryCatch({
current_project_name <- current_project %>%
sub("_BOT","",.) %>%
sub("_TOP","",.)
current_project_side <- ifelse(grepl("_BOT$", current_project),
c("BOT"),
c("TOP"))
all_names <- all_projects_names %>%
as.data.frame() %>%
.[["PROJEKTAS"]]
if (current_project_name %in% all_names) {
current_project_comp_quantity <- projects %>%
filter(PROJEKTAS == current_project_name) %>%
select(ifelse(current_project_side == "TOP",
TOP_KOMP,
BOT_KOMP)) %>%
as.data.frame() %>%
as.numeric()
conn_status$SMT_PROJ <- "OK"
print(paste("SMT_PROJ - OK - ", current_project_comp_quantity))
} else {
print("SMT_PROJ - NOT OK!!!!")
conn_status$SMT_PROJ <- "NOT_OK"
status_to_csv(date = Sys.time())
next()
}
pp_file_name <- file.path("PP",
paste(current_project, "PP", sep = "."))
if (file.exists(pp_file_name)) {
PnPFileXML <- read_xml(pp_file_name)
CycleTime <- xml_find_all(PnPFileXML, ".//CycleTime") %>%
xml_text() %>%
as.numeric()
PCBLength <- xml_find_all(PnPFileXML, ".//PCBLength") %>%
xml_text() %>%
as.numeric()
PCBWidth <- xml_find_all(PnPFileXML, ".//PCBWidth") %>%
xml_text() %>%
as.numeric()
BoardPitch <- xml_find_all(PnPFileXML, ".//BoardPitch") %>%
xml_text() %>%
as.numeric()
MaximumNumberOfBoards <- xml_find_all(PnPFileXML, ".//MaximumNumberOfBoards") %>%
xml_text() %>%
as.numeric()
StopperInX <- xml_find_all(PnPFileXML, ".//StopperInX") %>%
xml_text() %>%
as.numeric()
current_project_PCB_quantity <- xml_find_all(PnPFileXML, ".//CircuitId") %>%
xml_text() %>%
as.numeric() %>%
max()
current_project_data <- data.frame(current_project,
current_project_name,
current_project_side,
current_project_comp_quantity,
current_project_PCB_quantity,
CycleTime,
PCBLength,
PCBWidth,
BoardPitch,
MaximumNumberOfBoards,
StopperInX)
print(current_project_data)
write.csv(current_project_data,
"current_project_data.csv",
row.names = FALSE)
conn_status$PP_DATA <- "OK"
print("PP_DATA - OK")
} else {
print("PP_DATA - NOT OK!!!!")
conn_status$PP_DATA <- "NOT_OK"
status_to_csv(date = Sys.time())
next()
}
last_project <- current_project
}, error = function(e) {
print("SMT DB is busy 2")
conn_status$SMT_PROJ <- "NOT_OK"
status_to_csv(date = Sys.time())
e
}
) -> smt_DB_2_tryCatch
if(inherits(smt_DB_2_tryCatch, "error")) next()
}
PCBfinished()
status_to_csv(date = Sys.time())
print("Cycle finished!!!!!!!!!!!!!!!!")
}) -> timing
print(timing)
}
|
e5c2485511ad136959cc3d889fc56d095fe5c73c
|
5b6179f52aa5df28dc2527b0dcfa809e8e502c5d
|
/Codes/Map_study_sites.R
|
b27bce77506bc95005638ffd790597d424fcfe19
|
[] |
no_license
|
jms5151/EVP
|
e9de694f9e062ca149a2cac5e5d72e8f7d98f3f0
|
3ef8208d10353d6d058b0ea8085d9cc0a14ba20a
|
refs/heads/master
| 2021-06-05T18:05:24.776867
| 2020-06-12T02:53:07
| 2020-06-12T02:53:07
| 150,645,258
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,904
|
r
|
Map_study_sites.R
|
# https://eriqande.github.io/rep-res-web/lectures/making-maps-with-R.html
library(ggplot2)
library(ggmap)
library(maps)
library(mapdata)
library(ggrepel)
source("C:/Users/Jeremy/Box Sync/R_functions/Google_maps_API_token.R")
# set sites
ecuador.sites <-data.frame(longitude = c(-80.2225, -79.9554, -79.6109, -79.6109)
, latitude = c(-3.4764, -3.2581, -3.7173, -3.6573)
, Site = c("Huaquillas", "Machala", "Zaruma", "Portovelo"))
kenya.sites <- data.frame(longitude = c(34.636908, 34.767957, 39.668207, 39.566111)
, latitude = c(-0.035266, -0.091702, -4.043477, -4.287500)
, Site = c("Chulaimbo", "Kisumu", "Msambweni", "Ukunda"))
# get basemaps
# ecuador <- get_googlemap(center = c(-79.6109, -2.7173), maptype = "terrain", source = "google", zoom = 8, style='feature:all|element:labels|visibility:off')
# kenya <- get_googlemap(center = c(37.9083, 0.1769), maptype = "terrain", source = "google", zoom = 6, style='feature:all|element:labels|visibility:off')
ecuador <- get_googlemap(center = c(-79.6109, -2.0173), maptype = "terrain", source = "google", zoom = 7, style='feature:all|element:labels|visibility:off')
kenya <- get_googlemap(center = c(36.9083, -2.0173), maptype = "terrain", source = "google", zoom = 7, style='feature:all|element:labels|visibility:off')
# make maps
ggmap(ecuador) +
geom_point(data = ecuador.sites, mapping = aes(x = longitude, y = latitude)) +
geom_label_repel(aes(x = longitude, y = latitude, label = Site)
, xlim = c(ecuador.sites$longitude,0.05), ylim = c(ecuador.sites$latitude,0.05)
, min.segment.length = 0.5, direction='both'
, data = ecuador.sites) +
ggtitle("A. Ecuador") +
ylab("Latitude") +
xlab("Longitude")
ggmap(kenya) +
geom_point(data = kenya.sites, mapping = aes(x = longitude, y = latitude)) +
geom_label_repel(aes(x = longitude, y = latitude, label = Site)
, xlim = c(kenya.sites$longitude,0.05), ylim = c(kenya.sites$latitude,0.05)
, min.segment.length = 0.5, direction='both'
, data = kenya.sites) +
ggtitle("B. Kenya") +
ylab("Latitude") +
xlab("Longitude")
# -------------------------------------------------------------------------------------------
# https://www.r-spatial.org/r/2018/10/25/ggplot2-sf-3.html ----------------------------------
# load libraries
library("cowplot")
library("googleway")
library("ggplot2")
library("ggrepel")
library("ggspatial")
library("sf")
library("rnaturalearth")
library("rnaturalearthdata")
library("rgeos")
# set theme and get world map
theme_set(theme_bw())
world <- ne_countries(scale='medium', returnclass = 'sf')
# set study site coordinates
kenya.sites <- st_as_sf(data.frame(longitude = c(34.636908, 34.767957, 39.668207, 39.566111)
, latitude = c(-0.035266, -0.091702, -4.043477, -4.287500))
, coords = c("longitude", "latitude"), crs = 4210, agr = "constant")
ecuador.sites <- st_as_sf(data.frame(longitude = c(-80.2225, -79.9554, -79.6109, -79.6109)
, latitude = c(-3.4764, -3.2581, -3.7173, -3.6573)) # last latitude is actually -3.6873; I shifted it for visualization
, coords = c("longitude", "latitude"), crs = 4248, agr = "constant")
# Map of world with insets for Ecuador and Kenya -----------------------------------------
gworld <- ggplot(data = world) +
geom_sf(aes(fill = region_wb)) + #, show.legend = FALSE
geom_rect(xmin = -83, xmax = -73, ymin = -7, ymax = 4,
fill = NA, colour = "black", size = 1.5) +
geom_rect(xmin = 31, xmax = 45, ymin = -6.5, ymax = 6.5,
fill = NA, colour = "black", size = 1.5) +
scale_fill_viridis_d(option = "plasma", name="") +
theme(panel.background = element_rect(fill = "azure"),
panel.border = element_rect(fill = NA))
kenyamap <- ggplot(data = world) +
geom_sf(aes(fill = region_wb)) +
annotate(geom = "text", x = 37.7, y = 2, label = "Kenya", fontface = "bold", color = "grey22", size = 4) +
geom_sf(data = kenya.sites, size = 2, shape = 21, fill = "black", color='white') +
coord_sf(xlim = c(33, 43), ylim = c(-5, 5.5), expand = FALSE) +
scale_fill_viridis_d(option = "plasma") +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.title.y = element_blank(), axis.text.x = element_blank(),
axis.text.y = element_blank(), axis.ticks = element_blank(), panel.background = element_rect(fill = "azure"),
panel.border = element_rect(fill = NA), plot.background = element_blank())
ecuadormap <- ggplot(data = world) +
geom_sf(aes(fill = region_wb)) +
annotate(geom = "text", x = -78.1, y = -0.5, label = "Ecuador", fontface = "bold", color = "grey22", size = 4) +
geom_sf(data = ecuador.sites, size = 2, shape = 21, fill = "black", color='white') +
coord_sf(xlim = c(-81, -75), ylim = c(-5.1, 1.6), expand = FALSE) +
scale_fill_viridis_d(option = "plasma") +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.title.y = element_blank(), axis.text.x = element_blank(),
axis.text.y = element_blank(), axis.ticks = element_blank(), panel.background = element_rect(fill = "azure"),
panel.border = element_rect(fill = NA), plot.background = element_blank())
gworld +
annotation_custom(grob = ggplotGrob(ecuadormap),
xmin = -210, xmax = -75,
ymin = -90, ymax = 25) +
annotation_custom(grob = ggplotGrob(kenyamap),
xmin = 40, xmax = 175,
ymin = -50, ymax = 65)
# Map of Ecuador and Kenya -------------------------------------------------------------
gworld <- ggplot(data = world) +
geom_sf(fill = "transparent", show.legend = FALSE) +
geom_rect(xmin = -83, xmax = -73, ymin = -7, ymax = 4,
fill = NA, colour = "black", size = 1.5) +
geom_rect(xmin = 31, xmax = 45, ymin = -6.5, ymax = 6.5,
fill = NA, colour = "black", size = 1.5) +
scale_fill_viridis_d(option = "plasma", name="") +
theme(panel.background = element_rect(fill = NA), panel.border = element_rect(fill = NA))
kenyamap2 <- ggplot(data = world) +
geom_sf(aes(fill = region_wb)) +
annotate(geom = "text", x = 37.7, y = 2, label = "Kenya", fontface = "bold", color = "grey22", size = 6) +
annotate(geom = "text", x = 35, y = 0.25, label = "Chulaimbo", fontface = "italic", color = "grey22", size = 4) +
annotate(geom = "text", x = 35.1, y = -0.35, label = "Kisumu", fontface = "italic", color = "grey22", size = 4) +
annotate(geom = "text", x = 38.6, y = -3.8, label = "Msambweni", fontface = "italic", color = "grey22", size = 4) +
annotate(geom = "text", x = 38.7, y = -4.2, label = "Ukunda", fontface = "italic", color = "grey22", size = 4) +
geom_sf(data = kenya.sites, size = 2, shape = 21, fill = "black", color='white') +
coord_sf(xlim = c(33, 43), ylim = c(-5, 5.5), expand = FALSE) +
scale_fill_viridis_d(option = "plasma") +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.title.y = element_blank(), panel.background = element_rect(fill = "azure"),
panel.border = element_rect(fill = NA))
ecuadormap2 <- ggplot(data = world) +
geom_sf(aes(fill = region_wb)) +
annotate(geom = "text", x = -78.1, y = -0.5, label = "Ecuador", fontface = "bold", color = "grey22", size = 6) +
annotate(geom = "text", x = -79.3, y = -3.2, label = "Machala", fontface = "italic", color = "grey22", size = 4) +
annotate(geom = "text", x = -79.5, y = -3.45, label = "Huaquillas", fontface = "italic", color = "grey22", size = 4) +
annotate(geom = "text", x = -79.1, y = -3.65, label = "Zaruma", fontface = "italic", color = "grey22", size = 4) +
annotate(geom = "text", x = -79, y = -3.85, label = "Portovelo", fontface = "italic", color = "grey22", size = 4) +
geom_sf(data = ecuador.sites, size = 2, shape = 21, fill = "black", color='white') +
coord_sf(xlim = c(-81, -75), ylim = c(-5.1, 1.6), expand = FALSE) +
scale_fill_viridis_d(option = "plasma") +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.title.y = element_blank(), panel.background = element_rect(fill = "azure"),
panel.border = element_rect(fill = NA))
ggplot() +
coord_equal(xlim = c(0, 2), ylim = c(0, 1), expand = FALSE) +
annotation_custom(ggplotGrob(ecuadormap2), xmin = 0, xmax = 1, ymin = 0, ymax = 1) +
annotation_custom(ggplotGrob(kenyamap2), xmin = 1, xmax = 2, ymin = 0, ymax = 1) +
theme_void()
ggplot() +
coord_equal(xlim = c(0, 2), ylim = c(0, 2), expand = FALSE) +
annotation_custom(ggplotGrob(ecuadormap2), xmin = 0, xmax = 1, ymin = 0, ymax = 1) +
annotation_custom(ggplotGrob(kenyamap2), xmin = 1, xmax = 2, ymin = 0, ymax = 1) +
annotation_custom(ggplotGrob(gworld), xmin = 0, xmax = 2, ymin = 1, ymax = 2) +
theme_void()
|
872cf39055313085e1b092ebcefa0d2650ca2eb6
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/naniar/examples/add_any_miss.Rd.R
|
950d10f5330387b9412f88993e8d4a1e4638679f
|
[] |
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
| 265
|
r
|
add_any_miss.Rd.R
|
library(naniar)
### Name: add_any_miss
### Title: Add a column describing presence of any missing values
### Aliases: add_any_miss
### ** Examples
airquality %>% add_any_miss()
airquality %>% add_any_miss(Ozone)
airquality %>% add_any_miss(Ozone, Solar.R)
|
e7b0d352096a51d67df6dcee35d0bbb74fe67cbe
|
90bfb6687a16fc6d79dd99a3dc11828ad2c0321b
|
/scripts/hgnc_symbol_checker.R
|
e2c574f3938ee93d0420658fc4a79ef712e07df4
|
[] |
no_license
|
pilarcacheiro/wol
|
e028960f797212d885195ae4907fc05127511382
|
2ef44e6a37ac9a938354f081bb662993a98331c5
|
refs/heads/master
| 2022-10-05T14:40:34.123580
| 2020-06-08T21:35:36
| 2020-06-08T21:35:36
| 270,823,761
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,002
|
r
|
hgnc_symbol_checker.R
|
###############################################################################################################################
###############################################################################################################################
### Script: hgnc_symbol_checker.R
### Purpose: check gene symbols and retrieve hgnc id
### Description: this function returns hgnc ids for protein coding genes symbols / (only protein coding genes /input file could
### be modified)
### Input: "gene_with_protein_product.txt" (see README) file and a vector of gene symbols to check
### Output: dataframe with 3 columns: "HGNC.ID":corresponding hgnc id ("-" , if no hgnc id was found): "Gene.Symbol": gene
### symbol provided; "Type": mapping type (Approved.Symbol,Synonym.Symbol,Notfound.ProteinCoding.Symbol,...)
###############################################################################################################################
###############################################################################################################################
hgnc.checker <- function(gene.symbols,gene.file){
library(tidyr); library(dplyr); library(data.table)
check.approved <- function(input.genes,database) {
return(database %>% dplyr::select(hgnc_id,symbol) %>% mutate_if(is.factor,as.character) %>%
filter(symbol!="") %>%
filter(symbol %in% input.genes) %>% dplyr::rename (Gene.Symbol = symbol,HGNC.ID = hgnc_id) %>%
mutate_if(is.factor,as.character) %>% mutate(Type = "Approved.Symbol"))
}
check.synonyms <- function(input.genes,database){
return(database %>% dplyr::select(hgnc_id,alias_symbol) %>% mutate_if(is.factor,as.character) %>%
filter(alias_symbol!="") %>%
tidyr::separate_rows(alias_symbol,sep="\\|") %>% dplyr::rename(HGNC.ID = hgnc_id) %>%
mutate(Gene.Symbol = trimws(alias_symbol)) %>%
dplyr::select(HGNC.ID,Gene.Symbol) %>% filter(Gene.Symbol %in% input.genes) %>%
mutate_if(is.factor,as.character) %>% mutate(Type = "Synonym.Symbol"))
}
check.previous <- function(input.genes,database) {
return(database %>% dplyr::select(hgnc_id,prev_symbol) %>% mutate_if(is.factor,as.character) %>%
filter(prev_symbol!="") %>%
tidyr::separate_rows(prev_symbol,sep="\\|") %>% dplyr::rename(HGNC.ID = hgnc_id) %>%
mutate(Gene.Symbol = trimws(prev_symbol)) %>%
dplyr::select(HGNC.ID,Gene.Symbol) %>% filter(Gene.Symbol %in% input.genes) %>%
mutate_if(is.factor,as.character) %>% mutate(Type = "Previous.Symbol"))
}
check.duplicates.symbol <- function(file.to.check.symbols,duplicates.symbol){
if(!length(duplicates.symbol)) { return(file.to.check.symbols)}
else{
final.nodup.symbol <- file.to.check.symbols %>% filter(!Gene.Symbol %in% duplicates.symbol)
duplicate.symbols.df <- data.frame(HGNC.ID = rep("-",length(duplicates.symbol)), Gene.Symbol = duplicates.symbol,
Type = "Ambiguous.Symbol")
final.dups.symbol <- rbind(final.nodup.symbol,duplicate.symbols.df)
return(final.dups.symbol)
}
}
check.duplicates.id <- function(file.to.check.ids,duplicates.id){
if(!length(duplicates.id)) { return(file.to.check.ids)}
else{
final.nodup.id <- file.to.check.ids %>% filter(!HGNC.ID %in% duplicates.id)
duplicate.ids.df <- file.to.check.ids %>% filter(HGNC.ID %in% duplicates.id) %>% mutate(HGNC.ID = "-",Type="Ambiguous.Symbol")
final.dups.id <- rbind(final.nodup.id,duplicate.ids.df)
return(final.dups.id)
}
}
genes <- trimws(gene.symbols)
hgnc <- gene.file
hgnc.approved <- check.approved(genes,hgnc)
hgnc.synonyms <- check.synonyms(genes[! genes %in% hgnc.approved$Gene.Symbol],hgnc)
hgnc.previous <- check.previous(genes[! genes %in% c(hgnc.approved$Gene.Symbol,hgnc.synonyms$Gene.Symbol)],hgnc)
genes.not.found <- genes[! genes %in% c(hgnc.approved$Gene.Symbol,hgnc.synonyms$Gene.Symbol,hgnc.previous$Gene.Symbol)]
hgnc.notfound <- data.frame(HGNC.ID = rep("-",length(genes.not.found)),Gene.Symbol = genes.not.found) %>% mutate_if(is.factor,as.character) %>%
mutate(Type = "Notfound.ProteinCoding.Symbol")
hgnc.all <- rbind(hgnc.approved,hgnc.synonyms,hgnc.previous,hgnc.notfound)
duplicates.symbol <- hgnc.all$Gene.Symbol[duplicated(hgnc.all$Gene.Symbol)]
results.noduplicated.symbol <- check.duplicates.symbol(hgnc.all,duplicates.symbol)
duplicates.id <- results.noduplicated.symbol$HGNC.ID[duplicated(results.noduplicated.symbol$HGNC.ID)& results.noduplicated.symbol$HGNC.ID!="-"]
results.noduplicated.id <- check.duplicates.id(results.noduplicated.symbol,duplicates.id)
results.final <- results.noduplicated.id
return(results.final)
}
|
2c638fdd3b2bec551d977f39119ef02e2e27072d
|
52802ff28ca37aa7d028c1411b2e3948ecbf02f0
|
/text_mining/tidy_text/Ch03_tf_idf.R
|
49041e9e037006d7d10c74ce83723439a25282c9
|
[] |
no_license
|
PyRPy/ML_Py_Templates
|
a4cd06c5e0cc54ccf544269ae4bf3a8aece15b85
|
677d29207c459bbc9e89e0f1239a1792f128a413
|
refs/heads/master
| 2022-02-23T06:18:51.466170
| 2022-02-16T22:52:02
| 2022-02-16T22:52:02
| 167,294,425
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,250
|
r
|
Ch03_tf_idf.R
|
# word and document frequency: tf-idf
# Term frequency in Jane Austen's novels
# What are the most commonly used words in Jane Austen's novels?
library(dplyr)
library(janeaustenr)
library(tidytext)
book_words <- austen_books() %>%
unnest_tokens(word, text) %>%
count(book, word, sort = TRUE)
total_words <- book_words %>%
group_by(book) %>%
summarize(total = sum(n))
book_words <- left_join(book_words, total_words)
book_words
# let's look at the distribution of n/total for each novel, the
# number of times a word appears in a novel divided by the total number
# of terms (words) in that novel.
library(ggplot2)
ggplot(book_words, aes(n/total, fill = book)) +
geom_histogram(show.legend = FALSE) +
xlim(NA, 0.0009) +
facet_wrap(~book, ncol = 2, scales = "free_y")
# --- Zipf's law ---
# long-tailed distributions are so common in any given corpus of natural language
freq_by_rank <- book_words %>%
group_by(book) %>%
mutate(rank = row_number(),
`term frequency` = n/total)
freq_by_rank
# Zipf's law is often visualized by plotting rank on the x-axis and term frequency on
# the y-axis, on logarithmic scales.
freq_by_rank %>%
ggplot(aes(rank, `term frequency`, color = book)) +
geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) +
scale_x_log10() +
scale_y_log10()
# view this as a broken power law with, say, three sections
rank_subset <- freq_by_rank %>%
filter(rank < 500,
rank > 10)
lm(log10(`term frequency`) ~ log10(rank), data = rank_subset)
# Coefficients:
# (Intercept) log10(rank)
# -0.6226 -1.1125
freq_by_rank %>%
ggplot(aes(rank, `term frequency`, color = book)) +
geom_abline(intercept = -0.62, slope = -1.1, color = "gray50", linetype = 2) +
geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) +
scale_x_log10() +
scale_y_log10()
# --- The bind_tf_idf function ---
book_words <- book_words %>%
bind_tf_idf(word, book, n)
book_words
# look at terms with high tf-idf in Jane Austen's works.
book_words %>%
select(-total) %>%
arrange(desc(tf_idf))
# visualization for these high tf-idf words
book_words %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word)))) %>%
group_by(book) %>%
top_n(15) %>%
ungroup() %>%
ggplot(aes(word, tf_idf, fill = book)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~book, ncol = 2, scales = "free") +
coord_flip()
# !!! This is the point of tf-idf; it identifies words that are important to one document
# within a collection of documents.
# --- A corpus of physics texts ---
library(gutenbergr)
physics <- gutenberg_download(c(37729, 14725, 13476, 5001),
meta_fields = "author")
str(physics)
# find out how many times each word was used in each text
physics_words <- physics %>%
unnest_tokens(word, text) %>%
count(author, word, sort = TRUE)
physics_words
# calculate tf-idf, then visualize the high tf-idf words
plot_physics <- physics_words %>%
bind_tf_idf(word, author, n) %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word)))) %>%
mutate(author = factor(author, levels = c("Galilei, Galileo",
"Huygens, Christiaan",
"Tesla, Nikola",
"Einstein, Albert")))
plot_physics %>%
group_by(author) %>%
top_n(15, tf_idf) %>%
ungroup() %>%
mutate(word = reorder(word, tf_idf)) %>%
ggplot(aes(word, tf_idf, fill = author)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~author, ncol = 2, scales = "free") +
coord_flip()
# Very interesting indeed. One thing we see here is "eq" in the Einstein text?!
library(stringr)
physics %>%
filter(str_detect(text, "eq\\.")) %>%
select(text)
# "K1" is the name of a coordinate system for Einstein:
physics %>%
filter(str_detect(text, "K1")) %>%
select(text)
# "AB", "RC", and so forth are names of rays, circles, angles, and so forth for Huygens.
physics %>%
filter(str_detect(text, "AK")) %>%
select(text)
# remove some of these less meaningful words
mystopwords <- tibble(word = c("eq", "co", "rc", "ac", "ak", "bn",
"fig", "file", "cg", "cb", "cm"))
physics_words <- anti_join(physics_words, mystopwords, by = "word")
plot_physics <- physics_words %>%
bind_tf_idf(word, author, n) %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word)))) %>%
group_by(author) %>%
top_n(15, tf_idf) %>%
ungroup() %>%
mutate(author = factor(author, levels = c("Galilei, Galileo",
"Huygens, Christiaan",
"Tesla, Nikola",
"Einstein, Albert")))
ggplot(plot_physics, aes(word, tf_idf, fill = author)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~author, ncol = 2, scales = "free") +
coord_flip()
|
d7d6f874f83e6f935ef8dfde3520a8a8009cd034
|
12918ea5a65ded9a9f371c7b0759a6eea51cf5ab
|
/ratio_over_season.R
|
ff72068734fe6157b1b597ee7acf9225183aa9a9
|
[] |
no_license
|
dengyishuo/BPL
|
17673e1a39941deca086d1feb9b6a0027a38ce23
|
94c91d50de3350d0e1e4b2454dda2ba2abc9300c
|
refs/heads/master
| 2021-01-20T12:00:46.224195
| 2014-04-21T16:37:17
| 2014-04-21T16:37:17
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,497
|
r
|
ratio_over_season.R
|
#process data- 8 season 380*8 = 3040 onservations
d1 = read.csv("PL2012-13.csv",header = T)
d2 = read.csv("PL2011-12.csv",header = T)
d3 = read.csv("PL2010-11.csv",header = T)
d4 = read.csv("PL2009-10.csv",header = T)
d5 = read.csv("PL2008-09.csv",header = T)
d6 = read.csv("PL2007-08.csv",header = T)
d7 = read.csv("PL2006-07.csv",header = T)
d8 = read.csv("PL2005-06.csv",header = T)
label = c("Date","HomeTeam","AwayTeam","FTHG","FTAG","Lambda.H.","Lambda.A.")
data1 = d1[,label]
data2 = d2[,label]
data3 = d3[,label]
data4 = d4[,label]
data5 = d5[,label]
data6 = d6[,label]
data7 = d7[,label]
data8 = d8[,label]
#input: raw data
#out put: list contains: data, no. of week, three mean for each week
#for weeks with no game, simply remove that week
process_date = function(data){
data[,"Date"] = as.Date(data[,"Date"], "%d/%m/%Y")
data = data[order(data[,"Date"]),]
data[,"Date"] = as.numeric(data[,"Date"] - data[1,"Date"]) %/% 7
#for different season, number of week may different
nweek = max(data[,"Date"])+1
# goal_H + goal_A
actual_total_goal = data[,"FTHG"]+data[,"FTAG"]
implied_total_goal = data[,"Lambda.H."]+data[,"Lambda.A."]
data = data.frame(data,actual_total_goal,implied_total_goal)
#for each week plot
a1 = rep(0,nweek) #store average actual total goal for each week
a2 = rep(0,nweek)#store average implied total goal for each week
a3 = rep(0,nweek) #ratio
for (i in 1 : nweek){
d = data[,"Date"] == (i-1)
a1[i] = mean(data[d,"actual_total_goal"])
a2[i] = mean(data[d,"implied_total_goal"])
a3[i] = a1[i]/a2[i]
}
mean_actual = a1[!is.na(a1)]
mean_implied = a2[!is.na(a2)]
ratio = a3[!is.na(a3)]
list(data, nweek,mean_actual,mean_implied,ratio)
}
require(ggplot2)
for(i in 1 :8) {
assign((paste("list",i,sep="")),process_date(get(paste("data",i,sep=""))))
temp = get(paste("list",i,sep=""))
#plot(temp[[3]],type="l",main = paste("Average Total Goal for week",i,sep=" "))
#plot(temp[[4]],type="l",main = paste("Average Implied Goal for week",i,sep=" "))
#plot(temp[[5]],type="l",main = paste("Ratio of actual over implied for week",i,sep=" "))
#ts.plot(as.ts(temp[[3]]),as.ts(temp[[4]]))
#r = range(temp[[3]])
df = data.frame(temp[[3]],temp[[4]],temp[[5]])
ts.plot(df,lty=c(1:3),main = paste("For season",i,sep=" "),ylim = c(0,5.2))
ts.name = c("Average Total Goal","Average Implied Goal","Ratio of actual over implied")
legend(0.5,5.2,ts.name,lty=c(1:3),cex=0.5)
}
|
3a5269c902754910b9b08436dcf23729e37ca8e6
|
58f02ca234748a1e0214774f2cc07628fb6dbad4
|
/FMBNanalyzer/FMBNanalyzer_v_2_3_1.R
|
ca506b1fc5bd8a2255b9bcad08f257767e3da2e0
|
[
"MIT"
] |
permissive
|
ep142/FoodMicrobionet
|
0bd2bed7a2127267f2534c461633cff1165d54c4
|
599578aa801af4d87acc7673370fb588b9413281
|
refs/heads/master
| 2023-01-27T19:52:30.431777
| 2023-01-08T10:57:56
| 2023-01-08T10:57:56
| 229,555,940
| 4
| 1
|
MIT
| 2022-09-01T14:42:42
| 2019-12-22T11:14:05
|
HTML
|
UTF-8
|
R
| false
| false
| 64,174
|
r
|
FMBNanalyzer_v_2_3_1.R
|
# FMBN analyzer v2.3.1 beta
# performs graphical and numerical analysis of FMBN data generated with the
# ShinyFMBN app
# for this to work correctly
# perform a search in ShinyFMBN and export the __agg__ file: see the manual for ShinyFMBN on [Mendeley Data](https://data.mendeley.com/datasets/8fwwjpm79y/4) for further details
# put the file (you will find it in the output -> aggdata folder located in the app folder) in a new folder containing this template
# create a RStudio project https://support.rstudio.com/hc/en-us/articles/200526207-Using-RStudio-Projects for that folder
# set the options for filtering, saving etc.
# Preparatory steps -------------------------------------------------------
# install/load packages -------------------------------------------------
# need to use plyr:: because of conflicts
# there are also other potential conflicts between vegan and bipartite
.cran_packages <- c("plyr", "reshape2", "gplots", "tidyverse",
"randomcoloR", "bipartite", "RColorBrewer",
"ggrepel", "vegan")
.inst <- .cran_packages %in% installed.packages()
if(any(!.inst)) {
install.packages(.cran_packages[!.inst])
}
sapply(.cran_packages, require,
character.only = TRUE)
opar <- par(no.readonly = T)
set.seed(1234)
#########1#########2#########3#########4#########5#########6#########7#########8
# list files in working directory for convenience only
filelist <- list.files()
#########1#########2#########3#########4#########5#########6#########7#########8
# Load data ---------------------------------------------------------------
# (generated with the ShinyFMBN app or with FMBNmakefilesv5_x ) #
#########1#########2#########3#########4#########5#########6#########7#########8
# there must be only one file ending with _aggRDS in the project folder
input_file_name <- filelist[str_detect(filelist, "_agg.RDS")]
# this is overkill, really
if(length(input_file_name>1)) input_file_name <- input_file_name[1]
file_name_prefix <- str_remove(input_file_name, "_agg.RDS")
file_name <- basename(file_name_prefix)
input_data <- readRDS(input_file_name)
# list, containing the following
# studies the study table
# OTU_table the OTU table (taxa are columns) with absolute freqs
# OTU_table_relf OTU table (taxa are rows, a_label) with rel freqs
# sample_metadata the sample metadata
# taxa_metadata the taxa metadata
# edge_table basically, the OTU_table_relf in long format, with frequencies in %
# node_table info on sample and OTU nodes
# i_graph an igraph bipartite object
# references data frame containing the references
# version text decribing the FMBN version
# sample_agg the level of aggregation for samples
# tax_agg the level of aggregation for taxa
# diagnostic_f a diagnostic code related to the state of the app, irrelevant for
# the analysis
# check if the data are in the right format
check_names <- names(input_data) == c("studies","OTU_table", "OTU_table_relf",
"sample_metadata", "taxa_metadata",
"edge_table", "node_table", "i_graph",
"references", "version", "sample_agg",
"tax_agg", "diagnostic_f")
if(length(check_names) != sum(check_names)) cat("UNKNOWN ERROR: there must be something wrong with your data")
#########1#########2#########3#########4#########5#########6#########7#########8
# Set options -------------------------------------------------------------
#########1#########2#########3#########4#########5#########6#########7#########8
# options for saving graphs
savegraph <- T
# set savegraph to T if you want to save graphs (some graphs won't be displayed)
# use F if you don't want to save
graphresolution <- 300 # in dpi
graphsizein <- 7 # size in inches, overridden in some graphs
graphtype <- "tif" # alternative value is "pdf"
gfileext <- ifelse (graphtype == "pdf", ".pdf", ".tiff")
# options for number of OTU groups in sample and OTU palettes
overrides <- F # set to T if you want more than 15 categories in sample palettes
overrideOTU <- F # set to T if you want more than 15 categories in OTU palettes
# The "Other" column has no taxonomic meaning unless it is an aggregate of
# several OTUs (see instructions in the FMBN_make_files* script) and will be
# removed by default. The "Chloroplast" and "Mitochondria" columns only indicate
# contamination with chloroplast or mitochondria DNA/RNA and should also be
# removed. Set to F if you want to keep these columns in OTU tables.
# Eukaryota can also be removed
removeOther <- T
removeKonly <- T # removes the taxa which have been identified only at the level of Kingdom
removeChloroplast <- T
removeMitochondria <- T
removeEukaryota <- T
# options for filtering samples and OTUs
# if filtersamples == T the sample filter will be applied (overridden if sample_agg == "exp. code")
# if filterOTUs == T the maxrelab filter will be applied
filtersamples <- T
filterOTUs <- T
# filter for OTU abundance. OTUs with a maximum relative abundance < abtreshold
# will be removed or pooled (see below)
# keep the value low or you will loose too many OTUs between filtering and
# rarefaction
abtreshold <- 0.005
# options for taxa prevalence filter (only applicable when sample aggregation is sample)
prev_filter <- F
prev_treshold <- 0.05 # as fraction of samples
prev_filter_flag <- F
# flags for filtering, will be set to true if filtering is performed
filtersamples_flag <- F
filterOTU_flag <- F
# samples/sample groups with less than minsampleab will be removed
minsampleab <- 1000
# rarefy? if T vegan::rrarefy() will be used to extract a random rarefied
# matrix. If raremin is T will rarefy to the closest hundred below the
# minimum number of sequences, otherwise will rarefy to raren.
# The default is F, although rarefaction will be performed for bipartite
# analysis
# option for performing rarefaction analysis on the unfiltered matrix
dorareanalysis <- F
rareoption <- T
raremin <- T
raren <- 10000
rarefy_flag <- F # will be set to T if rarefaction is performed
# pooling of filtered OTUs: if pool is T, abundances of OTUs removed by
# filtering are pooled and shown as "Other" in bar charts; does not apply
# to heat maps and MDS.
pool <- T
# maximum number of OTUs allowed in stacked bar charts
# will be used to generate the OTU palette with randomcoloR
maxOTUcats <- 25
# maximum number of "species" for which prevalence and abundance is saved
topp <- 50
# if the number of "species" in data is lower, then it is going to be the
# max number of species
# maximum number of "species" in box plots, heat maps and NMDS
# must be <topp
topn <-25
# max number of genera in the bonus plot
topx <- 19
# max "samples" or sample categories for barplots and boxplots
max_samples <- 25
# logging the color scale in heat maps; set to T if you want the scale
# in log10(relabundance); set to F if you want to keep a linear scale
loghmscale <- T
#########1#########2#########3#########4#########5#########6#########7#########8
# make a qualitative color palette
n <- maxOTUcats
rpalette <- distinctColorPalette(n)
names(rpalette) <- NULL
#########1#########2#########3#########4#########5#########6#########7#########8
#########1#########2#########3#########4#########5#########6#########7#########8
# creating user defined functions for repetitive tasks
#########1#########2#########3#########4#########5#########6#########7#########8
# a function for coarse taxonomic filtering
filter_taxa_1 <- function(OTUmatrix){
# optionally remove "Other", "Chloroplast", "Mitochondria" and "Eukaryota"
# and taxa with identification only at the level of Kingdom
if (removeOther) {
OTUmatrix <- OTUmatrix[, colnames(OTUmatrix)!= "Other"]
}
if (removeChloroplast){
chl_labels <- input_data$taxa_metadata %>%
dplyr::filter(label == "Chloroplast" | class == "Chloroplast") %>%
pull(label)
OTUmatrix <- OTUmatrix[, -which(colnames(OTUmatrix) %in% chl_labels)]
}
if (removeMitochondria){
mit_labels <- input_data$taxa_metadata %>%
dplyr::filter(label == "Mitochondria" | family == "Mitochondria") %>%
pull(label)
OTUmatrix <- OTUmatrix[, -which(colnames(OTUmatrix) %in% mit_labels)]
}
if (removeEukaryota){
euk_labels <- input_data$taxa_metadata %>%
dplyr::filter(domain == "Eukaryota") %>%
pull(label)
if (length(euk_labels > 0)){
OTUmatrix <- OTUmatrix[, -which(colnames(OTUmatrix) %in% euk_labels)]
}
}
if (removeKonly){
Konly <- input_data$taxa_metadata %>%
dplyr::filter(is.na(phylum)) %>%
pull(label)
OTUmatrix <- OTUmatrix[, -which(colnames(OTUmatrix) %in% Konly)]
}
return(OTUmatrix)
}
# a function for creating OTU palettes
# Create palette for OTUs. See above for instructions to override defaults
# this should be transformed into a function but I don't have time
make_OTU_palettes <- function(OTUmatrix,
taxa_metadata = input_data$taxa_metadata,
tax_agg = input_data$tax_agg,
override_OTU = overrideOTU){
ET3 <- taxa_metadata %>%
dplyr::filter(label %in% colnames(OTUmatrix))
# must fill the NAs with info from higher taxa
ET3 <- ET3 %>%
mutate(domain = ifelse(is.na(domain),"Other", domain)) %>%
mutate(phylum = ifelse(is.na(phylum), domain, phylum)) %>%
mutate(class = ifelse(is.na(class), phylum, class)) %>%
mutate(order = ifelse(is.na(order), class, order)) %>%
mutate(family = ifelse(is.na(family), order, family))
ET3$class <- as.factor(ET3$class)
if (tax_agg == "class"){
ET3$family <- ET3$class
} else {
ET3$family <- as.factor(ET3$family)
}
ncolsfamily <- nlevels(ET3$family)
ncolsclass <- nlevels(ET3$class)
familypalette <- if(ncolsfamily<=12) {
brewer.pal(ncolsfamily, "Set3")[1:ncolsfamily]
} else {
distinctColorPalette(ncolsfamily)
}
classpalette <- if(ncolsclass<=12) {
brewer.pal(ncolsclass, "Set3")[1:ncolsclass]
} else {
distinctColorPalette(ncolsclass)
}
OTUcolors <- ET3[, c("label", "class", "family")]
fcolors <- data.frame(family = levels(OTUcolors$family),
fcolor = familypalette)
ccolors <- data.frame(class = levels(OTUcolors$class),
ccolor = classpalette)
# Merge the palettes with label and lineage info
OTUcolors <- merge(OTUcolors, ccolors, by = "class")
OTUcolors <- merge(OTUcolors, fcolors, by = "family")
OTUcolors <- arrange(OTUcolors, label)
# set the palette to use
catOTUs <- "class"
if (input_data$tax_agg != "class"){
catOTUs <- "family"
familystring <- paste(" and ", nlevels(OTUcolors$family),
" families.",sep = "")
if (!override_OTU) {
catOTUs <- ifelse(nlevels(OTUcolors$family)<=15,
"family","class")
}
if (catOTUs == "class") {
myOTUpalette <- as.character(OTUcolors$ccolor)
myOTUtext <- as.character(ccolors$class)
myOTUcolors <- as.character(ccolors$ccolor)
} else {
myOTUpalette <- as.character(OTUcolors$fcolor)
myOTUtext <- as.character(fcolors$family)
myOTUcolors <- as.character(fcolors$fcolor)
}
}
OTU_palette_list <- list(
palette = myOTUpalette,
text = myOTUtext,
colors = myOTUcolors,
OTU_colors_df = OTUcolors,
cat_OTU_flag = catOTUs
)
return(OTU_palette_list)
}
#########1#########2#########3#########4#########5#########6#########7#########8
# Fix the sample_metadata -------------------------------------------------
#########1#########2#########3#########4#########5#########6#########7#########8
# keep a copy of sample_metadata
sample_metadata <- input_data$sample_metadata
row.names(sample_metadata) <- sample_metadata$label
sample_metadata_f <- sample_metadata # will be modified by sample filtering
# note that input_data$sample_agg can take values "sample" and "exp. code"
#########1#########2#########3#########4#########5#########6#########7#########8
# Create a "filtered" version of the OTU table -------------------------------
#########1#########2#########3#########4#########5#########6#########7#########8
tOTUm <- input_data$OTU_table
totseqs <- rowSums(tOTUm[complete.cases(tOTUm),])
OTUmatrixf <- tOTUm[complete.cases(tOTUm),]
# sample filter first
# optionally remove samples/sample groups with less than minsampleab sequences
# filtered samples will be also removed from the sample metadata table
# reset filtersamples if aggregation is exp. code
if(input_data$sample_agg == "exp. code") filtersamples <-F
if (filtersamples){
samples_to_keep <- which(totseqs >= minsampleab)
if(length(samples_to_keep) < nrow(OTUmatrixf)){
OTUmatrixf <- OTUmatrixf[samples_to_keep,]
totseqs <- rowSums(OTUmatrixf)
keepMetadata <- which(row.names(sample_metadata) %in% names(samples_to_keep))
sample_metadata_f <- sample_metadata_f[keepMetadata,]
}
filtersamples_flag <- T
}
# optionally remove "Other", "Chloroplast", "Mitochondria" and "Eukaryota"
# and taxa with identification only at the level of Kingdom
OTUmatrixf <- filter_taxa_1(OTUmatrix = OTUmatrixf)
# optionally rarefy the samples with vegan::rrarefy()
if (rareoption){
rarenn <- ifelse(raremin, 100*floor(min(totseqs)/100), raren)
OTUmatrixf <- vegan::rrarefy(OTUmatrixf, rarenn)
# drop colums with 0 sum
OTUmatrixf <- OTUmatrixf[, colSums(OTUmatrixf)>0]
OTUtokeep <- colnames(OTUmatrixf)
rarefy_flag <- T
}
# optionally filter OTUs which are rare and/or have low prevalence
if (filterOTUs){
OTUmatrixf_relab <- OTUmatrixf/rowSums(OTUmatrixf)
maxrelab <- apply(OTUmatrixf_relab, 2, max)
# OTU to keep (mar rel. abundance > abtreshold AND prevalence > prev_treshold)
prevdf <- apply(X = OTUmatrixf,
MARGIN = 2,
FUN = function(x){sum(x > 0)})
min_rel_ab <- apply(X = OTUmatrixf_relab,
MARGIN = 2,
FUN = function(x){min(x)})
max_rel_ab <- apply(X = OTUmatrixf_relab,
MARGIN = 2,
FUN = function(x){max(x)})
# Add taxonomy and total read counts to this data.frame
prevdf <- data.frame(Prevalence = prevdf,
TotalAbundance = colSums(OTUmatrixf),
min_rel_ab = min_rel_ab,
max_rel_ab = max_rel_ab)
prevdf <- prevdf %>%
rownames_to_column(var = "label") %>%
left_join(., select(input_data$taxa_metadata, label, phylum:species))
if(prev_filter){
pass_prev_filter <- dplyr::filter(select(prevdf, label, Prevalence),
Prevalence > floor(nrow(sample_metadata)*prev_treshold)) %>%
pull(label)
OTUtokeep <- intersect(
names(which(maxrelab >= abtreshold)),
pass_prev_filter)
} else {
OTUtokeep <- names(which(maxrelab >= abtreshold))
}
prevdf <- prevdf %>%
mutate(relAbundance = TotalAbundance/sum(TotalAbundance),
pass_maxrelab_treshold = ifelse(label %in% OTUtokeep, "T", "F"))
# make a plot
# prevalence vs abundance graph
OTUmatrixf <- OTUmatrixf[, which(colnames(OTUmatrixf_relab) %in% OTUtokeep)]
f_seq_ret <- round(sum(OTUmatrixf)/sum(tOTUm),4)
title_text <- "Prevalence vs. abundance, by Phylum"
subtitle_text <- paste("using the filters you retain ", length(OTUtokeep),
" taxa (triangles) out of ", ncol(tOTUm),
" (", f_seq_ret*100, "% of init. seqs.)", sep ="")
# a prevalence and abundance plot
prev_ab_plot <- ggplot(prevdf, aes(x = TotalAbundance, y = Prevalence / nrow(OTUmatrixf),
shape = as.factor(pass_maxrelab_treshold), color = phylum)) +
geom_point(size = 2, alpha = 0.7) +
facet_wrap( ~ phylum) +
geom_hline(yintercept = ifelse(prev_filter, prev_treshold, 0),
alpha = 0.5,
linetype = 2) +
labs(x = "total abundance", y = "Prevalence [Frac. Samples]",
shape = 'pass ab. treshold',
title = title_text, subtitle = subtitle_text) +
scale_x_log10() +
scale_y_continuous(minor_breaks = seq(0, 1, 0.05)) +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90))
print(prev_ab_plot)
filterOTU_flag <- T
if(prev_filter) prev_filter_flag <- T
}
prevdf <- prevdf %>%
mutate(relprev = Prevalence/nrow(OTUmatrixf)) %>%
arrange(-relprev, - relAbundance)
filtOTU <- ncol(tOTUm)-ncol(OTUmatrixf)
# fix sample and taxa
sample_metadata_f <- sample_metadata_f %>%
mutate_at(vars(llabel, s_type, L1, L4, L6, nature, process, spoilage,
studyId, target1, target2), as.factor)
taxa_metadata_f <- input_data$taxa_metadata %>%
dplyr::filter(label %in% colnames(OTUmatrixf)) # maybe should create factors here
# print a summary
cat("The original OTU table has", ncol(tOTUm), "OTUs and ",
nrow(tOTUm), "samples/sample groups.", "\n", "\n", sep = " ")
cat("After filtering there are", "\n", nlevels(sample_metadata$llabel),
"llabel groups", "\n", sep = " ")
cat(nlevels(sample_metadata_f$L1), "food groups", "\n", sep = " ")
cat(nlevels(sample_metadata_f$L4), "food subgroups", "\n", sep = " ")
cat("After filtering/rarefaction you have removed", filtOTU, "OTU out of",
ncol(tOTUm), "\n", "\n", sep = " ")
topp <- ifelse(topp<nrow(prevdf), topp, nrow(prevdf))
toppprev <- slice(prevdf,1:topp) %>% select(label, phylum, class, order, family,
genus, relAbundance, min_rel_ab,
max_rel_ab, relprev)
write_tsv(toppprev, str_c("file_name_prefix","topp.txt",sep="_"))
#########1#########2#########3#########4#########5#########6#########7#########8
# Make palettes for samples -----------------------------------------------
# The script will determine if sample labels are llabels or sample ids, based #
# on aggSample, loaded with the input data. #
# If the number of L4 levels is <=15 L4 will be used for palettes. #
# L1 will be used otherwise. To override this set overrides to T (unwise, may #
# result in problems in legends). #
#########1#########2#########3#########4#########5#########6#########7#########8
catsamples <- "L4"
maxslevels <- max(c(nlevels(sample_metadata_f$L1),
nlevels(sample_metadata_f$L4)))
if(maxslevels>12) {
lspalette <- distinctColorPalette(maxslevels)
names(lspalette) <- NULL
}
if (!overrides){
catsamples <- ifelse(nlevels(sample_metadata_f$L4)<=15,
"L4","L1")
}
samplenames <- data.frame(label = row.names(OTUmatrixf), stringsAsFactors = F)
cat("There are ", nrow(OTUmatrixf), " samples in the data set, belonging to",
"\n", nlevels(sample_metadata_f$L1), " food groups and ",
nlevels(sample_metadata_f$L4), " subgroups.", "\n",
catsamples, " will be used for palettes.", sep ="")
groupcolors <- inner_join(samplenames, select(sample_metadata_f, label, L1, L4))
ncolfoodg <- nlevels(groupcolors$L1)
ncolfoodsg <- nlevels(groupcolors$L4)
foodgpalette <- if(ncolfoodg<=12) {
brewer.pal(ncolfoodg, "Paired")[1:ncolfoodg]
} else {
distinctColorPalette(ncolfoodg)
}
foodsgpalette <- if(ncolfoodsg<=12) {
brewer.pal(ncolfoodsg, "Paired")[1:ncolfoodsg]
} else {
distinctColorPalette(ncolfoodsg)
}
foodgpalette <- data.frame(cbind(L1 = levels(groupcolors$L1),
fgcolor = foodgpalette))
foodsgpalette <- data.frame(L4 = levels(groupcolors$L4),
fsgcolor = foodsgpalette)
# Merge the palettes with sample names and metadata
groupcolors <- merge(groupcolors, foodgpalette, by = "L1")
groupcolors <- merge(groupcolors, foodsgpalette, by = "L4")
groupcolors <- groupcolors %>% dplyr::arrange(label)
# Set the palette and colors and text for legends
if (catsamples == "L1") {
myspalette <- as.character(groupcolors$fgcolor)
mystext <- as.character(foodgpalette$L1)
myscolors <- as.character(foodgpalette$fgcolor)
} else {
myspalette <- as.character(groupcolors$fsgcolor)
mystext <- as.character(foodsgpalette$L4)
myscolors <- as.character(foodsgpalette$fsgcolor)
}
# Show the palette and plots a legend in a horizontal format
# It might be a good idea to save the legend graph
ltitle <- paste("Sample groups ", "(", catsamples, ", FoodEx2 class.)", sep = "")
plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n", ann = FALSE)
legend("center", pch=15, legend = mystext, col = myscolors,
bty = "n",
# ncol = if(length(mystext>10)) 2 else 1,
title = ltitle)
if (savegraph) {
par(pin = c(graphsizein,graphsizein))
if (graphtype == "pdf") {
pdf(file = paste(file_name, "fpal.pdf", sep = ""), width = graphsizein,
height = graphsizein, paper = "default")
} else {
tiff(filename = paste(file_name, "fpal.tif", sep = ""),
width = graphsizein, height = graphsizein, units = "in",
res = graphresolution, compression = "none")
}
plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n", ann = FALSE)
legend("center", pch=15, legend = mystext, col = myscolors,
bty = "n",
ncol = if(length(mystext>10)) 2 else 1,
title = ltitle)
par(opar)
dev.off()
}
# Clean up (if you want to recreate a palette you have to run again)
rm(foodgpalette, foodsgpalette, ncolfoodg, ncolfoodsg, samplenames)
#########1#########2#########3#########4#########5#########6#########7#########8
# Rarefaction analysis and diversity indices (optional) -----------------------
#########1#########2#########3#########4#########5#########6#########7#########8
if (dorareanalysis){
richness <- t(estimateR(tOTUm)) # should be saved or given option to save or printed
rarecurve(tOTUm, step = 10, col = myspalette)
if (savegraph) {
par(pin = c(graphsizein,graphsizein))
if (graphtype == "pdf") {
pdf(file = paste(file_name, "rarecurve.pdf", sep = ""), width = graphsizein,
height = graphsizein, paper = "default")
} else {
tiff(filename = paste(file_name, "rarecurve.tif", sep = ""),
width = graphsizein, height = graphsizein, units = "in",
res = graphresolution, compression = "none")
}
rarecurve(tOTUm, step = 10, col = myspalette)
par(opar)
dev.off()
}
}
#########1#########2#########3#########4#########5#########6#########7#########8
# Bar plots at the genus, family and class level --------------------------
# Using rel. frequencies before filtering/rarefaction (if any). #
# The plots are generated only if the number of categories is <=maxOTUcats #
# You can change this by setting maxOTUcats in line 119 #
# Note: this section will attempt to use a color brewer qualitative palette #
# if the number of categories for the taxonomic level are <=12, #
# otherwise it will use a default palette and difference in colours may be #
# difficult to perceive. #
#########1#########2#########3#########4#########5#########6#########7#########8
# need the following because OTUmatrixf_relab is only created if filterOTUs is T
if(!exists("OTUmatrixf_relab")) OTUmatrixf_relab <- OTUmatrixf/rowSums(OTUmatrixf)
# column Other may be close to 0 if no taxa filtering is applied
if (pool){
OTUmatrixf_relab_2 <- cbind(OTUmatrixf_relab,1-rowSums(OTUmatrixf_relab))
colnames(OTUmatrixf_relab)[ncol(OTUmatrixf_relab)] <-"Other"
} else {
OTUmatrixf_relab_2 <- OTUmatrixf_relab/rowSums(OTUmatrixf_relab)
}
otm <- as.data.frame(OTUmatrixf_relab_2) %>% rownames_to_column(var = "source")
edge_table_filtered <- pivot_longer(otm, cols = 2:ncol(otm), names_to = "target", values_to = "weight")
# join taxonomic information and recode factors to move Other at the top
edge_table_filtered <- left_join(edge_table_filtered,
select(taxa_metadata_f, label, class:species),
by = c("target" = "label")) %>%
dplyr::filter(weight >0)
# setting flags for bar plots
bpgenusf <- F
bpfamilyf <- F
bpclassf <- F
if (input_data$tax_agg != "class"){
if (input_data$tax_agg != "family") {
# Genus level
e_table_f_agg <- edge_table_filtered %>%
group_by(source, genus) %>%
dplyr::summarise(weight = sum(weight)) %>%
mutate(genus = ifelse(is.na(genus), "Other", genus))
taxa_levels <- unique(e_table_f_agg$genus)
if ("Other" %in% taxa_levels) {
taxa_levels <- c("Other",setdiff(taxa_levels,"Other"))
}
e_table_f_agg$genus <- factor(e_table_f_agg$genus, levels = taxa_levels)
n_taxa <- length(levels(e_table_f_agg$genus))
if (n_taxa<=maxOTUcats) {
bpgenusf <- T
sbplot <- ggplot(data = e_table_f_agg, aes(x = source, y= weight, fill = genus))
if (n_taxa>=12) {
bpgenus <- sbplot + geom_col(position = "fill") +
scale_fill_manual(values = rpalette[1:n_taxa]) +
labs(title = "Relative abundance, genus",
x= "Samples/Sample groups",
y= "Relative abundance") +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.background= element_rect(fill = "white"),
plot.title = element_text(hjust = 0.5))
print(bpgenus)
} else {
bpgenus <- sbplot + geom_col(position = "fill") +
labs(title = "Relative abundance, genus",
x= "Samples/Sample groups",
y= "Relative abundance") +
scale_fill_brewer(type = "qual", palette = "Paired") +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.background= element_rect(fill = "white"),
plot.title = element_text(hjust = 0.5))
print(bpgenus)
}
} else {
# con shiny non deve restituire un messaggio ma un grafico
# o devo mettere un campo output con un messaggio
cat("Too many genera, genus bar chart aborted...","\n","\n")
}
}
# Family level
e_table_f_agg <- edge_table_filtered %>%
group_by(source, family) %>%
dplyr::summarise(weight = sum(weight)) %>%
mutate(family = ifelse(is.na(family), "Other", family))
taxa_levels <- unique(e_table_f_agg$family)
if ("Other" %in% taxa_levels) {
taxa_levels <- c("Other",setdiff(taxa_levels,"Other"))
}
e_table_f_agg$family <- factor(e_table_f_agg$family, levels = taxa_levels)
n_taxa <- length(levels(e_table_f_agg$family))
if (n_taxa <= maxOTUcats) {
bpfamilyf <- T
sbplot <- ggplot(data = e_table_f_agg, aes(x = source, y= weight, fill = family))
if (nlevels(e_table_f_agg$Family)>=12) {
bpfamily <- sbplot + geom_col(position = "fill") +
scale_fill_manual(values = rpalette[1:n_taxa]) +
labs(title = "Relative abundance, Family",
x= "Samples/Sample groups",
y= "Relative abundance") +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.background= element_rect(fill = "white"),
plot.title = element_text(hjust = 0.5))
bpfamily
} else {
bpfamily <- sbplot + geom_col(position = "fill") +
labs(title = "Relative abundance, Family",
x= "Samples/Sample groups",
y= "Relative abundance") +
scale_fill_brewer(type = "qual", palette = "Paired") +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.background= element_rect(fill = "white"),
plot.title = element_text(hjust = 0.5))
bpfamily
}
} else {
cat("Too many families, family bar chart aborted...","\n","\n")
}
} else {
cat("OTU aggregated at the Class level, Genus and Family plots skipped")
}
# Class level
e_table_f_agg <- edge_table_filtered %>%
group_by(source, class) %>%
dplyr::summarise(weight = sum(weight)) %>%
mutate(class = ifelse(is.na(class), "Other", class))
taxa_levels <- unique(e_table_f_agg$class)
if ("Other" %in% taxa_levels) {
taxa_levels <- c("Other",setdiff(taxa_levels,"Other"))
}
e_table_f_agg$class <- factor(e_table_f_agg$class, levels = taxa_levels)
n_taxa <- length(levels(e_table_f_agg$class))
sbplot <- ggplot(data = e_table_f_agg, aes(x = source, y= weight, fill = class))
if (n_taxa<=maxOTUcats){
bpclassf <- T
if (n_taxa>=12) {
bpclass <- sbplot + geom_col(position = "fill") +
scale_fill_manual(values = rpalette[1:n_taxa]) +
labs(title = "Relative abundance, Class",
x= "Samples/Sample groups",
y= "Relative abundance") +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.background= element_rect(fill = "white"),
plot.title = element_text(hjust = 0.5))
bpclass
} else {
bpclass <- sbplot + geom_col(position = "fill") +
labs(title = "Relative abundance, Class",
x= "Samples/Sample groups",
y= "Relative abundance") +
scale_fill_brewer(type = "qual", palette = "Paired") +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.background= element_rect(fill = "white"),
plot.title = element_text(hjust = 0.5))
bpclass
}
} else {
cat("Too many classes, class bar chart aborted...","\n","\n")
}
# saving barplots
if (savegraph) {
if (bpgenusf){
ggsave(filename = str_c(file_name_prefix, "bp","genus",gfileext),
plot = bpgenus, width = 7, height = 5, dpi = graphresolution)
}
if (bpfamilyf){
ggsave(filename = str_c(file_name_prefix, "bp","family",gfileext),
plot = bpfamily, width = 7, height = 5, dpi = graphresolution)
}
if (bpclassf){
ggsave(filename = str_c(file_name_prefix, "bp","class",gfileext),
plot = bpclass, width = 7, height = 5, dpi = graphresolution)
}
}
# Box plots for the n (see topn) most abundant taxa -------------------------------
# I am selecting the top n most abundant and prevalent taxa (genera, families, classes,
# depending on taxonomic aggregation in the input file)
# with some exceptions all further analysis will be performed on these taxa
topntaxa <- dplyr::filter(toppprev, !is.na(get(input_data$tax_agg)))
topn <- ifelse(topn<nrow(topntaxa), topn, nrow(topntaxa))
topntaxa <- topntaxa %>%
slice(1:topn) %>%
pull(label)
edge_table_filtered <- edge_table_filtered %>%
dplyr::filter(target %in% topntaxa)
# now join the metadata for samples
sample_data <- sample_metadata_f %>%
select(label, studyId, llabel, foodId, L1, L4, L6)
edge_table_boxplot <- left_join(edge_table_filtered,
sample_data,
by = c("source" = "label"))
# get the variable to use on x axis
# it will be the lowest aggregation level with <= max_samples levels
sample_nlevels <- sample_data %>%
summarize(across(.fns = n_distinct)) %>%
select(llabel, foodId, L6, L4, L1)
xaxisvariable <- colnames(sample_nlevels)[which(sample_nlevels[1,]<=max_samples)[1]]
# now plot
topnboxplot <- ggplot(edge_table_boxplot, mapping = aes(x = get(xaxisvariable), y = weight))
topnboxplot +
geom_boxplot() +
geom_jitter(alpha = 0.2) +
facet_wrap(~target) +
labs(x= xaxisvariable, y= "rel.ab.") +
scale_y_log10(breaks = c(1E-5, 1E-4, 1E-3, 1E-2, 1E-1, 1)) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
strip.text = element_text(size = 6))
ggsave(filename = str_c(file_name_prefix, "bxp","topntaxa",gfileext),
width = 9, height = 7, dpi = graphresolution)
#########1#########2#########3#########4#########5#########6#########7#########8
# NMDS with vegan package -------------------------------------------------
#########1#########2#########3#########4#########5#########6#########7#########8
# NMDS with vegan package using Bray-Curtis distance as default
# using OTUmatrixf (absolute abundances) works usually better, but triggers
# the autotransform option in metaMDS; if you want you can use relative
# abundances (use OTUmatrixf3)
# filtering OTUmatrixf (removing samples)
samples_2_keep <- which(row.names(OTUmatrixf) %in% groupcolors$label)
MDSmatrix <- OTUmatrixf
MDSall <- metaMDS(MDSmatrix, trymax = 50)
MDSall
stressplot(MDSall)
plot(MDSall, type = "t")
# get proper plots using ggplot2
# make data frames, add metadata for row names, sort to use palettes
# Create palette for OTUs. See above for instructions to override defaults
# this should be transformed into a function but I don't have time
OTU_palette_data <- make_OTU_palettes(OTUmatrixf)
OTUcolors <- OTU_palette_data$OTU_colors_df
myOTUcolors <- OTU_palette_data$colors
catOTUs <- OTU_palette_data$cat_OTU_flag
speciescoord <- as.data.frame(MDSall$species)
speciescoord <- speciescoord[order(row.names(speciescoord)),] %>%
rownames_to_column("label") %>%
left_join(OTUcolors)
speciescoord$olabel <- OTUcolors[,which(colnames(OTUcolors)==
catOTUs)]
samplecoord <-as.data.frame(MDSall$points)
samplecoord <- samplecoord[order(row.names(samplecoord)),]
samplecoord$slabel <- groupcolors[,which(colnames(groupcolors)==
catsamples)]
rangex <- max(c(max(samplecoord$MDS1),max(speciescoord$MDS1)))
maxx <- rangex
minx <- min(min(samplecoord$MDS1), min(speciescoord$MDS1))
minx <- if_else(abs(minx)-abs(trunc(minx)) < 0.5, trunc(minx)-0.5,trunc(minx)-1)
maxx <- if_else(abs(maxx)-abs(trunc(maxx)) < 0.5, trunc(maxx)+0.5,trunc(maxx)+1)
rangey <- max(c(max(samplecoord$MDS2),max(speciescoord$MDS2)))
miny <- min(min(samplecoord$MDS2), min(speciescoord$MDS2))
miny <- if_else(abs(miny)-abs(trunc(miny)) < 0.5, trunc(miny)-0.5, trunc(miny)-1)
maxy <- rangey
maxy <- if_else(abs(maxy)-abs(trunc(maxy)) < 0.5, trunc(maxy)+0.5,trunc(maxy)+1)
# get the group with less than 12 levels, if any
sample_nlevels_MDS <- sample_data %>%
summarize(across(.fns = n_distinct)) %>%
select(L6, L4, L1)
colorv_MDS <- colnames(sample_nlevels_MDS)[which(sample_nlevels[1,]<=max_samples)[1]]
# sample MDS plot.
MDSsampleplot <- ggplot(data = samplecoord, aes(x = MDS1, y= MDS2))
# may be use ggrepel
MDSsampleplot + geom_point(aes(colour = str_wrap(slabel,20))) +
scale_color_manual(values = myscolors) +
scale_x_continuous(limits = c(minx, maxx), breaks = seq(minx, maxx,0.1)) +
scale_y_continuous(limits = c(miny, maxy), breaks = seq(miny, maxy, 0.1)) +
labs(title = "MDS, samples",
x= "dim(1)",
y= "dim(2)",
colour = catsamples) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
# saving sample metadata
write_tsv(sample_metadata_f, str_c(file_name_prefix, "sample_metadata.txt",
sep = "_"))
samplecoord_2 <- samplecoord %>%
rownames_to_column(var = "label") %>%
left_join(.,select(sample_metadata_f, label, L1:L6))
# need to change a variable name
samplecoord_2 <- samplecoord_2 %>% rename(llabel = label)
MDSsampleplot_2 <- ggplot(data = samplecoord_2, aes(x = MDS1, y= MDS2))
if(sample_nlevels_MDS[1,colorv_MDS]<=12){
MDSsampleplot_2 +
geom_point(aes(colour = str_wrap(get(colorv_MDS),20))) +
geom_text_repel(mapping = aes(label = llabel),
size = I(2.0), alpha = I(0.6), max.overlaps = 20) +
scale_color_brewer(type = "qual", palette = "Paired") +
scale_x_continuous(limits = c(minx, maxx), breaks = seq(minx, maxx,0.1)) +
scale_y_continuous(limits = c(miny, maxy), breaks = seq(miny, maxy, 0.1)) +
labs(title = "MDS, samples",
x= "dim(1)",
y= "dim(2)",
colour = colorv_MDS) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
} else{
MDSsampleplot_2 +
geom_point(aes(colour = str_wrap(get(colorv_MDS),20))) +
geom_text_repel(mapping = aes(label = llabel),
size = I(2.0), alpha = I(0.6), max.overlaps = 20) +
scale_color_manual(values = rpalette) +
scale_x_continuous(limits = c(minx, maxx), breaks = seq(minx, maxx,0.1)) +
scale_y_continuous(limits = c(miny, maxy), breaks = seq(miny, maxy, 0.1)) +
labs(title = "MDS, samples",
x= "dim(1)",
y= "dim(2)",
colour = colorv_MDS) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
}
if (savegraph) {
ggsave(filename =str_c(file_name_prefix, "_MDSsamples",gfileext),
width = 7, height = 5, dpi = graphresolution)
}
# a small change
speciescoord <- speciescoord %>% rename(llabel = label)
# species MDS plot.
MDSOTUplot <- ggplot(data = speciescoord, aes(x = MDS1, y= MDS2))
MDSOTUplot + geom_point(aes(colour = olabel)) +
geom_text_repel(mapping = aes(label = llabel),
size = 2.0, alpha = 0.6, max.overlaps = 20) +
scale_color_manual(values = myOTUcolors) +
scale_x_continuous(limits = c(minx,maxx), breaks = seq(minx,maxx,0.5)) +
scale_y_continuous(limits = c(miny,maxy), breaks = seq(miny,maxy,0.5)) +
labs(title = "Non-metric MDS, OTUs",
x= "dim(1)",
y= "dim(2)",
colour = catOTUs) +
theme_bw()+
theme(plot.title = element_text(hjust = 0.5))
if (savegraph) {
ggsave(filename = str_c(file_name_prefix, "_MDSspecies", gfileext),
width = 7, height = 5, dpi = graphresolution)
}
# biplot
MDSsampleplot +
geom_point(aes(colour = slabel)) +
geom_text_repel(label = row.names(samplecoord),
size = 3,
color = "red", alpha = 0.6, max.overlaps = 20) +
geom_text_repel(data = speciescoord, aes(x= MDS1, y= MDS2, label = llabel),
size = I(2.5), alpha = I(0.6), max.overlaps = 20) +
scale_color_manual(values = myscolors) +
scale_x_continuous(limits = c(minx,maxx), breaks = seq(minx,maxx,0.5)) +
scale_y_continuous(limits = c(miny,maxy), breaks = seq(miny,maxy,0.5)) +
labs(x= "dim(1)",
y= "dim(2)",
colour = catsamples) +
theme_bw() +
theme(legend.key.size = unit(0.1, "in"),
legend.text = element_text(size = 6))
if (savegraph) {
ggsave(filename = str_c(file_name_prefix, "_MDSbp",gfileext),
width = 7, height = 5, dpi = graphresolution)
}
#########1#########2#########3#########4#########5#########6#########7#########8
# Heatmaps ----------------------------------------------------------------
#########1#########2#########3#########4#########5#########6#########7#########8
# create a palette of 100 colours for OTU abundance
colscale <- colorRampPalette(c("lightyellow", "orange", "brown"),
space = "rgb")(100)
# remove the columns with the pooled OTUs (if any) from the filtered matrix
# of relative frequencies
OTUmatrixf_relab_hm <- OTUmatrixf_relab[, topntaxa]
if (any(colnames(OTUmatrixf_relab_hm) == "Other")){
OTUmatrixf_relab_hm <- OTUmatrixf_relab_hm[, -which(colnames(OTUmatrixf_relab_hm) == "Other")]
}
# Re-create palette for OTUs. See above for instructions to override defaults
OTU_palette_data <- make_OTU_palettes(OTUmatrixf_relab_hm)
OTUcolors <- OTU_palette_data$OTU_colors_df
myOTUcolors <- OTU_palette_data$colors
catOTUs <- OTU_palette_data$cat_OTU_flag
myOTUtext <- OTU_palette_data$text
myOTUpalette <- OTU_palette_data$palette
cat("The aggregation level for OTUs is ", input_data$tax_agg, "\n",
"There are ", ncol(OTUmatrixf_relab_hm), " OTU in this set.", "\n",
"The category used for OTU palettes is ", catOTUs, ".\n",
sep="")
# Show the palette and plot a legend in a horizontal format. It might be a good
# idea to save the palette
par(opar)
plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n", ann = FALSE)
legend("center", pch=15, legend = myOTUtext, y.intersp = 0.8,
col = myOTUcolors, bty = "n", title = catOTUs,
ncol = 1)
if (savegraph) {
if (graphtype == "pdf") {
pdf(file = paste(file_name, "hmOTUpal.pdf", sep = ""), width = graphsizein,
height = graphsizein, paper = "default")
} else {
tiff(filename = paste(file_name, "hmOTUpal.tif", sep = ""),
width = graphsizein, height = graphsizein, units = "in",
res = graphresolution, compression = "none")
}
plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n", ann = FALSE)
legend("center", pch=15, legend = myOTUtext, y.intersp = 0.8,
col = myOTUcolors, bty = "n", title = catOTUs,
ncol = if(length(myOTUtext>10)) 2 else 1)
dev.off()
}
# calculate distance matrices and dendrograms
sample.dist <- vegdist(OTUmatrixf_relab_hm, method = "bray")
# may generate warnings
sample.clus <- hclust(sample.dist, "aver")
OTU.dist <- vegdist(t(OTUmatrixf_relab_hm), method = "bray")
OTU.clus <- hclust(OTU.dist, "aver")
# may generate warnings: it is probably related to the order packages are loaded
hm <- OTUmatrixf_relab_hm
keyscale <- "rel. abundance"
if (loghmscale) {
# lbound gives a default to 0 abundances using max number of seqs per sample
lbound <- 10^(-ceiling(log10(max(rowSums(tOTUm)))))
hm <- aaply(hm, c(1,2),
function(x,minprop) log10(x+minprop),minprop=lbound)
keyscale <- "log10(rel.abundance)"
}
# heat map with legend for abundance levels, margins, lhei, lwid, srtCol,
# offsetRow and offsetCol may require tinkering.
# Size temporarily reset for saving
heatmap.2(hm,
Rowv = as.dendrogram(sample.clus),
Colv = as.dendrogram(OTU.clus),
srtCol = 30,
col = colscale,
breaks = 101,
symbreaks = F,
ColSideColors = myOTUpalette,
RowSideColors = myspalette,
margins = c(7,11),
trace = "both", density.info = "none",
tracecol = "orange1",
lhei = c(2.5,7.5),
lwid = c(2.5,7.5),
offsetRow = 0.5,
offsetCol = 0.5,
key.title = "NA",
key.xlab = keyscale,
keysize = 0.1,
symkey = F
)
if (savegraph) {
if (graphtype == "pdf") {
pdf(file = paste(file_name, "hm2.pdf", sep = ""), width = 10,
height = 7, paper = "a4r")
} else {
tiff(filename = paste(file_name, "hm2.tif", sep = ""),
width = 10, height = 7, units = "in",
res = graphresolution, compression = "none")
}
heatmap.2(hm,
Rowv = as.dendrogram(sample.clus),
Colv = as.dendrogram(OTU.clus),
srtCol = 30,
col = colscale,
breaks = 101,
symbreaks = F,
ColSideColors = myOTUpalette,
RowSideColors = myspalette,
margins = c(7,11),
trace = "both", density.info = "none",
tracecol = "orange1",
lhei = c(2.5,7.5),
lwid = c(2.5,7.5),
offsetRow = 0.5,
offsetCol = 0.5,
key.title = "NA",
key.xlab = keyscale,
keysize = 0.1,
symkey = F
)
dev.off()
}
par(opar)
#########1#########2#########3#########4#########5#########6#########7#########8
# Network analysis with the bipartite package -----------------------------
# This will be performed only if sample aggregation is Llabel. #
# Rarefaction and filtering are performed by default. #
#########1#########2#########3#########4#########5#########6#########7#########8
# Calculate and plot degree distributions, separately for OTU and samples
# using the unfiltered matrix (Other and chloroplast are removed)
# exponential, power law and truncated power law are given in black, dark grey
# and light grey, respectively. Results are saved in objects for further use.
# The defaults for this section are slightly different and filtering is
# performed from scratch
if(input_data$sample_agg == "exp. code"){
OTUmatrixbip <- tOTUm[complete.cases(tOTUm),]
# filter chloroplast, mitochondria etc. if applicable
OTUmatrixbip <- filter_taxa_1(OTUmatrix = OTUmatrixbip)
OTUdd <- as.data.frame(degreedistr(OTUmatrixbip, level = "higher",
plot.it = FALSE))
degreedistr(OTUmatrixbip, level = "higher")
Sampledd <- as.data.frame(degreedistr(OTUmatrixbip, level = "lower",
plot.it = FALSE))
degreedistr(OTUmatrixbip, level = "lower")
# Filtering and rarefaction performed
# remove samples is the sample filter was used
if (filtersamples_flag){
OTUmatrixbip <- OTUmatrixbip[samples_to_keep,]
}
OTUmatrixbip2 <- OTUmatrixbip/rowSums(OTUmatrixbip)
maxrelabbip <- apply(OTUmatrixbip2, 2, max)
OTUtokeepbip <- names(which(maxrelabbip >= abtreshold))
OTUmatrixbip <- OTUmatrixbip[, which(colnames(OTUmatrixbip2) %in% OTUtokeepbip)]
totseqsbip <- rowSums(OTUmatrixbip)
rarennbip <- ifelse(raremin, 100*floor(min(totseqsbip)/100), raren)
OTUmatrixbip <- vegan::rrarefy(OTUmatrixbip, rarennbip)
rarefy_flag <- T
OTUmatrixbip <- OTUmatrixbip[,colSums(OTUmatrixbip)>0]
OTUtokeepbip <- colnames(OTUmatrixbip)
filtOTUbip <- ncol(tOTUm)-ncol(OTUmatrixbip)
# prints a summary
cat("The original OTU/taxa abundance table has", ncol(tOTUm), "OTUs/taxa and ",
nrow(tOTUm), "samples/sample groups.", "\n", "\n", sep = " ")
cat("After filtering there are", "\n", n_distinct(sample_metadata$llabel),
"llabel groups", "\n", sep = " ")
cat(n_distinct(sample_metadata$L1), "food groups", "\n", sep = " ")
cat(n_distinct(sample_metadata$L4),
"food subgroups", "\n", sep = " ")
cat("After filtering/rarefaction you have removed", filtOTU, "OTU/TAXA out of",
ncol(tOTUm), "\n", "\n", sep = " ")
#########1#########2#########3#########4#########5#########6#########7#########8
# Rebuild palette for OTUs: palettes are made for family and class. Family is #
# is used if number of families <=15, otherwise class is used. To override #
# this set overrideOTU <- T (may result in problems in legends) #
#########1#########2#########3#########4#########5#########6#########7#########8
OTU_palette_data <- make_OTU_palettes(OTUmatrixbip)
OTUcolors <- OTU_palette_data$OTU_colors_df
myOTUcolors <- OTU_palette_data$colors
catOTUs <- OTU_palette_data$cat_OTU_flag
myOTUtext <- OTU_palette_data$text
myOTUpalette <- OTU_palette_data$palette
cat("The aggregation level for OTUs is ", input_data$tax_agg, "\n",
"There are ", ncol(OTUmatrixbip), " OTU in this set.", "\n",
"The category used for OTU palettes is ", catOTUs, ".\n",
sep="")
# Show the palette and plot a legend in a horizontal format. It might be a good
# idea to save the palette graph
plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n", ann = FALSE)
legend("center", pch=15, legend = myOTUtext, y.intersp = 0.8,
col = myOTUcolors, bty = "n",
ncol = if(length(myOTUtext)>10) 2 else 1,
title = catOTUs)
if (savegraph) {
if (graphtype == "pdf") {
pdf(file = paste(file_name, "bipOTUpal.pdf", sep = ""), width = graphsizein,
height = graphsizein, paper = "default")
} else {
tiff(filename = paste(file_name, "bipOTUpal.tif", sep = ""),
width = graphsizein, height = graphsizein, units = "in",
res = graphresolution, compression = "none")
}
plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n", ann = FALSE)
legend("center", pch=15, legend = myOTUtext, y.intersp = 0.8,
col = myOTUcolors, bty = "n",
ncol = if(length(myOTUtext)>10) 2 else 1,
title = catOTUs)
dev.off()
}
#########1#########2#########3#########4#########5#########6#########7#########8
# Plot the filtered web. adjust y.lim and text.width to fit labels if needed.
# The legend may need some tinkering. Adjust y.lim and twidths to fit labels
# and legend if needed. The section has been tested with par(pin=c(13.5,6)).
# In Rstudio you need to adjust the size of the plot pane.
#########1#########2#########3#########4#########5#########6#########7#########8
ncol_top <- case_when(
length(myOTUtext) <=5 ~ 1,
length(myOTUtext) >5 && length(myOTUtext) <=10 ~ 2,
length(myOTUtext) >10 && length(myOTUtext) <=15 ~ 3,
length(myOTUtext) >15 ~4
)
ncol_bottom = case_when(
length(mystext) <=5 ~ 1,
length(mystext) >5 && length(mystext) <=10 ~ 2,
length(mystext) >10 && length(mystext) <=15 ~ 3,
length(mystext) >15 ~4
)
plotweb(OTUmatrixbip, text.rot = 90, y.lim=c(0,2.5), col.high = myOTUpalette,
col.low = myspalette, low.y = 0.8)
twidthOTU <- max(strwidth(myOTUtext))*0.8
sltitle <- ifelse(catsamples == "L1", "FoodEx2 class L1", "FoodEx2 class L4")
legend("top", xjust = 0.5, yjust = 0, legend = myOTUtext,
col = myOTUcolors, bty = "n", title = catOTUs,
pch = 15, cex = 0.75, ncol = ncol_top, y.intersp = 0.6, text.width = twidthOTU)
twidths <- max(strwidth(mystext))*0.6
legend("bottom", xjust = 0.5, yjust = 1,
legend = mystext,
col = myscolors, bty = "n",
title = sltitle, y.intersp = 0.6,
pch = 15, cex = 0.75, ncol = ncol_bottom, text.width = twidths)
if (savegraph) {
if (graphtype == "pdf") {
pdf(file = paste(file_name, "bipnet.pdf", sep = ""), width = graphsizein*1.2,
height = graphsizein, paper = "default")
} else {
tiff(filename = paste(file_name, "bipnet.tif", sep = ""),
width = graphsizein*1.2, height = graphsizein, units = "in",
res = graphresolution, compression = "none")
}
plotweb(OTUmatrixbip, text.rot = 90, y.lim=c(0,2.5), col.high = myOTUpalette,
col.low = myspalette, low.y = 0.8)
twidthOTU <- max(strwidth(myOTUtext))*0.8
sltitle <- ifelse(catsamples == "L1", "FoodEx2 class L1", "FoodEx2 class L4")
legend("top", xjust = 0.5, yjust = 0, legend = myOTUtext,
col = myOTUcolors, bty = "n", title = catOTUs,
pch = 15, cex = 0.75, ncol = ncol_top, y.intersp = 0.6, text.width = twidthOTU)
twidths <- max(strwidth(mystext))*0.6
legend("bottom", xjust = 0.5, yjust = 1,
legend = mystext,
col = myscolors, bty = "n",
title = sltitle, y.intersp = 0.6,
pch = 15, cex = 0.75, ncol = ncol_bottom, text.width = twidths)
dev.off()
}
# restoring graphical parameters
par(opar)
#########1#########2#########3#########4#########5#########6#########7#########8
# Visualize the web in matrix style format
visweb(OTUmatrixbip, type = "diagonal")
#########1#########2#########3#########4#########5#########6#########7#########8
# Calculate H2' for the network (the higher the value the more selective
# the OTUs) and d' (same for samples, ranges from 0, no specialisation, to 1,
# perfect specialist). d' for OTUs is calculated later.
Htwo <- H2fun(OTUmatrixbip)
dprimefood <- dfun(OTUmatrixbip)
Htwo
dprimefood
#########1#########2#########3#########4#########5#########6#########7#########8
# Calculate statistics for OTU nodes and sample nodes (filtered network) and
# save them in data frames
slevelstats <- specieslevel(OTUmatrixbip)
OTUstats <- slevelstats$`higher level`
# join with relative abundance
OTUfreqs <- colSums(OTUmatrixbip)/sum(OTUmatrixbip)
OTUstats <- cbind(OTUstats,OTUfreqs)
samplestats <- slevelstats$`lower level`
# join the Class
OTUstats$label <- row.names(OTUstats)
OTUstats <- left_join(OTUstats, select(input_data$taxa_metadata, label, class))
OTUstats$Class <- as.factor(OTUstats$class)
# plot d values
OTUdab <- OTUstats %>% dplyr::select(label, d, OTUfreqs, class) %>%
dplyr::arrange(desc(d))
# if the number of taxa is > 50 only the first 50 (most specialist) will be used
OTUdab_toplot <- if(nrow(OTUdab)>50) slice(OTUdab, 1:50) else OTUdab
dabplotwarning <- ifelse(nrow(OTUdab)>50,
str_wrap(
str_c("Warning: only the 50 taxa with highest values of specialization, out of ",
nrow(OTUdab), " taxa in the dataset are being plotted"), 50
),
"")
dabplot <- ggplot(data = OTUdab_toplot, aes(x=d,
y = reorder(factor(label), d),
size = OTUfreqs,
colour = class))
# use alternative palettes and styles depending on the number of categories
dabtitle <- str_c("Specialization (d) and abundance plot")
dabsubtitle <- ifelse(dabplotwarning !="", dabplotwarning, waiver())
if(n_distinct(OTUdab_toplot$class)<=12){
dabplot + geom_point() +
scale_color_brewer(type = "qual", palette = "Paired") +
labs(y = "taxa", size = "rel. ab.", title = dabtitle, subtitle = dabsubtitle) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust=0.5))
} else {
dabplot_palette <- myOTUcolorsbip
names(dabplot_palette) <- myOTUtext
dabplot + geom_point() +
scale_color_manual(values = dabplot_palette) +
labs(y = "taxa", size = "rel. ab.", title = dabtitle, subtitle = dabsubtitle) +
theme_bw() +
theme(axis.text.y = element_text(size = 16/log2(n_distinct(OTUdab_toplot$class))),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust=0.5))
}
if (savegraph) {
ggsave(filename = str_c(file_name_prefix, "_dabplot",gfileext),
width = 7, height = 5, dpi = graphresolution)
}
# plot degree vs betweeness, only the OTUs with the top 20% values are labeled
OTUstats$OTUlabel <- as.character(rep("",nrow(OTUstats)))
lbl <- which(OTUstats$normalised.degree >= quantile(OTUstats$normalised.degree, 0.80))
OTUstats$OTUlabel[lbl] <- OTUstats$label[lbl]
lbl <- which(OTUstats$weighted.betweenness != 0)
OTUstats$OTUlabel[lbl] <- OTUstats$label[lbl]
degbplot <- ggplot(data = OTUstats, aes(x = normalised.degree,
y = weighted.betweenness,
colour = Class,
size = OTUfreqs))
if(n_distinct(OTUstats$class)<=12){
degbplot + geom_point() +
geom_text_repel(aes(label = OTUlabel), colour = "black",
alpha = 0.6, size = 3) +
labs(x = "normalised degree", y = "weighted betweenness") +
scale_color_brewer(type = "qual", palette = "Paired") +
theme_bw()
} else {
degplot_palette <- myOTUcolors
names(degplot_palette) <- myOTUtext
degbplot + geom_point() +
geom_text_repel(aes(label = OTUlabel), colour = "black",
alpha = 0.6, size = 3) +
labs(x = "normalised degree", y = "weighted betweenness") +
scale_color_manual(values = degplot_palette) +
theme_bw()
}
if (savegraph) {
ggsave(filename = str_c(file_name_prefix, "_degbplot",gfileext),
width = 7, height = 5, dpi = graphresolution)
}
#########1#########2#########3#########4#########5#########6#########7#########8
# Recalculate degree distributions, separately for OTU and samples
# using the filtered matrix
OTUddf <- as.data.frame(degreedistr(OTUmatrixbip, level = "higher",
plot.it = FALSE))
degreedistr(OTUmatrixf, level = "higher")
Sampleddf <- as.data.frame(degreedistr(OTUmatrixbip, level = "lower",
plot.it = FALSE))
degreedistr(OTUmatrixbip, level = "lower")
#########1#########2#########3#########4#########5#########6#########7#########8
# Compute and plot modules, may need to tamper with labsize
mylabsize <- 0.6
moduleWebObject <- computeModules(OTUmatrixbip);
plotModuleWeb(moduleWebObject, labsize = mylabsize)
if (savegraph) {
if (graphtype == "pdf") {
pdf(file = paste(file_name, "modw.pdf", sep = ""), width = graphsizein,
height = graphsizein, paper = "default")
} else {
tiff(filename = paste(file_name, "modw.tif", sep = ""),
width = graphsizein, height = graphsizein, units = "in",
res = graphresolution, compression = "none")
}
moduleWebObject <- computeModules(OTUmatrixbip);
plotModuleWeb(moduleWebObject, labsize = mylabsize)
dev.off()
}
# restoring graphical parameters
par(opar)
#########1#########2#########3#########4#########5#########6#########7#########8
# Calculate network level stats. Only some options are included. #
# For small networks you may run #
# netstats <- as.data.frame(networklevel(OTUmatrixf, index = "ALLBUTDD" #
# Run this only at the end, may cause crashes for large matrices. #
# Make sure you save files and export the plots before you run it #
#########1#########2#########3#########4#########5#########6#########7#########8
netstats <- as.data.frame(networklevel(OTUmatrixbip,
index = c("connectance",
"weighted connectance",
"nestedness",
"weighted nestedness",
"cluster coefficient")))
colnames(netstats)<-"index"
#########1#########2#########3#########4#########5#########6#########7#########8
# Save individual dataframes (as .txt files) for future use #
#########1#########2#########3#########4#########5#########6#########7#########8
write.table(OTUstats, file = str_c(file_name_prefix, "_OTUstats.txt"),
append = FALSE, sep = "\t", col.names = TRUE, row.names = TRUE)
write.table(samplestats, file = str_c(file_name_prefix, "_samplestats.txt"),
append = FALSE, sep = "\t", col.names = TRUE, row.names = TRUE)
write.table(netstats, file = str_c(file_name_prefix, "_netstats.txt", sep = ""),
append = FALSE, sep = "\t", col.names = TRUE, row.names = TRUE)
}
#########1#########2#########3#########4#########5#########6#########7#########8
# Bonus plot --------------------------------------------------------------
# bar plot with the x taxa with the highest absolute abundance. #
# skipped if aggregation level is other than genus or sample aggregation is #
# other than exp.code.
# The abundances of the remaining OTUs are optionally pooled. # #
#########1#########2#########3#########4#########5#########6#########7#########8
if(input_data$sample_agg == "exp. code" & input_data$tax_agg == "genus"){
OTUsums <- colSums(OTUmatrixbip)
# topx should be <20 and definitely <25
topxOTUnodesab <- names(OTUsums[order(OTUsums, decreasing = TRUE)])[1:topx]
OTUmatrixf3 <- OTUmatrixbip[,which(colnames(OTUmatrixbip) %in% topxOTUnodesab)]
if (pool) {
if (rarefy_flag){
sampleSums <- round(rarennbip*(totseqs-totseqsbip)/totseqs,0)+rarennbip
} else {sampleSums <- totseqs}
} else {
sampleSums <- rowSums(OTUmatrixbip)
}
OTUmatrixf3 <- OTUmatrixf3/sampleSums
Other <- 1-rowSums(OTUmatrixf3)
OTUmatrixf3 <- cbind(OTUmatrixf3, Other)
ET5 <- melt(OTUmatrixf3, value.name = "Weight")
colnames(ET5)[1:2] <- c("Sample", "Taxa")
taxa_levels <- unique(ET5$Taxa)
if ("Other" %in% taxa_levels) {
taxa_levels <- c("Other",setdiff(taxa_levels,"Other"))
}
ET5$Taxa <- factor(ET5$Taxa, levels = taxa_levels)
# make a palette
ncols <- nlevels(ET5$Taxa)
# make a qualitative color palette
bp_palette <- distinctColorPalette(ncols)
names(bp_palette) <- levels(ET5$OTU)
mygtitle <- paste("Relative abundance, top", ncols, "taxa (based on tot seqs)")
sbplot <- ggplot(data = ET5, aes(x = Sample, y= Weight, fill = Taxa))
if (topx<=12) {
sbplot <- sbplot + geom_col(position = "fill") +
labs(title = str_wrap(mygtitle,40),
x= "Samples/Sample groups",
y= "Relative abundance",
fill = "taxa") +
scale_fill_brewer(type = "qual", palette = "Paired") +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
legend.key.size = unit(0.2, "in"),
plot.title = element_text(hjust = 0.5))
print(sbplot)
} else {
sbplot <- sbplot + geom_col(position = "fill") +
labs(title = str_wrap(mygtitle,40),
x= "Samples/Sample groups",
y= "Relative abundance",
fill = "taxa") +
scale_fill_manual(values = bp_palette) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
legend.key.size = unit(0.2, "in"),
plot.title = element_text(hjust = 0.5))
print(sbplot)
}
if (savegraph) {
ggsave(str_c(file_name_prefix, "_topxab", gfileext),
width = 7, height = 5, dpi = graphresolution)
}
}
#########1#########2#########3#########4#########5#########6#########7#########8
# Save an image of the workspace: please realize that some objects which are
# recycled in the script must be created afresh (i.e. ET3, ET4, OTUmatrixf3)
save.image(file = paste(file_name_prefix, "image.Rdata", sep = ""))
# if you want to do some clean up use rm(list = ls()) before running
# the script again
# rm(list = ls())
#########1#########2#########3#########4#########5#########6#########7#########8
# Credits and citation ----------------------------------------------------
#########1#########2#########3#########4#########5#########6#########7#########8
# Script created by Eugenio Parente, 2018, modified in 2022. #
# The checkpackages function is derived from http://tinyurl.com/jjwyzph #
# The code for making qualitative palette is derived from: #
# http://tinyurl.com/hawqufy #
# The code for heat maps is modified from: http://tinyurl.com/hxxbmvz #
# assume this is under GNU general public licence #
# http://www.gnu.org/licenses/gpl-3.0.en.html #
# if you decide to use this script in your work, please mention #
# the FoodMicrobionet web site #
# http://www.foodmicrobionet.org #
# and cite relevant publications therein as appropriate #
# References for packages used in this script: #
all_packages <- c("base", .cran_packages)
map(all_packages, citation)
#########1#########2#########3#########4#########5#########6#########7#########8
# this script was successfully tested on the following systems:
# iMac 21.5 inches late 2013, MacOS 10.15.7, R 4.1.2, RStudio 2021.09.0+351
# MacBook Pro Retina 13" Early 2015, 2.7 GHz Intel Core i5, MacOS 10.15.7, R 4.1.2, RStudio 2021.09.0+351
#########1#########2#########3#########4#########5#########6#########7#########8
# Future plans (in case I have time, which is unlikely):
# transform the script in an interactive Rmarkdown document (almost done)
# improve filtering
# use function to automate/improve some of the actions i.e.
# creation of palettes for samples
# use functions from phyloseq and SpiecEasi to estimate networks
# improve the way the format of the input data is checked
# offer different abundance/prevalence filters
# add options for aggregating taxa (if too many)
# add an option for using absolute or relative abundances in vegan::metaMDS
# maybe make alpha a function of the number of points in the MDS plots
# automatically skip bipartite analysis if sample agg is not llabel
# improve the control of options in plotweb
|
1463fe7d1f6c4c96524386eb03a50658ab326e13
|
8db74a17ef5a9218a0abd1dda219ed14ab1810ac
|
/plot3.R
|
35266b9c4c70e5400b581e452024a268adc8c015
|
[] |
no_license
|
robertjacobson/ExData_Plotting1
|
915bf8165b8340a98ed650ff237258c5bb3c073c
|
a7fc240137dd1f258155345f35fe5751e115922d
|
refs/heads/master
| 2021-08-19T17:31:34.199499
| 2017-11-27T03:13:34
| 2017-11-27T03:13:34
| 112,108,990
| 0
| 0
| null | 2017-11-26T19:08:21
| 2017-11-26T19:08:21
| null |
UTF-8
|
R
| false
| false
| 948
|
r
|
plot3.R
|
# If datas doesn't exist, load it into the environment.
if(length(datas) == 0){
source('downloadDataset.R')
}
friIndex <- match(as.Date('2007-02-02'), datas$Date)
count <- nrow(datas)
plot_colors <- c('grey','red','blue')
max_y <- max(datas$Sub_metering_1, datas$Sub_metering_2, datas$Sub_metering_3)
png(
file='plot3.png',
width=480,
height=480
)
# Lines
plot(
datas$Sub_metering_1,
type='l',
ann=FALSE,
axes=FALSE,
col=plot_colors[1]
)
lines(
datas$Sub_metering_2,
type='l',
ann=FALSE,
col=plot_colors[2]
)
lines(
datas$Sub_metering_3,
type='l',
ann=FALSE,
col=plot_colors[3]
)
# X Labels
axis(1, at=0, lab=c('Thu'))
axis(1, at=friIndex, lab=c('Fri'))
axis(1, at=count, lab=c('Sat'))
# Y Labels
title(ylab='Energy sub metering')
axis(2, las=1, at=10*0:max_y)
# Legend
legend("topright", max_y, c('Sub_meterng_1', 'Sub_metering_2', 'Sub_metering_3'), cex=0.8, col=plot_colors, lwd=2)
# Box
box()
dev.off()
|
be4a421eb187942de64e22960daeecc6842e6dec
|
217c2788d589bc0dff3993bd4a02c04b26296fb2
|
/dailyCovidRestimate.R
|
ed6db2094a18ecf508e11b95b2bccb26a08a7e8f
|
[] |
no_license
|
tjibbed/UKFprojection
|
d3fdecf1a5deace4f4956a0c5bb924a62a4a1f99
|
37b300cb2052eec6ab58595a82350c9df65ec7c5
|
refs/heads/master
| 2023-02-13T09:46:00.873526
| 2021-01-14T10:20:34
| 2021-01-14T10:20:34
| 256,415,670
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,296
|
r
|
dailyCovidRestimate.R
|
library(ggplot2)
library(data.table)
library(stringr)
library(dplyr)
library(psych)
library(ggpubr)
library(animation)
library(tidyverse)
library(survival)
library(flexsurv)
library(lubridate)
library(survminer)
library(RCurl)
library(httr)
library(jsonlite)
source("EarlyIncidencePrediction.R")
source("WithinHospModel.R")
source("FigurePlotting.R")
source("IncidenceModel.R")
source("Survival.R")
source("ExpertParameters.R")
readLandKreisNames()
RKIinciList<-getInciForKreiseRKI(c("Emmendi","Freiburg","Hochschwarzwald"),loadData = T)
sum(RKIinciList$all)
singleRtEsti(RKIinciList$all,
min(RKIinciList$Date),
as.Date("2020-09-28"),
pexp(1:100,1/5)-pexp(0:99,1/5),
pgamma(1:100,1.87,0.28)-pgamma(0:99,1.87,0.28),
runLength=150,
underreporting=1)
myEst<-multiRtEsti(RKIinciList$all,
min(RKIinciList$Date),
as.Date("2020-09-28"),
c(1,0),
# pexp(1:100,1/3)-pexp(0:99,1/3),
pgamma(1:100,1.87,0.28)-pgamma(0:99,1.87,0.28),
runLength=150,
underreporting=1,
numRuns=1000)
warnings()
getReShadedPlot(myEst,ylimits = c(0,6))
|
b1fb50fb738343851a28b4363c79ee3ef9bb0d7b
|
ae6580683da835a034d2e43a2b8023c43071bd05
|
/my_ann_orignal.R
|
c3be7fc86ffca3c9063d753ed7cf693783ec6063
|
[
"MIT"
] |
permissive
|
agrutkarsh/Driving-Stereotype-Identification-of-Heavy-Goods-Vehicle-code-only-i.e.-without-dataset-
|
f25c3af2b71dd3c1655b6c4d3325a0411fc8ab6d
|
00035f560d42150bddf031e3eea31fd99560cc19
|
refs/heads/master
| 2021-04-29T14:29:07.706540
| 2018-02-16T16:31:41
| 2018-02-16T16:31:41
| 121,773,042
| 0
| 0
| null | 2018-02-16T16:30:31
| 2018-02-16T16:27:37
| null |
UTF-8
|
R
| false
| false
| 2,807
|
r
|
my_ann_orignal.R
|
setwd('C:/Users/psxua/Dropbox/projects/phd/codes/r/edinburgh series')
library("pracma", lib.loc="~/R/R-3.2.2/library")
library("cclust", lib.loc="~/R/R-3.2.2/library")
library("cluster", lib.loc="~/R/R-3.2.2/library")
library("clValid", lib.loc="~/R/R-3.2.2/library")
data<-read.csv("output_4cls_unnormalised_data.csv", header = FALSE)
data2<-data[,1:10]
data2<-data2/300
no_of_row<-nrow(data)
no_of_col<-ncol(data)
max_cls<-4
pos<-cbind()
nc<-rbind()
train<-rbind()
out<-rbind()
for (i in 1:no_of_row){
if (data[i,11]=='NaN'){
pos<-cbind(pos,i)
n<-data2[i,1:10]
nc<-rbind(nc,n)
}
else{
n<-data2[i,1:10]
train<-rbind(train,n)
n<-data[i,11]
out<-rbind(out,n)
}
}
out1 <- array(0,dim=c(nrow(train),max_cls))
for (i in 1:nrow(train)){
for (j in 1:max_cls){
if (out[i]==j)
out1[i,j]=1
}
}
trd<-t(train)
nhl<-20
nol<-4
nil<-nrow(trd)
ns<-ncol(trd)
wih<-rbind()
for (i in 1:nil){
temp <- -1+2*(runif(nhl))
wih <- rbind(wih,temp)
}
who<-rbind()
for (i in 1:nhl){
temp <- -1+2*(runif(nol))
who <- rbind(who,temp)
}
y<-out1
alpha <- 0.2
se <- 1
epoc <- 0
yhl<-matrix(0,nhl,1)
yp<-matrix(0,ns,nol)
error<-matrix(0,ns,nol)
delo<-matrix(0,nol,1)
delh<-matrix(0,nhl,1)
while (epoc<2000){
epoc<-epoc+1
for (i in 1:ns){
for (j in 1:nhl){
temp1<-t(trd[,i])
temp2<-temp1*wih[,j]
temp<-sum(temp2)
yhl[j,1]=sigmoid(temp,a=1,b=0)
}
for (j in 1:nol){
temp1<-t(yhl[,1])
temp2<-temp1*who[,j]
temp<-sum(temp2)
yp[i,j]<-sigmoid(temp,a=1,b=0)
error[i,j]<-y[i,j]-yp[i,j]
delo[j,1]<-yp[i,j]*(1-yp[i,j])*error[i,j]
}
for (j in 1:nhl){
for (k in 1:nol){
who[j,k]<-who[j,k]+alpha*yhl[j,1]*delo[k,1]
}
}
for (j in 1:nhl){
temp=0
for (k in 1:nol){
temp<-temp+delo[k,1]*who[j,k]
}
delh[j,1]<-yhl[j,1]*(1-yhl[j,1])*temp
}
for (j in 1:nil){
for (k in nhl){
wih[j,k]<-wih[j,k]+alpha*trd[j,i]*delh[k,1]
}
}
se<-sum((error^2))
}
print(se)
print(epoc)
}
output<-rbind()
ynew<-matrix(0,nol,1)
for (loop in 1:nrow(nc)){
x=nc[loop,]
for (j in 1:nhl){
temp1<-t(x)
temp2<-temp1*wih[,j]
temp<-sum(temp2)
yhl[j,1]<-sigmoid(temp,a=1,b=0)
}
for (j in 1:nol){
temp1<-t(yhl[,1])
temp2<-temp1*who[,j]
temp<-sum(temp2)
ynew[j,1]<-sigmoid(temp,a=1,b=0)
}
ypos=1
ymax=ynew[1,1]
for (j in 1:(nrow(ynew)-1)){
if (ymax<ynew[(j+1),1]){
ymax<-ynew[j+1]
ypos<-j+1
}
}
output<-rbind(output,ypos)
}
write.csv(output,file="ann_result_on_unnormalised_data2.csv")
|
536a7d1c9d134abb27ad708f50723dd99a936078
|
b5e7b204795818d53e69488b3883a746a11c11ff
|
/plot4.r
|
acacfcc633a6eb272490093a050c4986eef0751e
|
[] |
no_license
|
geoffness/ExData_Plotting1
|
b8a56eeafa6ae16b80b23a2b704b4e687fddceea
|
91ba9b445efbd7dd5114fa766937eb6b5bf3f16a
|
refs/heads/master
| 2021-01-18T07:37:36.626144
| 2015-09-13T11:22:58
| 2015-09-13T11:22:58
| 42,343,123
| 0
| 0
| null | 2015-09-12T03:27:20
| 2015-09-12T03:27:20
| null |
UTF-8
|
R
| false
| false
| 1,369
|
r
|
plot4.r
|
## Code to read household energy consumption data then plot a group of 4 plots
## covering global active power, voltage, energy sub metering and global reactive power
## Note: assumes presence of file "household_power_consumption.txt" in working directory
# Read and format
FebData <- read.table(pipe('grep "^[1-2]/2/2007" "household_power_consumption.txt"'),sep = ";")
header <- readLines("household_power_consumption.txt",n = 1)
names(FebData) <- strsplit(header,";")[[1]]
FebData$datetime <- strptime(paste(FebData$Date,FebData$Time),"%d/%m/%Y %H:%M:%S")
FebData$weekday <- format(FebData$datetime,"%a")
# Output plots to png file dest
png(filename = "plot4.png")
par(mfrow=c(2,2))
plot(FebData$datetime,FebData$Global_active_power,type = "l",
xlab = "",ylab = "Global Active Power")
plot(FebData$datetime,FebData$Voltage,type = "l",
xlab = "datetime",ylab = "Voltage")
plot(FebData$datetime,FebData$Sub_metering_1,type = "l",ylab = "Energy sub metering",xlab = "")
lines(FebData$datetime,FebData$Sub_metering_2,col="red")
lines(FebData$datetime,FebData$Sub_metering_3,col="blue")
legend("topright",legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),
col = c("black","red","blue"),lty = c(1,1,1),bty = "n")
plot(FebData$datetime,FebData$Global_reactive_power,type = "l",
xlab = "datetime",ylab = "Global_reactive_power")
dev.off()
|
e0beb5d01431c0eaa05a4873a0f816b2ff56a078
|
153415943e27c98bf6bd27366ce49835050f0899
|
/games.R
|
b47b787acadf6cdce94d0a69eaeb67f186a50b96
|
[] |
no_license
|
ptoulis/long-term-causal-effects
|
a8f7746618df0188e58728e6035e9538fb8aebcb
|
7c955450978ca81e4cbe7e6d725d37cefb748ce0
|
refs/heads/master
| 2021-01-22T09:43:08.141192
| 2016-09-22T22:20:07
| 2016-09-22T22:20:07
| 68,416,870
| 5
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,156
|
r
|
games.R
|
# GAME = list(payoff matrix for agent A, payoff for agent B, ...)
# mixedStrategy = (piA, piB, ...) where piX = action distribution for agent X.
#
game.mckelvey <- function(A=5, B=5) {
Payoff.A <- matrix(c(1, 0, 1,
0, 0, 0,
1, A, 1), nrow=3, byrow=T)
Payoff.B <- matrix(c(1, 0, 1,
0, 0, 0,
1, B, 1), nrow=3, byrow=T)
return(list(Payoff.A, Payoff.B))
}
game.lieberman <- function() {
Payoff.A <- 3.373 * matrix(c(15, 0, -2,
0, -15, -1,
1, 2, 0), nrow=3, byrow=T)
Payoff.B <- 3.373 * matrix(c(-15, 0, -1,
0, 15, -2,
2, 1, 0), nrow=3, byrow=T)
return(list(A=Payoff.A, B=Payoff.B))
}
game.rapoport <- function(W, L) {
Payoff.A <- 1 * matrix(c(W, L, L, L, L,
L, L, W, W, W,
L, W, L, L, W,
L, W, L, W, L,
L, W, W, L, L), nrow=5, byrow=T)
Payoff.B <- -t(Payoff.A)
return(list(A=Payoff.A, B=Payoff.B))
}
get.player.index <- function(mixed.strategy, game, agentId) {
num.actions = nrow(game[[1]])
start = 1 + (agentId-1) * num.actions
end = start + num.actions - 1
stopifnot(end <= length(mixed.strategy), start > 0)
return(list(start=start, end=end))
}
player.mixedStrategy <- function(mixed.strategy, game, agentId) {
# Gets the player strategy from the entire vector of mixed strategy profile
#
# Returns:
# distribution over actions of player agentId
ind = get.player.index(mixed.strategy, game, agentId)
st = matrix(mixed.strategy[ind$start:ind$end], ncol=1)
stopifnot(abs(1-sum(st)) < 1e-3)
return(st)
}
other.mixedStrategy <- function(mixed.strategy, game, agentId) {
#
# Returns the mixed strategies of all other agents flattened out
# in a column vector.
ind = get.player.index(mixed.strategy, game, agentId)
return(matrix(mixed.strategy[-seq(ind$start, ind$end)], ncol=1))
}
|
858ae6fdda6d94c51b690be5a7bffbff6b3f4cd2
|
721d96cf611a8ee4ee224e6dae7c92a9faf180c0
|
/R/FUNCTION-trim_v5.R
|
99227877a3d002334a437f1c95cc07354e892e94
|
[] |
no_license
|
cran/astrochron
|
5b05df5f4f114cdf6df630cdfe0f649e5f45dbbf
|
c86d83297c0b75ce1ab1a026a25fe05e0e6cb3fe
|
refs/heads/master
| 2023-09-01T06:57:37.447424
| 2023-08-26T13:40:02
| 2023-08-26T14:30:40
| 21,361,937
| 5
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,333
|
r
|
FUNCTION-trim_v5.R
|
### This function is a component of astrochron: An R Package for Astrochronology
### Copyright (C) 2016 Stephen R. Meyers
###
###########################################################################
### trim: function to remove outliers - (SRM: March 9-10, 2012; June 30, 2012;
### May 20, 2013; August 6, 2015; July 22, 2016)
###
### automatically remove outliers from time series.
### outliers are determined using boxplot
###########################################################################
trim <- function (dat,c=1.5,genplot=T,verbose=T)
{
if(verbose) cat("\n----- REMOVING OUTLIERS FROM STRATIGRAPHIC SERIES -----\n")
ipts <- length(dat[,1])
if(verbose) cat(" * Number of original data points=", ipts,"\n")
eval <- !dat[,2] %in% boxplot.stats(dat[,2],coef=c)$out
out <- rep(NA,ipts*2)
dim(out) <- c(ipts,2)
trimmed <- rep(NA,ipts*2)
dim(trimmed) <- c(ipts,2)
trimpts=0
for (i in 1:ipts)
{
if (isTRUE(eval[i]))
{
out[i,1]= dat[i,1]
out[i,2]=dat[i,2]
trimpts = trimpts + 1
}
else
{
trimmed[i,1]= dat[i,1]
trimmed[i,2]= dat[i,2]
}
}
if(verbose) cat(" * Number of data points post-trimming=", trimpts,"\n")
### grab all data except 'NA' values, assign to dat2
dat2 <- data.frame(subset(out,!(out[,2] == 'NA')))
### grab all trimmed data except 'NA' values, assign to datrimmed
datrimmed <- data.frame(subset(trimmed,!(trimmed[,2] == 'NA')))
if(genplot)
{
### plot data series. Note, cex is the factor by which to increase or decrease default symbol size
par(mfrow=c(2,2))
xrange = range(c(dat[,1]))
yrange = range(c(dat[,2]))
plot(dat2, cex=.5,xlim=xrange,ylim=yrange,xlab="Location",ylab="Value",main="Data Series")
lines(dat2)
par(new=TRUE)
plot(datrimmed, cex=.5, xlim=xrange,ylim=yrange,col="red")
### plot the denisty and the histogram together
hist(dat2[,2],freq=F,xlab="Value",main="Distribution values"); lines(density(dat2[,2], bw="nrd0"),col="red"); grid()
### boxplot
boxplot(dat2[,2])
### Normal probabilty plot (Normal Q-Q Plot)
qqnorm(dat2[,2]); qqline(dat2[,2], col="red"); grid()
}
return(dat2)
### END function trim
}
|
c719af45b4d36200e0dc9b8b55721cf3f6e24e39
|
24bf55acfd9df65714d83975ae5e2e0988d2486e
|
/man/fbind.Rd
|
7237af1f6e578f1ecfdf66a52985e001e9bf43da
|
[] |
no_license
|
pdaniele/foofactors
|
7764d789e5fd33712d4c5977eb872a306863154a
|
7e19df7176ba16ad0a0438e4aebe4721861c0957
|
refs/heads/master
| 2020-06-13T16:12:42.697155
| 2016-12-08T19:07:09
| 2016-12-08T19:07:09
| 75,359,928
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 472
|
rd
|
fbind.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fbind.R
\name{fbind}
\alias{fbind}
\title{Bind two factors}
\usage{
fbind(a, b)
}
\arguments{
\item{a}{factor}
\item{b}{factor}
}
\value{
factor
}
\description{
Create a new factor from two existing factors, where the new factor's levels
are the union of the levels of the input factors
}
\examples{
fbind(iris$Species[c(1,51,101)], PlantGrowth$group[c(1,11,21)])
}
|
8f8907458e0386780c7d84a84a16c63173fd20ff
|
1e7f61b700940811ef7757d21b3690023ec9822a
|
/tests/dimchecks.R
|
d48367250063349653543b9bd09cc5adbe333aaa
|
[] |
no_license
|
jwjeong1117/pomp
|
b2417b9514c583fbe19c3280878fab6d60817c00
|
5ce3725cc381039e310f1999b8d37631ac359c14
|
refs/heads/master
| 2021-01-22T11:21:53.811139
| 2015-10-20T21:15:27
| 2015-10-20T21:15:27
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,652
|
r
|
dimchecks.R
|
library(pomp)
set.seed(1420306530L)
pompExample(ricker)
po <- ricker
pars <- coef(po)
xstart <- init.state(po,params=pars)
rprocess(po,xstart,times=0:5,params=pars)[,1,]
rprocess(po,xstart=parmat(xstart,5),times=0:5,params=pars)[,3,]
rprocess(po,xstart=xstart,times=0:5,params=parmat(pars,3))[,3,]
try(
rprocess(po,xstart=parmat(xstart,2),times=0:5,params=parmat(pars,3))[,,3]
)
rprocess(po,xstart=parmat(xstart,2),times=0:5,params=parmat(pars,6))[,,3]
x <- rprocess(po,xstart=parmat(xstart,2),times=0:5,params=parmat(pars,8))
rmeasure(po,x=x,params=pars,times=0:5)[,,3]
try(
rmeasure(po,x=x,params=parmat(pars,3),times=0:5)[,,3]
)
rmeasure(po,x=x,params=parmat(pars,4),times=0:5)
x <- rprocess(po,xstart=xstart,times=0:5,params=pars)
rmeasure(po,x=x,params=parmat(pars,2),times=0:5)
y <- rmeasure(po,x=x,params=parmat(pars,4),times=0:5)
dmeasure(po,x=x,y=y[,2,,drop=F],params=pars,times=0:5)
x <- rprocess(po,xstart=parmat(xstart,3),times=0:5,params=pars)
y <- rmeasure(po,x=x,params=pars,times=0:5)
try(dmeasure(po,x=x,y=y,params=parmat(pars,3),times=0:5))
f1 <- dmeasure(po,x=x,y=y[,1,,drop=F],params=parmat(pars,3),times=0:5)
f2 <- dmeasure(po,x=x,y=y[,1,,drop=F],params=pars,times=0:5)
stopifnot(identical(f1,f2))
g1 <- skeleton(po,x=x,t=0:5,params=pars)
g2 <- skeleton(po,x=x,t=0:5,params=parmat(pars,3))
stopifnot(identical(g1,g2))
g3 <- skeleton(po,x=x,t=0:5,params=parmat(pars,6))
stopifnot(identical(g1,g3[,1:3,]))
stopifnot(identical(g1,g3[,4:6,]))
pompExample(gompertz)
p <- parmat(coef(gompertz),5)
f1 <- partrans(gompertz,p,"inv")
f2 <- parmat(coef(gompertz,transform=TRUE),5)
stopifnot(identical(f1,f2))
|
07be9713b1c3a9bae2f3c0db18bd95cb630adab3
|
26f465c3aeab5838385e7008f8fc15f97deb74b9
|
/experiments/Experiment 3/algorithms/LOF.R
|
aaa805780d777f873e75924515bd58fd72e87e36
|
[] |
no_license
|
msandim/master-thesis
|
3776038565897ccd838f5f88a06499a90f2e0e86
|
db60b549446e953747764e40feccc7cf1fcbe2d9
|
refs/heads/master
| 2021-09-14T01:23:39.003398
| 2018-05-07T02:51:41
| 2018-05-07T02:51:41
| 84,819,866
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 867
|
r
|
LOF.R
|
library(dbscan)
library(caret)
lof_apply <- function(data)
{
data_X <- data %>% select(-outlier)
returnData <- data %>% select(outlier)
# Scale the data:
scaleObject <- preProcess(data_X, method = c("center", "scale"))
data_X <- predict(scaleObject, data_X) %>% as.matrix
returnData$lof_03 <- lof(data_X, k = 3)
returnData$lof_05 <- lof(data_X, k = 5)
returnData$lof_08 <- lof(data_X, k = 8)
returnData$lof_14 <- lof(data_X, k = 14)
returnData$lof_19 <- lof(data_X, k = 19)
returnData$lof_25 <- lof(data_X, k = 25)
returnData$lof_30 <- lof(data_X, k = 30)
return(returnData)
}
#lof_save <- function(data, algorithm, dataset, fold)
#{
# for(parameter in names(data))
# {
# writePredictions(data[[name]],
# paste0(algorithm,":",parameter),
# dataset,
# fold)
# }
#}
|
3d1419bd50b9b251dcb324310eacda3c69a6ad27
|
3f680c621d68cd817097e1a83915ceaead162e12
|
/man/getAllFunnelsIncAIC.Rd
|
80a9a8347fc7b2fb716123f3726f61634e676c8b
|
[] |
no_license
|
rohan-shah/mpMap2
|
46273875750e7a564a17156f34439a4d93260d6c
|
c43bb51b348bdf6937e1b11298b9cdfe7a85e001
|
refs/heads/master
| 2021-05-23T20:34:59.327670
| 2020-07-19T10:24:09
| 2020-07-19T10:24:09
| 32,772,885
| 10
| 6
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,779
|
rd
|
getAllFunnelsIncAIC.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getAllFunnels.R
\name{getAllFunnelsIncAIC}
\alias{getAllFunnelsIncAIC}
\title{Get all funnels, including AIC lines}
\usage{
getAllFunnelsIncAIC(cross, standardised = FALSE)
}
\arguments{
\item{cross}{The object of class \code{mpcross} containing the pedigree of interest}
\item{standardised}{Should the output funnels be standardised?}
}
\value{
Matrix of mixing orders that contribute to the final popluation. Rows DO NOT refer to specific genetic lines.
}
\description{
Get every order of the founding lines, which makes a contribution to the final population
}
\details{
This function is similar to \code{\link{getAllFunnels}}, but more useful for populations with maintenance (or AIC) generations. It returns a list of all the mixing orders in the initial generations, which make a genetic contribution to the final population. Unlike for \code{\link{getAllFunnels}}, rows of the returned matrix DO NOT refer to specific genetic lines.
}
\examples{
set.seed(1)
pedigree <- fourParentPedigreeRandomFunnels(initialPopulationSize = 1000,
selfingGenerations = 6, intercrossingGenerations = 1)
#Assume infinite generations of selfing in subsequent analysis
selfing(pedigree) <- "infinite"
#Generate random map
map <- qtl::sim.map(len = 100, n.mar = 101, anchor.tel = TRUE, include.x = FALSE)
#Simulate data
cross <- simulateMPCross(map = map, pedigree = pedigree, mapFunction = haldane, seed = 1L)
#Because we have maintenance in this experiment, we can't get out the funnels per genetic line
funnels <- getAllFunnels(cross)
dim(funnels)
funnels[1:10,]
#But we can get out a list of all the funnels that go into the experiment.
funnels <- getAllFunnelsIncAIC(cross)
dim(funnels)
funnels[1:10,]
}
|
84f64fe753f6c0e4bc6887562876d6bdb68726db
|
f335d68753c16e38f887e7cbaee903feeaa5d37b
|
/man/var_sinar.Rd
|
7d3004882b1e10fb430686e44544ac6a86e112d2
|
[] |
no_license
|
cran/sinar
|
fd50c470ed8924d6ef03c45a8ad641c17341dde6
|
bd1f88640db4bf80a5dacb3f938ce842383b80e7
|
refs/heads/master
| 2023-01-07T03:42:14.314920
| 2020-11-06T16:00:06
| 2020-11-06T16:00:06
| 311,435,252
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 536
|
rd
|
var_sinar.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cls_sinar.R
\name{var_sinar}
\alias{var_sinar}
\title{Empirical estimate for the variance of innovations.}
\usage{
var_sinar(X)
}
\arguments{
\item{X}{A integer matrix where each cell is the observed value in the
regular lattice.}
}
\value{
The estimated standard deviation in the \eqn{SINAR(1,1)}.
}
\description{
\eqn{\sigma^2_\epsilon} is the variance the innovations for the
\eqn{SINAR(1,1)} model.
}
\examples{
data("nematodes")
var_sinar(nematodes)
}
|
8e285cc88595bfdc2a487f21fe86de8580539673
|
89a1c2e27d5c39aa71a6f7e7680d10f362e3f0f6
|
/LawfulResidentStatusScrape.r
|
c3d16c53871fc9d759520fb6f76376427129dfbf
|
[] |
no_license
|
azankhanyari/USImmigration_Visualization
|
f9e9d3b59d2ae4d944d82ea826c9562edbb4376d
|
547af6b6faac3f63a77be10a2b944fb5c09518da
|
refs/heads/master
| 2022-03-25T13:33:20.079910
| 2019-12-17T17:40:30
| 2019-12-17T17:40:30
| 228,669,965
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 517
|
r
|
LawfulResidentStatusScrape.r
|
library(rvest)
url <- "https://www.dhs.gov/immigration-statistics/yearbook/2017/table2"
table3 <- url %>% html() %>% html_nodes(xpath = '//*[@id="content-area"]/div/div/article/div[1]/div[2]/div/div/table') %>% html_table(fill = TRUE)
table3_unlist <- table3[[1]]
NaturalisationByRegion <- table3_unlist[2:88,c(1,4)]
names(NaturalisationByRegion) <- c('Country','Total People in 2017')
write.csv(NaturalisationByRegion, file = "C:\\Users\\Rick\\Documents\\DV CA\\LawfulResidenceByCountry.csv",row.names = FALSE)
|
d2e0f0b72eb9fa2fe7ad75159c71b9adf7f951b1
|
180dd82b3c98dd23bab21ba2566277cd3a2a3d51
|
/R/read_members.R
|
6922a94c7afb96d21ab4c1cbda51eca4f4580019
|
[
"Apache-2.0"
] |
permissive
|
andrew-MET/harpIO
|
2e90861751a2b47ec8cdebde3de6d35414575cb7
|
976ddeca6337c31e7682d7a371552942cbb4cc66
|
refs/heads/master
| 2023-05-10T11:55:28.830541
| 2023-01-11T09:18:03
| 2023-01-11T09:18:03
| 275,838,990
| 0
| 3
|
NOASSERTION
| 2022-11-08T12:24:30
| 2020-06-29T14:27:58
|
R
|
UTF-8
|
R
| false
| false
| 7,580
|
r
|
read_members.R
|
# Read ensemble members from a forecast file
#
# \code{read_members} reads all ensemble members from a forecast file for a
# single lead time. It is possible to read from grib or netcdf files via the
# helper functions. It is assumed that grib files only contain one member and
# ntcdf files contain all members.
#
# @param model_files Files to read. For NetCDF only 1 file is expected, but for
# grib a vector of files can be passed. The character string 'mbr' must be
# somewhere in the paths for the member numbes to be identified. It is
# assumed that grib files only contain one member. For grib2 (yet to be
# implemented) this might not be the case.
# @param parameter The parameter to read.
# @param members A vector of numbers identifying the members to read. This is
# ignored for grib files as it is assumed that the members were already
# decided when the filenames were obtained.
# @param file_type The forecast file format. The function can attempt to
# ascertain the format from the file name, but if it can't \code{file_type}
# must be passed as an argument.
# @param lead_time The lead time to read.
# @param ... Arguments to be passed to \code{read_netcf} or \code{read_grib}
#
# @return A list containing: \cr \code{model_data}: The 3d field (the 3rd
# dimension is ensemble member). \cr \code{x}: The x coordinates in
# the projection of the forecast file. \cr \code{y}: The y coordinates
# in the projection of the forecast file. \cr \code{proj4_string}: The
# proj4 projection string for the forecast file. \cr \code{parameter}:
# The parameter that the 3d field represents. \cr \code{filename}: The
# full path to the forecast files.
# NOT currently in an exportable state. Use mepsr for now.
#
# @examples
# fname <- get_filenames(file_date = 2017080100, template = "meps_met")
# model_field <- read_members(fname, "precipitation_amount_acc", lead_time = 24)
# model_field <- read_members(fname, "Pcp", members = c(2, 4, 6), lead_time = 24)
#
# my_static_path <- "/lustre/storeB/users/andrewts/surfacePerturbations/grib"
# my_expt <- "MEPS_summer2017_sfcPertRef"
# my_template <- file.path(
# "${YYYY}${MM}${DD}${HH}",
# "${experiment}",
# "mbr${MBR3}/fc${YYYY}${MM}${DD}${HH}+${LDT3}.grib"
# )
# fname <- get_filenames(
# file_path = my_static_path,
# file_date = 2017052700,
# experiment = my_expt,
# template = my_template,
# lead_time = 3, member = seq(0, 10)
# )
# model_field <- read_members(fname, "fog")
### NEEDS UPDATING!!!! - change name to read_eps. Accommodate multi dimensions better.
read_members <- function(model_files,
parameter,
members = seq(0, 9),
file_type = NULL,
lead_time = NULL,
...) {
#
# read control and get domain info
#
if (is.null(file_type)) {
file_type <- tolower(tools::file_ext(model_files[1]))
if (! file_type %in% c("grib", "grb", "nc", "nc4", "netcdf")) {
if (stringr::str_detect(model_files[1], "grib")) {
file_type = "grib"
} else {
stop("Unable to ascertain file type. Call the function with file_type = '<file_type>'",
call. = FALSE
)
}
} else {
file_type <- switch(
file_type,
"grb" = "grib",
"nc" = "netcdf",
"nc4" = "netcdf",
file_type
)
}
}
if (tolower(file_type) == "grib") {
if (!requireNamespace("Rgrib2", quietly = TRUE)) {
stop("Package Rgrib2 required for read_members() - you can get it from HARP",
call. = FALSE
)
}
num_perturbed_members <- length(model_files) - 1
model_file <- model_files[1]
geofield_data <- read_grib(model_file, parameter)
domain_data <- meteogrid::DomainExtent(geofield_data)
x <- seq(domain_data$x0, domain_data$x1, domain_data$dx)
y <- seq(domain_data$y0, domain_data$y1, domain_data$dy)
proj4_string <- paste0(
"+", paste(
meteogrid::proj4.list2str(attr(geofield_data, "domain")$projection), collapse = " +"
)
) %>%
stringr::str_replace("latlong", "longlat") %>%
stringr::str_replace_all(" = ", "=") %>%
stringr::str_replace_all(" =", "=") %>%
stringr::str_replace_all("= ", "=")
members <- ifelse(length(model_files) > 1,
model_files %>%
strsplit("/") %>%
purrr::map(~ stringr::str_subset(., "mbr")) %>%
purrr::map_dbl(readr::parse_number),
0
)
data_all <- array(NA, c(dim(geofield_data), num_perturbed_members + 1))
data_all[, , 1] <- geofield_data
} else if (tolower(file_type) == "netcdf") {
if (!requireNamespace("ncdf4", quietly = TRUE)) {
stop("Package ncdf4 required for read_members() - Please install from CRAN",
call. = FALSE
)
}
if (is.null(lead_time)) stop("lead_time must be supplied for NetCDF data")
model_file <- model_files[1]
ncID <- ncdf4::nc_open(model_file)
x <- ncdf4::ncvar_get(ncID, "x")
y <- ncdf4::ncvar_get(ncID, "y")
proj4_string <- ncdf4::ncatt_get(ncID, "projection_lambert", "proj4")$value
num_members <- length(members)
nc_members <- ncdf4::ncvar_get(ncID, "ensemble_member")
ncdf4::nc_close(ncID)
if (num_members > length(nc_members)) {
cat("\nWARNING: Number of members in file =", length(nc_members))
cat("\n Number of members requested =", num_members)
cat("\n All members will be read from the file")
cat("\n")
members <- nc_members
num_members <- length(members)
}
num_perturbed_members <- num_members - 1
data_all <- array(NA, c(length(x), length(y), num_perturbed_members + 1))
data_all[, , 1] <- read_netcdf(model_file, parameter, members[1], lead_time, ...)
} else {
stop("Unknown file type: ", file_type, ". Can only deal with netcdf or grib",
call. = FALSE
)
}
#
# Get perturbed members
#
if (num_perturbed_members > 0) {
pb <- utils::txtProgressBar(min = 1, max = num_perturbed_members, initial = 1, style = 3)
for (member in 1:num_perturbed_members) {
member_name <- paste0("mbr", formatC(member, width = 3, flag = "0"))
if (file_type == "grib") {
model_file <- model_files[member + 1]
data_all[, , member + 1] <- read_grib(model_file, parameter)
} else if (file_type == "netcdf") {
data_all[, , member + 1] <- read_netcdf(model_file, parameter, members[member], lead_time, ...)
}
utils::setTxtProgressBar(pb, member)
}
}
#
# Convert units - it is assumed that when geopotential is requested, geopential
# height is what is wanted
#
is_temperature <- function(x) {
tolower(x) %in% c("t", "t2m", "sst") | stringr::str_detect(x, "temperature")
}
is_pressure <- function(x) {
tolower(x) == "pmsl" | stringr::str_detect(x, "pressure")
}
is_geopotential <- function(x) {
tolower(x) %in% c("z0m", "z") | stringr::str_detect(x, "geopotential")
}
if (is_temperature(parameter) & min(data_all, na.rm = TRUE) > 200) {
data_all <- data_all - 273.15
}
if (is_pressure(parameter) & min(data_all, na.rm = TRUE) > 2000) {
data_all <- data_all / 100
}
if (is_geopotential(parameter)) {
data_all <- data_all / 9.80665
}
list(
model_data = data_all,
x = x,
y = y,
member = members,
proj4_string = proj4_string,
parameter = parameter,
filename = model_files
)
}
|
1dff54020061706aec70ff0eb94fab77e1517e57
|
690c3a0b5858c6f6873ba2e01093da27f7b36e04
|
/R codes/Critical Slowing Down_R Codes/Out-of-sample predictions/BTC_appendix.R
|
92eb2ada8db02dd5cd41b2de7229f703c8b92cdb
|
[] |
no_license
|
stancld/bachelors_thesis
|
ca1552ac3d8ad31de5e27b6457258776b077e03e
|
d218639f0e76d9240f4225ae4cc57550ac9f8232
|
refs/heads/master
| 2023-03-26T16:18:58.484752
| 2021-03-29T21:57:08
| 2021-03-29T21:57:08
| 352,791,769
| 3
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,924
|
r
|
BTC_appendix.R
|
library(quantmod)
library(ggplot2)
library(gtable)
library(grid)
library(gridExtra)
library(tidyverse)
library(lubridate)
setwd("C:/Data/Skola/Bachelor's Thesis/R Codes")
getSymbols("BTC-USD", env = .GlobalEnv, from = '2015-08-20', to = '2018-02-23', periodicity = 'daily')
data <- log(`BTC-USD`[,6])
##### Period1
start <- 1
end <- which(time(data) == '2017-11-15')
(data1 <- data[start:end])
MA <- rep(NA, end - start + 1)
time_frame <- 1:end
gaussian_kernel <- function(sigma, r, t){
(1 / (sqrt(2*pi) * sigma)) * exp(-((r-t)^2) / (2 * sigma^2))
}
for (t in time_frame){
MA[t] <- sum(sapply(time_frame, function(x) gaussian_kernel(sigma = 5, r = x, t = t) * data1[x])) /
sum(sapply(time_frame, function(x) gaussian_kernel(sigma = 5, r = x, t = t)))
}
residuals <- rep(NA, end - start + 1)
residuals[1:end] <- data1[1:end] - MA[1:end]
plot(residuals, type = 'l')
### 100 days
# AR(1)
residuals.ar <- residuals[1:nrow(data)]
AR <- rep(NA, end - 100)
end1 <- end - 100
for (i in 1:end1){
AR[i] <- summary(lm(residuals.ar[(i+1):(i+99)] ~ residuals.ar[i:(i+98)]))$coefficients[2,1]
}
AR <- c(rep(NA, 100), AR, rep(NA, 100))
plot(AR, type = 'l')
# Variance
variance <- rep(NA, end - 100)
for (i in 1:end1){
start <- i
end2 <- i + 100
variance[i] <- sqrt(var(residuals[start:end2]))
}
plot(variance, type = 'l')
variance <- c(rep(NA, 100), variance, rep(NA, 100))
print(length(variance))
plot(variance, type = 'l')
###### PLOTS
p1 <- ggplot(data = NULL, mapping = aes(x = decimal_date(time(data)), y = data$`BTC-USD.Adjusted`)) +
geom_line(size = 0.8, colour = 'black') +
geom_line(aes(x = decimal_date(time(data)), y = c(MA,rep(NA,100))), size = 0.8, colour = 'red', alpha = 0.8) +
geom_vline(xintercept = max(decimal_date(time(data1))), linetype = 'dashed', size = 0.8) +
xlab("") +
ylab("") +
scale_x_continuous(limits = c(min(decimal_date(time(data))),
max(decimal_date(time(data)))), expand = c(0,0)) +
scale_y_continuous(limits = c(5.3, 10.5), expand = c(0,0)) +
theme_gray() +
theme(axis.text.x = element_text(face = 'bold', colour = 'black', size = 11),
axis.text.y = element_text(face = 'bold', colour = 'black', size = 11),
plot.title = element_text(face = 'bold', hjust = 0.5, size = 16)) +
geom_label(aes(x = max(decimal_date(time(data))) - 0.07, y = 10.3, fontface = 2),
label = 'BTC-USD', fill = 'grey')
p1
##### P2, residuals------
#residuals <- c(residuals, rep(NA, 100))
p2 <- ggplot(data = NULL, mapping = aes(x = decimal_date(time(data)), y = residuals)) +
geom_segment(mapping = aes(xend = decimal_date(time(data)), yend = 0), size = 0.7) +
xlab("") +
ylab("") +
theme_bw() +
theme(axis.text.x = element_text(face = 'bold', colour = 'black', size = 11),
axis.text.y = element_text(face = 'bold', colour = 'black', size = 11)) +
scale_x_continuous(limits = c(min(decimal_date(time(data))),
max(decimal_date(time(data)))), expand = c(0,0)) +
geom_label(aes(x = max(decimal_date(time(data))) - 0.08, y = 0.2, fontface = 2),
label = 'Residuals', fill = 'grey')
p2
##### P3, AR(1)------
p3 <- ggplot(data = NULL, mapping = aes(x = decimal_date(time(data)), y = AR)) +
geom_line(size = 0.8) +
xlab("") +
ylab("") +
theme_bw() +
theme(axis.text.x = element_text(face = 'bold', colour = 'black', size = 11),
axis.text.y = element_text(face = 'bold', colour = 'black', size = 11)) +
scale_x_continuous(limits = c(min(decimal_date(time(data))),
max(decimal_date(time(data)))), expand = c(0,0)) +
geom_label(aes(x = max(decimal_date(time(data))) - 0.046, y = 0.93, fontface = 2),
label = 'AR(1)', fill = 'grey')
p3
##### P4, var------
p4 <- ggplot(data = NULL, mapping = aes(x = decimal_date(time(data)), y = sqrt(variance))) +
geom_line(size = 0.8) +
xlab("") +
ylab("") +
theme_bw() +
theme(axis.text.x = element_text(face = 'bold', colour = 'black', size = 11),
axis.text.y = element_text(face = 'bold', colour = 'black', size = 11),
axis.title.x = element_text(face = 'bold', colour = 'black', size = 14)) +
scale_x_continuous(limits = c(min(decimal_date(time(data))),
max(decimal_date(time(data)))), expand = c(0,0)) +
geom_label(aes(x = max(decimal_date(time(data))) - 0.07, y = 0.245, fontface = 2),
label = 'Std. dev', fill = 'grey')
p4
g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g3 <- ggplotGrob(p3)
g4 <- ggplotGrob(p4)
g <- rbind(g1, g2, g3, g4, size = 'first')
g$widths <- unit.pmax(g1$widths, g2$widths, g3$widths, g4$widths)
grid.newpage()
grid.draw(g)
png('BTC.png', width = 900, height = 600)
grid.newpage()
grid.draw(g)
dev.off()
|
546de51905a5e5d73e981dceb64a28586b1e84d0
|
1648d74b87220c51ab86225e438dce2ff3cc72dc
|
/Churn_Prediction_Ensemble_Model.r
|
683155fa2f649d5161ad112d1f7bd405571068e9
|
[
"MIT"
] |
permissive
|
s0yabean/ds_portfolio
|
fa69d138a9761790d80db696fc4ad2b26d36f605
|
446c001ba63ffb9aee18ac9d252ff88520eba145
|
refs/heads/master
| 2020-06-16T20:55:33.358912
| 2019-07-07T22:44:41
| 2019-07-07T22:44:41
| 195,700,875
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 14,037
|
r
|
Churn_Prediction_Ensemble_Model.r
|
################################################################################################
# Churn-Prediction Machine Learning Project
# Description: A paid project for a venture-backed startup, helping to predict the likeliest customers
# about to churn for a B2C subscription-based app. Here, I am going through a couple of popular
# ML algorithms for this binary classification problem to find baseline performance, and also
# trying out an ensemble method to see if performance improves.
# Date: 2018 Spring
# Author: Tony Tong
################################################################################################
library(caret)
library(dplyr)
library(pROC)
library(vtreat)
library(corrplot)
################################################################################################
# Parallel Processing (To speed up machine learning speeds)
################################################################################################
# Parallel Processing (Added by Tony. My Mac has an i5 processor, which has 4 cores)
library(doParallel)
cl <- makeCluster(detectCores())
registerDoParallel(cl)
# run when rounding up machine learning project to stop the parallel processing
# stopCluster(cl)
################################################################################################
# Obtaining and Pre-processing Data
# Essentially here, I am running the data through a dummy variable conversion (converting text to numerics/ factors)
# and vtreat, which is to standardise, normalise and deal with discrepancies like missing values in the data.
# Workflow: Data -> dummyVar() -> vtreat() -> scale() for both TRAINING and TEST data.
################################################################################################
# Connect to db
connection <- XXXXXXX::pgsql_connect("XXXXXXXXXXXXXXXXXXXXXXXXXXXX")
# Training data. Positive class is set as default to be 0, when there is no renewal.
train_data <- dbGetQuery(connection, "XXXXXXXXXXXXXXXXXXXXXXXXXXXX")
# Test data. Key difference is that there is an extra date filter, to those before 21st Feb as there was an engineering change before.
test_data <- dbGetQuery(connection,"XXXXXXXXXXXXXXXXXXXXXXXXXXXX")
# Dummy variable transformation
transform = dummyVars(" ~ .", data = train_data)
train_data_dummy = data.frame(predict(transform, newdata = train_data))
str(train_data_dummy)
# create a cross frame experiment (TRAINING data)
cross_frame_experiment <- mkCrossFrameCExperiment(train_data_dummy, colnames(train_data_dummy), 'renewed', 1)
plan <- cross_frame_experiment$treatments
performance_stats <- plan$scoreFrame
treated_train <- cross_frame_experiment$crossFrame
# treat TEST data based on the same treatment plan on training data
test_data_dummy = data.frame(predict(transform, newdata = test_data))
treated_test <- vtreat::prepare(plan, test_data_dummy, pruneSig = NULL)
# Scaling
treated_train[, 1:24] = scale(treated_train[, 1:24])
treated_test[, 1:24] = scale(treated_test[, 1:24])
# Converting to character as a downstream function does not work with numerics/ factors.
treated_train$renewed_char <- ifelse(treated_train$renewed == 1, "yes", "no")
treated_test$renewed_char <- ifelse(treated_test$renewed == 1, "yes", "no")
################################################################################################
# Measuring Correlation and picking top 20 variables
################################################################################################
correlation <- cor(treated_train)
corrplot(correlation, method = "circle", tl.cex = .5, tl.col = 'black')
vars = as.data.frame(correlation) %>% mutate(variable = colnames(correlation)) %>%
select(variable, renewed) %>% arrange(desc(abs(renewed))) %>% dplyr::slice(2:21)
x_vars <- paste(unlist(vars[1:20,1]), collapse=' + ')
equation <- as.formula(paste("renewed_char", x_vars, sep = " ~ " ))
# str(treated_train)
# str(treated_test)
################################################################################################
# Creating a standard set of control parameters in train function
# Number and repeats are changable parameters
################################################################################################
myControl = trainControl(method = "cv", number = 3, repeats = 2, search = 'grid',
classProbs = TRUE, summaryFunction=twoClassSummary, verboseIter = TRUE)
################################################################################################
# Machine Learning
# Methods tried:
# Random Forest (rf)
# Logistic Regression (glm)
# Xgboost (xgb)
# Ada (Boosted Classification Tree)
# Naive Bayes (nb)
# Support Vector Machine (svm)
################################################################################################
#################################
# Random Forest
#################################
rf_fit = train(equation, data = treated_train, method="rf", metric = 'ROC', trControl = myControl)
rf_fit
# testing data
rf_fit_prob <- predict(rf_fit, newdata = treated_test, type='prob')[,2]
roc(treated_test$renewed, rf_fit_prob) %>% auc
confusionMatrix(ifelse(rf_fit_prob>= 0.50, 1, 0), treated_test$renewed)
# Training Data
# ROC Sens Spec
# 0.8192454 0.8405315 0.6713205
# Testing Data
# ROC Sens Spec
# 0.72 0.6272 0.6926
#################################
# GLMNET (Logistic Regression)
#################################
glmnet_grid <- expand.grid(.alpha = c(0, 0.5, 1), .lambda=seq(0, 1, by=.01))
glmnet_fit <- train(equation, data = treated_train,
method='glmnet', tuneGrid=glmnet_grid, metric='ROC',
trControl= myControl)
glmnet_fit$results
# testing data
glmnet_fit_prob <- predict(glmnet_fit, newdata = treated_test, type='prob')[,2]
roc(treated_test$renewed, glmnet_fit_prob) %>% auc
confusionMatrix(ifelse(glmnet_fit_prob >= 0.50, 1, 0), treated_test$renewed)
# Training Data
# ROC Sens Spec
# 0.8186528 0.8968968 0.6185666
# Testing Data
# ROC Sens Spec
# 0.7778 0.7629 0.6667
####################################################
# XGBOOST - extreme gradient boosting (tree method)
####################################################
# xgboost tree (0.9605899 0.8795028 0.9524339)
xgb_grid <- expand.grid(nrounds = 50, #the maximum number of iterations
eta = c(0.01,0.1), # shrinkage
max_depth = c(2,6,10),
gamma = 0, #default=0
colsample_bytree = 1, #default=1
min_child_weight = 1, #default=1
subsample = c(.05, 1))
xgb_fit <- train(equation, data = treated_train, method='xgbTree', tuneGrid=xgb_grid, metric='ROC',
trControl=myControl)
xgb_fit
xgb_fit_prob <- predict(xgb_fit, newdata = treated_test, type='prob')[,2]
roc(treated_test$renewed, xgb_fit_prob ) %>% auc
confusionMatrix(ifelse(xgb_fit_prob >= 0.50, 1, 0), treated_test$renewed)
# Training Data
# ROC Sens Spec
# 0.8192677 0.8593222 0.6636708
# Testing Data
# ROC Sens Spec
# 0.7864 0.6681 0.7305
################################################################################################
# SVM Modelling
################################################################################################
svm_fit <- train(equation, data = treated_train,
method='svmLinear2', metric='ROC',
trControl=myControl)
svm_fit
svm_fit_prob <- predict(svm_fit, newdata = treated_test, type='prob')[,2]
roc(treated_test$renewed, svm_fit_prob ) %>% auc
confusionMatrix(ifelse(svm_fit_prob >= 0.50, 1, 0), treated_test$renewed)
# Training Data
# ROC Sens Spec
# 0.8079858 0.9309330 0.5743828
# Testing Data
# ROC Sens Spec
# 0.7191 0.7694 0.6527
################################################################################################
# NB Modelling
################################################################################################
nb_fit <- train(equation, data = treated_train,
method='nb', metric='ROC',
trControl=myControl)
nb_fit
nb_fit_prob <- predict(nb_fit, newdata = treated_test, type='prob')[,2]
roc(treated_test$renewed, nb_fit_prob ) %>% auc
confusionMatrix(ifelse(nb_fit_prob >= 0.50, 1, 0), treated_test$renewed)
# Training Data
# ROC Sens Spec
# 0.7977393 0.9090973 0.5955845
# Testing Data
# ROC Sens Spec
# 0.755 0.7543 0.6527
################################################################################################
# Boosted Classification Trees
################################################################################################
ada_fit <- train(equation, data = treated_train,
method='ada', metric='ROC',
trControl=myControl)
ada_fit
ada_fit_prob <- predict(ada_fit, newdata = treated_test, type='prob')[,2]
roc(treated_test$renewed, ada_fit_prob) %>% auc
confusionMatrix(ifelse(ada_fit_prob >= 0.50, 1, 0), treated_test$renewed)
# Training Data
# ROC Sens Spec
# 0.8219956 0.8313704 0.6812450
# Testing Data
# ROC Sens Spec
# 0.7841 0.7651 0.6567
################################################################################################
# Seeing results on training data (during resampling) to approximate performace of alogrithms
################################################################################################
# Based on a subset training data (sample bags)
results = resamples(list(ada = ada_fit, nb = nb_fit, svm = svm_fit,
xgb = xgb_fit, rf = rf_fit, glmnet = glmnet_fit))
summary(results)
bwplot(results)
dotplot(results)
# dev.off() #if plot does not show
################################################################################################
# Ensemble Modelling
################################################################################################
# Adding all 6 to build an ensemble model
treated_train$ada_fit_prob = predict(ada_fit, newdata = treated_train, type='prob')[2] %>% unlist()
treated_train$nb_fit_prob = predict(nb_fit, newdata = treated_train, type='prob')[2] %>% unlist()
treated_train$svm_fit_prob = predict(svm_fit, newdata = treated_train, type='prob')[2] %>% unlist()
treated_train$rf_fit_prob = predict(rf_fit, newdata = treated_train, type='prob')[2] %>% unlist()
treated_train$glmnet_fit_prob = predict(glmnet_fit, newdata = treated_train, type='prob')[2] %>% unlist()
treated_train$xgb_fit_prob = predict(xgb_fit, newdata = treated_train, type='prob')[2] %>% unlist()
treated_test$ada_fit_prob = predict(ada_fit, newdata = treated_test, type='prob')[2] %>% unlist()
treated_test$nb_fit_prob = predict(nb_fit, newdata = treated_test, type='prob')[2] %>% unlist()
treated_test$svm_fit_prob = predict(svm_fit, newdata = treated_test, type='prob')[2] %>% unlist()
treated_test$rf_fit_prob = predict(rf_fit, newdata = treated_test, type='prob')[2] %>% unlist()
treated_test$glmnet_fit_prob = predict(glmnet_fit, newdata = treated_test, type='prob')[2] %>% unlist()
treated_test$xgb_fit_prob = predict(xgb_fit, newdata = treated_test, type='prob')[2] %>% unlist()
# Scaling the probabilities just like all the other data
treated_train[, 27:32] = scale(treated_train[, 27:32])
treated_test[, 27:32] = scale(treated_test[, 27:32])
equation_ensemble <- as.formula("XXXXXXXXXXXXXXXXXXXXXXXXXXXX")
#################################
# GLMNET (Logistic Regression)
# Reruning the machine learning
# using glmnet (arbitrary)
#################################
glmnet_fit_ensemble <- train(equation_ensemble, data = treated_train, method='glmnet',
tuneGrid=glmnet_grid, metric = 'ROC', trControl = myControl)
glmnet_fit_ensemble_prob <- predict(glmnet_fit_ensemble, newdata = treated_test , type='prob')[,2]
# KEEP POSITIVE CLASS AT 0! (Did not renew, which is the default in the confusion matrix function)
roc(treated_test$renewed, glmnet_fit_ensemble_prob) %>% auc
confusionMatrix(ifelse(glmnet_fit_ensemble_prob >= 0.50, 1, 0), treated_test$renewed)
####################################################
# XGBOOST - extreme gradient boosting (tree method)
####################################################
xgb_grid <- expand.grid(nrounds = 50, #the maximum number of iterations
eta = c(0.01,0.1), # shrinkage
max_depth = c(2,6,10),
gamma = 0, #default=0
colsample_bytree = 1, #default=1
min_child_weight = 1, #default=1
subsample = c(.05, 1))
xgb_fit_ensemble <- train(equation_ensemble, data = treated_train, method='xgbTree',
tuneGrid=xgb_grid, metric = 'ROC', trControl = myControl)
xgb_fit_ensemble_prob <- predict(xgb_fit_ensemble, newdata = treated_test , type='prob')[,2]
# KEEP POSITIVE CLASS AT 0! (Did not renew, which is the default in the confusion matrix function)
roc(treated_test$renewed, xgb_fit_ensemble_prob) %>% auc
confusionMatrix(ifelse(xgb_fit_ensemble_prob >= 0.50, 1, 0), treated_test$renewed)
####################################################
# Conclusion
####################################################
# For single algorithm, having boosted classification trees gave the best performane ROC wise, so we can tell that tree-based
# algorithms seem to work well for this problem type.
# During the ensembling, there was severe overfitting on the training data, as the training accuracy went up to 99% but testing accuracy remained similar to baseline.
# This suggests that the main bottleneck is not from algorithms, perhaps we need more/ better data or fine-tuning hyperparameters.
|
2a1dd8265d35f84307c76216c8a3c851da7c83a3
|
41972a07b4ff2f873dc0f786621bdf3b48279af2
|
/dataType/list.R
|
bcefbf01da8d89886b31db3aad5627787b8f1309
|
[] |
no_license
|
juniuszhou/R_language
|
4920be49e26d6bbccd54839e27504f36b4b188c6
|
e4925034336b372df13b310b410c7e3d34daeef7
|
refs/heads/master
| 2021-01-18T22:58:19.408997
| 2016-05-12T07:36:47
| 2016-05-12T07:36:47
| 21,159,610
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,072
|
r
|
list.R
|
# _____________________________________________________
# To make a list "person", actually it is a map.
# 矢量中的元素只能是基本数据类型，而列表中的元素可以是R中的各种对象。
# 一个矢量中的元素必须是同一类型，而一个列表中的元素可以是不同种类的对象。
# each item in the list could be different type, different length.
# and each item has a name.
person <- list(name="payal", x=2, y=9, year=1990)
person
# Accessing things inside a list --
person$name
person$x
person["name"]
# data without name.
q <- list(1,2,3)
q
# pairlist
p <- pairlist()
p$a <- 1
p$a
p
a <- c(1,2,3)
b <- c(4,5,6)
l <- list(a, b)
l[1]
# get internal data
a <- list(1, 2, 3)
a[1]
a[[1]]
x<- list(a = c(0, 1, 2), b = c('a', 'b', 'c'))
# following both methods are the same just different format
x[[c(1,2)]]
x[[1]][[2]]
# filter columns, seems wrong
x[x$a>0]
x <- list(abc = c(0, 1, 2))
# partial name match. just for prefix is unique.
x$a
y='a'
# following doesn't work because expression behind $ can't be evaluated.
x$y
#
|
e4c9b88e89971bcc5cb9ec88c5259f08b1da451b
|
74550574a2c3aacffc61e6f5bb215e25f35e430b
|
/R/moc.R
|
4409d64f62ef3d65a8ca12fc335625b90125ec49
|
[] |
no_license
|
cran/moc
|
797ebc7b51f0bdc0d6badcd2b41152221dfa47de
|
d9e5491d9bdf1229b033f9ac6b441e4ffe023eb9
|
refs/heads/master
| 2021-06-03T19:05:14.538630
| 2019-02-28T21:32:16
| 2019-02-28T21:32:16
| 17,719,038
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 69,367
|
r
|
moc.R
|
.packageName <- "moc"
##
## moc : Package to fit general multivariate finite mixtures models
##
## Copyright (C) 2000-2019 Bernard Boulerice
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License version 3 as published by
## the Free Software Foundation.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
##
## SYNOPSIS
#
# moc(y, density=NULL, joint=FALSE, groups=1,
# gmu=NULL, gshape=NULL, gextra=NULL, gmixture=inv.glogit, expected = NULL,
# pgmu=NULL, pgshape=NULL, pgextra=NULL, pgmix=NULL, check.length=TRUE,
# scale.weight=FALSE, wt=1, data=NULL,
# ndigit=10, gradtol=0.0001, steptol=gradtol, iterlim=100, print.level=1,...)
#
## DESCRIPTION
##
## Function to fit general nonlinear multivariate finite mixtures model
##
##
moc<- function(y, density=NULL, joint=FALSE, groups=1,
gmu=NULL, gshape=NULL, gextra=NULL, gmixture=inv.glogit, expected = NULL,
pgmu=NULL, pgshape=NULL, pgextra=NULL, pgmix=NULL, check.length=TRUE,
scale.weight=FALSE, wt=1, data=NULL,
ndigit=10, gradtol=0.0001, steptol=gradtol, iterlim=100, print.level=1,...)
{
call <- sys.call()
if (!is.null(data)) {
mocData <- list2env(eval(data))
density <- eval(call$density,mocData)
if(!is.null(call$gmixture)) gmixture <- eval(call$gmixture,mocData)
gmu <- eval(call$gmu,mocData)
gshape <- eval(call$gshape,mocData)
gextra <- eval(call$gextra,mocData)
expected <- eval(call$expected,mocData)
}
# attach(data,pos=2);on.exit(detach(2),add=TRUE)
# thisEnv <- environment()
# for( i in names( data ) ) {
# assign( i, data[[i]], envir = thisEnv )
# }
resp<-as.matrix(eval(substitute(y)))
ndim<-dim(resp)
n<-ndim[1]
nt<-ndim[2]
ng<-groups
inv.glogit<- if(ng==1) {function(gmix) {cbind(1)}} else
{function(gmix) {rbind(c(1,exp(gmix)))/(1+sum(exp(gmix)))}}
if(groups > 1) attr(inv.glogit,"parameters")<-paste(" G",2:groups," vs G1",sep="")
##
## check density
##
if(!is.function(density)) stop("\ndensity must be a function\n")
if(length(formals(density))>4) stop("\ndensity must not use more than 4 arguments\n")
.density<-density
##
## count the number of parameters
##
npl <- length(pgmu)
nps <- length(pgshape)
npext <- length(pgextra)
npmix <- length(pgmix)
np <- npl+nps+npmix+npext
##
## create local versions of functions and
## check that the functions return correct values
##
#repn<-deparse(substitute(rep(1,n),list(n=n)))
if(!is.list(gmu) & (length(gmu) != ng) & !is.null(gmu))
stop(paste("\ngmu must be a list of functions of length ",ng))
if(!is.list(gshape) & (length(gshape) != ng) & !is.null(gshape))
stop(paste("\ngshape must be a list of functions of length ",ng))
if(!is.list(gextra) & (length(gextra) != ng) & !is.null(gextra))
stop(paste("\ngextra must be a list of functions of length ",ng))
.gmu<-list()
.gshape<-list()
.gextra<-list()
.expected<-list()
for( ig in 1:ng) {
##
## check gmu
##
if(length(dim(gmu[[ig]](pgmu)))!=2 | dim(gmu[[ig]](pgmu))[2]!=nt)
stop(paste("\ngmu in group",ig,"must return a matrix with vectors of length nvar = ",nt))
if(any(is.na(gmu[[ig]](pgmu)))) stop(paste("\nThe gmu function returns NAs in group ",ig))
if(dim(gmu[[ig]](pgmu))[1]==1) {
fcall<-paste(deparse(gmu[[ig]],control=NULL)[1],collapse="")
fbody<-paste(deparse(gmu[[ig]],control=NULL)[-1],collapse="\n")
ftext<-paste(c(fcall,"{matrix(",fbody,",",n,",",nt,",byrow=TRUE)}"),collapse="")
.gmu[[ig]]<-eval(parse(text=ftext))
environment(.gmu[[ig]])<-environment(gmu[[ig]])
} else
if(dim(gmu[[ig]](pgmu))[1]==n) .gmu[[ig]]<-gmu[[ig]] else
stop(paste("\ngmu in group",ig,"should return a matrix of length 1 or ",n))
##
## check gshape
##
if(is.null(gshape) & is.null(pgshape)) .gshape[[ig]]<- function(...) 1 else
{
if(length(dim(gshape[[ig]](pgshape)))!=2 | dim(gshape[[ig]](pgshape))[2]!=nt)
stop(paste("\ngshape in group",ig,"must return a matrix with vectors of length nvar = ",nt))
if(any(is.na(gshape[[ig]](pgshape))))
stop(paste("\nThe shape function returns NAs in group",ig))
if(dim(gshape[[ig]](pgshape))[1]==1) {
fcall<-paste(deparse(gshape[[ig]],control=NULL)[1],collapse="")
fbody<-paste(deparse(gshape[[ig]],control=NULL)[-1],collapse="\n")
ftext<-paste(c(fcall,"{matrix(",fbody,",",n,",",nt,",byrow=TRUE)}"),collapse="")
.gshape[[ig]]<-eval(parse(text=ftext))
environment(.gshape[[ig]])<-environment(gshape[[ig]])
} else
if(dim(gshape[[ig]](pgshape))[1]==n ) .gshape[[ig]]<-gshape[[ig]] else
stop("\ngshape in group",ig,"should return a matrix of length 1 or",n)
}
##
## check gextra
##
if(is.null(gextra) & is.null(pgextra)) .gextra[[ig]]<- function(...) 1 else
{
if(length(dim(gextra[[ig]](pgextra)))!=2 |
(((lgext<-dim(gextra[[ig]](pgextra))[2])!=nt)&check.length))
stop(paste("\ngextra in group",ig,"must return a matrix with vectors of length nvar =",nt))
if(any(is.na(gextra[[ig]](pgextra))))
stop(paste("\nThe extra parameters function returns NAs in group",ig))
if(dim(gextra[[ig]](pgextra))[1]==1) {
fcall<-paste(deparse(gextra[[ig]],control=NULL)[1],collapse="")
fbody<-paste(deparse(gextra[[ig]],control=NULL)[-1],collapse="\n")
ftext<-paste(c(fcall,"{matrix(",fbody,",",n,",",ifelse(check.length,nt,lgext),
",byrow=TRUE)}"),collapse="")
.gextra[[ig]]<-eval(parse(text=ftext))
environment(.gextra[[ig]])<-environment(gextra[[ig]])
} else
if(dim(gextra[[ig]](pgextra))[1]==n ) .gextra[[ig]]<-gextra[[ig]] else
stop(paste("\ngextra in group",ig,"should return a matrix of length 1 or",n))
}
if (is.null(expected))
{
environment(.gmu[[ig]])<-globalenv()
.expected[[ig]]<- eval(expression(function(p) {gmu[[k]](p[1:npl])}),
list(k=ig,gmu=.gmu,npl=npl))
} else
{
ptot <- c(pgmu,pgshape,pgextra,pgmix)
##
## check expected
##
if(length(dim(expected[[ig]](ptot)))!=2 | dim(expected[[ig]](ptot))[2]!=nt)
stop(paste("\nexpected in group",ig,"must return a matrix with vectors of length nvar =",nt))
if(any(is.na(expected[[ig]](ptot)))) stop(paste("\tThe expected function returns NAs in group",ig))
if(dim(expected[[ig]](ptot))[1]==1){
fcall<-paste(deparse(expected[[ig]],control=NULL)[1],collapse="")
fbody<-paste(deparse(expected[[ig]],control=NULL)[-1],collapse="\n")
ftext<-paste(c(fcall,"{matrix(",fbody,",",n,",",nt,",byrow=TRUE)}"),collapse="")
.expected[[ig]]<-eval(parse(text=ftext))
environment(.expected[[ig]])<-environment(expected[[ig]])
} else
if(dim(expected[[ig]](ptot))[1]==n) .expected[[ig]]=expected[[ig]] else
stop(paste("\nexpected for group",ig,"should return a matrix of length 1 or",n))
environment(.expected[[ig]])<-globalenv()
}
}
##
## check the returned values of the mixture function
##
if(is.null(gmixture) & is.null(pgmix)) { .gmixture<-function(...) {1} } else
{
if(dim(gmixture(pgmix))[2]!=ng)
stop(paste("\ngmixture must return a matrix with vectors of length groups=",ng))
if(any(is.na(gmixture(pgmix)))) stop("\nThe mixture function returns NAs")
if(any(gmixture(pgmix)<0) | any(abs(apply(gmixture(pgmix),1,sum)-1)>.Machine$double.eps^0.5))
warning("\nThe mixture function components probabilities must be >=0 and sum to 1")
if(dim(gmixture(pgmix))[1]==1 ) {
fcall<-paste(deparse(gmixture,control=NULL)[1],collapse="")
fbody<-paste(deparse(gmixture,control=NULL)[-1],collapse="\n")
ftext<-paste(c(fcall,"{matrix(",fbody,",",n,",",ng,",byrow=TRUE)}"),collapse="")
.gmixture<-eval(parse(text=ftext))
environment(.gmixture)<-environment(gmixture)
} else
if(dim(gmixture(pgmix))[1]==n) .gmixture<-gmixture else
stop(paste("\ngmixture should return a matrix of length 1 or",n))
}
##
## check and scale weights if necessary
##
wt<-as.vector(wt)
if((length(wt)==1) & (wt==1)) wt <- rep(1,n)
if(length(wt)!=n) stop("\nwt must be the same length as the other variables")
if(min(wt)<0) stop("\nAll weights must be non-negative")
if(any(is.na(wt))) stop("\n weights contain NA\n")
if((sum(wt)!=n) & scale.weight) {
warning("\nThe weights have been rescaled to sum to the the sample size ",n,"\n")
wt<-wt/mean(wt)
}
##
## define the likelihood functions
##
## New Code
naresp <- which(is.na(resp))
jointlike <- if(joint) {
function(mdens) {mdens}
} else {
function(mdens)
{
mdens[naresp] <- 1
.C("cjointlike", as.double(mdens), as.integer(dim(mdens)[1]),
as.integer(dim(mdens)[2]), jll = double(dim(mdens)[1]),NAOK=TRUE,PACKAGE="moc")$jll
# Cjointlike( as.double(mdens), as.integer(dim(mdens)[1]),as.integer(dim(mdens)[2]),
# jll = double(dim(mdens)[1]))$jll
}
}
loglike <- function(p) {
parm <- split(p, rep(c("mu", "shape", "extra", "mix"),
c(npl, nps, npext, npmix)))
dens <- sapply(1:ng, function(ind) jointlike(.density(resp,
.gmu[[ind]](parm$mu), .gshape[[ind]](parm$shape),
.gextra[[ind]](parm$extra))))
.C("mixloglike", as.double(dens), as.double(.gmixture(parm$mix)),
as.double(wt), as.integer(n), as.integer(ng),
mll = double(1),NAOK=TRUE,PACKAGE="moc")$mll
# Cmixloglike( as.double(dens), as.double(.gmixture(parm$mix)),
# as.double(wt), as.integer(n), as.integer(ng),
# mll = double(1))$mll
}
##
##
## check that the likelihood returns an appropriate value and minimize
##
p<-c(pgmu,pgshape,pgextra,pgmix)
if(is.na(loglike(p)))
stop("\nLikelihood returns NAs: probably invalid initial values")
if(np>0){
dt<-system.time(z0 <- nlm(loglike,p=p,hessian=TRUE,
ndigit=ndigit,gradtol=gradtol,steptol=steptol,
iterlim=iterlim,print.level=print.level,...))[3]} else
{dt<-system.time(z0 <- list(minimum=loglike(p),estimate=p,code=0,iterations=0))[3]}
cat("\n Estimation took ",dt," seconds.\n")
##
## compute cov and se's
##
if(np==0)cov <- NULL else
if(np==1)cov <- 1/z0$hessian else
{
a <- if(any(is.na(z0$hessian)) | any(abs(z0$hessian)==Inf)) 0 else
qr(z0$hessian)$rank
if(a==np)cov <- solve(z0$hessian) else
cov <- matrix(NA,ncol=np,nrow=np)
}
se <- sqrt(diag(cov))
##
## compute mixture and posterior probabilities
##
parm<-split(z0$estimate,rep(c("mu","shape","extra","mix"),c(npl,nps,npext,npmix)))
pmix<-.gmixture(parm$mix)
if(joint){
post1<- .C("mixpost",
as.double(sapply(1:ng,function(ind) .density(
resp,.gmu[[ind]](parm$mu),.gshape[[ind]](parm$shape),
.gextra[[ind]](parm$extra)))), as.double(pmix),
as.double(wt), as.integer(n), as.integer(ng),post=double(n*ng),
PACKAGE="moc")$post
} else
{ post1<-.C("mixpost",
as.double(sapply(1:ng,function(ind) jointlike(
.density(resp,.gmu[[ind]](parm$mu),.gshape[[ind]](parm$shape),
.gextra[[ind]](parm$extra))))), as.double(pmix),
as.double(wt), as.integer(n), as.integer(ng),post=double(n*ng),
PACKAGE="moc")$post
}
dim(post1) <- c(n,ng)
dimnames(post1)<-list(NULL,paste("Group",1:ng,sep=""))
##
## compute posterior prob., fitted and observed values
##
mpost<-apply(post1*wt,2,mean)
fitted.mean<-matrix(t(sapply(1:ng,function(ind)
apply(.expected[[ind]](z0$estimate)*post1[,ind]*wt,2,mean,na.rm=TRUE)))/mpost,ng,nt)
mpost<-sapply(1:ng,function(ind) apply(post1[,ind]*wt*ifelse(is.na(resp),NA,1),2,mean,na.rm=TRUE))
observed.mean<-matrix(t(sapply(1:ng,function(ind) apply(post1[,ind]*wt*resp,2,mean,na.rm=TRUE))/mpost),ng,nt)
dimnames(fitted.mean)<-list(paste("Group",1:ng,sep=""),
ifelse(is.na(nchar(temp<-dimnames(resp)[[2]])[1:nt]) ,
paste("V",1:nt,sep=""),temp))
dimnames(observed.mean)<-list(paste("Group",1:ng,sep=""),
ifelse(is.na(nchar(temp<-dimnames(resp)[[2]])[1:nt]) ,
paste("V",1:nt,sep=""),temp))
##
## clean functions environment and
## makes the weights callable
##
environment(.density)<-globalenv()
for(ig in 1:ng)
{
environment(.gmu[[ig]])<-globalenv()
environment(.gshape[[ig]])<-globalenv()
environment(.gextra[[ig]])<-globalenv()
}
environment(.gmixture)<-globalenv()
attributes(.gmu)<-attributes(gmu)
attributes(.gshape)<-attributes(gshape)
attributes(.gextra)<-attributes(gextra)
attributes(.gmixture)<-attributes(gmixture)
attributes(.density)<-attributes(density)
attributes(.expected)<-attributes(expected)
gname<-paste("Group",1:ng,sep="")
names(.gmu)<-gname
names(.gshape)<-gname
names(.gextra)<-gname
names(.expected)<-gname
wt<-match.call()$wt
if(is.null(wt)) {wt<-call("rep",1,n)} else
{
if(scale.weight) {wt<-substitute(as.vector(a)/mean(as.vector(a)),list(a=wt)) } else
wt<-substitute(as.vector(a),list(a=wt))
}
if(any(.gmixture(parm$mix)<0) | any(abs(apply(rbind(.gmixture(parm$mix)),1,sum)-1)>.Machine$double.eps^0.5))
warning("\nThe final mixture probablities are not all >=0 or don't sum to 1.\n")
##
## return a list of class moc
##
moc.out <- list(
call=call,
data=match.call()$data,
resp=substitute(as.matrix(txt),list(txt=match.call()$y)),
density=.density,
joint=joint,
nsubject=n,
nvar=nt,
nobs=sum(!is.na(resp)),
groups=ng,
npar=c(npl,nps,npext,npmix),
gmu=.gmu,
gshape=.gshape,
gextra=.gextra,
gmixture=.gmixture,
expected = .expected,
prior.weights=wt,
post.prob=post1,
loglikelihood=-z0$minimum,
df=sum(eval(wt))*nt-np,
AIC=2*z0$minimum+2*np,
BIC=2*z0$minimum+np*log(sum(eval(wt))*nt),
coefficients=z0$estimate,
cov=cov,
hessian=z0$hessian,
fitted.mean=fitted.mean,
observed.mean=observed.mean,
iterations=z0$iterations,
code=z0$code,
execution.time=dt)
class(moc.out) <- "moc"
return(moc.out) }
update.moc <- function(object,groups=1:object$groups,parm=object$coef,what=NULL,evaluate=FALSE,...)
{
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc\n")
moc.args <- c("y", "density", "joint", "groups", "gmu", "gshape", "gextra", "gmixture", "expected",
"pgmu", "pgshape", "pgextra", "pgmix", "check.length", "scale.weight", "wt", "data",
"ndigit", "gradtol", "steptol", "iterlim", "print.level")
ocall <- as.list(object$call)
names(ocall)[2] <- "y"
ng <- length(groups)
ocall$groups <- ng
arg.list <- as.list(match.call(expand.dots=FALSE))$...
valid.arg <- match(names(arg.list),moc.args)
if(length(valid.arg)) if(any(is.na(valid.arg))) stop(paste("\tInvalid moc arguments\n",names(arg.list)[is.na(valid.arg)]))
pnames <- c("mu","shape","extra","mix")
if(!all(groups %in% 1:object$groups)) stop(paste("\n\tgroups should be a vector of valid group numbers for",paste(substitute(object)),"\n"))
oldparm <- split(object$coefficients,rep(pnames,object$npar))
objpnam <- pnames[which(object$npar>0)]
if(!is.list(parm)) {
parm <- split(parm,rep(pnames,object$npar))
parm <- replace(list(mu=NULL,shape=NULL,extra=NULL,mix=NULL),names(parm),parm)
}
if((length(unlist(parm)) != sum(object$npar)) | !all(pnames %in% names(parm))) {
stop("\n\tparm should be a named list (mu,shape,extra,mix) or vector of corect length\n")}
if(!all(object$npar == sapply(parm,length))) stop("\n\tYou supplied the wrong number of parameters\n")
if(is.null(what)){
ocall$pgmu <- parm$mu
ocall$pgshape <- parm$shape
ocall$pgextra <- parm$extra
ocall$pgmix <- parm$mix
for(ln in names(arg.list)) ocall[[ln]] <- arg.list[[ln]]
if(evaluate) return(eval(as.call(ocall))) else return(as.call(ocall))
}
if(!is.list(what)) {
what <- split(what,rep(pnames,object$npar))
what <- replace(list(mu=NULL,shape=NULL,extra=NULL,mix=NULL),names(what),what)
}
if((length(unlist(what)) != sum(object$npar)) | !all(pnames %in% names(what))) {
stop("\n\twhat should be a named list (mu,shape,extra,mix) or vector of correct length\n")}
if(!all(object$npar == sapply(what,length))) stop("\n\tYou supplied the wrong number of constraints in what\n")
update.fun <- function(fun,parm,oldparm,what,funlist=TRUE)
{
what <- replace(what,what==0,NA)
pfree <- which(is.na(what))
if(length(pfree)) {
npfree <- 1:length(pfree)
freena <- which(is.na(parm) & is.na(what))
parm[freena] <- oldparm[freena]
} else npfree <- NULL
wnegpos <- replace(what,what<0,NA)
peq <- which(!is.na(wnegpos))
if(length(peq)){
npeq <- unclass(factor(wnegpos,exclude=NA))[peq]+length(npfree)
unieq <- sapply(split(parm[peq],npeq),function(x) length(unique(x)))
if(any(unieq>1)) stop("\n\tSome free parameters with equality constraints have different values")
if(any(is.na(parm[peq]))) stop("\n\tYou are trying to put equality constraint on 'NA' values\n")
peq.first <- sapply(split(peq,npeq),function(x) x[1])
} else {
npeq <- NULL
unieq <- NULL
peq.first <- NULL
}
wnegpos <- replace(what,what>0,NA)
pfix <- which(!is.na(wnegpos))
if(length(pfix)) {
npfix <- unclass(factor(wnegpos,exclude=NA))[pfix]
unifix <- sapply(split(parm[pfix],npfix),function(x) length(unique(x)))
if(any(is.na(parm[pfix]))) stop("\n\tYou are trying to fix some parameters with 'NA' value")
fixna <- which(is.na(parm) & !is.na(wnegpos))
parm[fixna] <- oldparm[fixna]
} else {
npfix <- NULL
unifix <- NULL
}
startval <- parm[c(pfree,peq.first)]
oparstr <- character(length(c(pfree,peq)))
oparstr[pfix] <- paste(parm[pfix])
oparstr[c(pfree,peq)] <- paste("np[",c(npfree,npeq),"]",sep="")
if(funlist) {
newfun <- as.pairlist(lapply(groups,function(gr) eval(parse(text=paste("function(np){",deparse(fun,width.cutoff=500,control=NULL),
"[[",gr,"]](c(",paste(oparstr,collapse=","),"))}")),parent.frame(2))))
names(newfun) <- paste("Group",groups,sep="")
}
else {
newfun <- eval(parse(text=paste("function(np){",deparse(fun,width.cutoff=500,control=NULL),
"(c(",paste(oparstr,collapse=","),"))}")),parent.frame(2))
}
attributes(newfun) <- attributes(eval(fun,parent.frame(2)))
if(!is.null(attr(newfun,"parameters"))) attr(newfun,"parameters") <-
tapply(attr(newfun,"parameters")[c(pfree,peq)],c(npfree,npeq),paste,collapse=",")
list(newfun=newfun,startval=startval,fixval=parm[pfix],newpar=c(npfree,npeq),oldpar=c(pfree,peq))
}
newpar <- alist(mu=NULL,shape=NULL,extra=NULL,mix=NULL)
for(pn in pnames[-4]) {
if(!is.null(tmp <- ocall[[paste("g",pn,sep="")]])) {
newfun <- update.fun(tmp,parm[[pn]],oldparm[[pn]],what[[pn]])
## For some reason statements like
# assign(paste("ocall$g",pn,sep=""),newfun$newfun)
# if(!is.null(ocall[[paste("pg",pn,sep="")]])) assign(paste("ocall$pg",pn,sep=""), newfun$startval)
## don't do the assignment and statement like
# ocall[[paste("g",pn,sep="")]] <- newfun$newfun
## works only part time because it does strong type checking and don't force the assignment
## with incompatible types.
## So, we have to do it the long way !
switch(pn,
mu={ocall$gmu <- newfun$newfun
if(!is.null(ocall[["pgmu"]])) ocall$pgmu <- newfun$startval},
shape={ocall$gshape <- newfun$newfun
if(!is.null(ocall[["pgshape"]])) ocall$pgshape <- newfun$startval},
extra={ocall$gextra <- newfun$newfun
if(!is.null(ocall[["pgextra"]])) ocall$pgextra <- newfun$startval})
newpar[[pn]] <- newfun$newpar
}}
if(length(groups)==1) {
ocall$pgmix <- NULL
ocall$gmixture <- NULL }
else {
if(!is.null(ocall$gmixture)) {
newgmixture <- update.fun(ocall$gmixture,parm$mix,oldparm$mix,what$mix,funlist=FALSE)
ocall$gmixture <- newgmixture$newfun
ocall$pgmix <- newgmixture$startval
newpar$mix <- newgmixture$newpar
} else {
newgmixture <- update.fun(as.name("inv.glogit"),parm$mix,oldparm$mix,what$mix,funlist=FALSE)
ocall$gmixture <- newgmixture$newfun
ocall$pgmix <- newgmixture$startval
newpar$mix <- newgmixture$newpar
}}
if(!is.null(xnam <- ocall$expected)) {
xwhat <- numeric(0)
for(pn in pnames){
tmp <- what[[pn]]
if(any(lz <- (tmp<0))) {tmp[lz] <- tmp[lz]+min(xwhat[xwhat<0],0)}
if(any(gz <- (tmp>=0))) {tmp[gz] <- (newpar[[pn]]+max(xwhat[xwhat>0],0))*(tmp[gz]!=0)}
xwhat <- c(xwhat,tmp)}
newexpected <- update.fun(xnam,c(unlist(parm)),c(unlist(oldparm)),xwhat)
ocall$expected <- newexpected$newfun
}
for(ln in names(arg.list)) ocall[[ln]] <- arg.list[[ln]]
if(!is.null(eval(object$data)))
{
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
if(evaluate) eval(as.call(ocall),parent.frame()) else as.call(ocall)
}
post<-function(object,...) UseMethod("post")
post.moc<-function(object, ...)
{
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc\n")
if(!is.null(object$post.prob)) {return(invisible(object$post.prob))} else
{
if(!is.null(eval(object$data)))
{
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
parm<-split(object$coefficients,rep(c("mu","shape","extra","mix"),object$npar))
mix<-object$gmixture(parm$mix)
if(object$groups==1) cbind(1) else
{
if(object$joint) {
post1<-sapply(1:object$groups,function(ind)
object$density(as.matrix(eval(object$resp)),object$gmu[[ind]](parm$mu),
object$gshape[[ind]](parm$shape),object$gextra[[ind]](parm$extra)))*mix
} else
{post1<-sapply(1:object$groups,function(ind)
apply(object$density(as.matrix(eval(object$resp)),object$gmu[[ind]](parm$mu),
object$gshape[[ind]](parm$shape),object$gextra[[ind]](parm$extra)),
1,prod,na.rm=TRUE))*mix
}
dimnames(post1)<-list(NULL,paste("Group",1:object$groups,sep=""))
post1 <- post1/apply(post1,1,sum)
obj.name <- deparse(match.call()$object)
object$post.prob <- post1
# eval(substitute(assign(obj.name,object,pos=sys.frame())))
attr(post1,"moc.name") <- obj.name
invisible(post1)
}
}
}
fitted.moc<-function(object,...)
{
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc\n")
if(!is.null(eval(object$data))){
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2);
# on.exit(detach(2),add=TRUE)
}
n<-object$nsubject
dim1<-c(n,object$nvar,object$groups)
fit<-array(NA,dim=dim1)
for(ig in 1:object$groups) fit[,,ig]<-object$expected[[ig]](object$coefficients)
dimnames(fit)<-list(NULL,
ifelse(is.na(nchar(temp<-dimnames(eval(object$resp))[[2]])[1:dim1[2]]),
paste("V",1:dim1[2],sep=""),temp),paste("Group",1:dim1[3],sep=""))
attr(fit,"moc.name") <- deparse(match.call()$object,control=NULL)
invisible(fit)
}
residuals.moc<-function(object,...,type=c("deviance","response","mixture","gradient"),post.weight=TRUE,within=FALSE)
{
thiscall <- sys.call()
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc\n")
if(!is.null(eval(object$data))){
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
#
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
choices<-c("deviance","response","mixture","gradient")
type<-match.arg(type,choices)
n<-object$nsubject
nt<-object$nvar
ng<-object$groups
dim1<-c(n,nt,ng)
parm<-split(object$coefficients,rep(c("mu","shape","extra","mix"),object$npar))
wts<-eval(object$prior.weights)
wpost<-post.moc(object)
if (within) wpost<-t(t(wpost)/apply(wpost,2,mean))
y<-as.matrix(eval(object$resp))
res<-array(y,dim=dim1)-fitted(object)
if(type=="mixture"){
## Compute the empirical distribution
res <- npmle.gradient(object,gradient=FALSE,average=FALSE)
res <- array(res,append(dim(res),1,1))
dimnames(res) <- list(NULL,NULL,"mixture")
post.weight <- FALSE
} else if(type=="gradient") {
res <- npmle.gradient(object,gradient=TRUE,average=FALSE)
res <- array(res,append(dim(res),1,1))
dimnames(res) <- list(NULL,NULL,paste("Group",1:dim1[3],sep=""))
post.weight <- FALSE
} else {
for(ig in 1:object$groups)
{
m<-object$gmu[[ig]](parm$mu)
s<-object$gshape[[ig]](parm$shape)
extra<-object$gextra[[ig]](parm$extra)
switch(type,
response= res[,,ig]<-res[,,ig],
deviance= res[,,ig]<-
sqrt(2*wts*(log(object$density(y,y,s,extra)/object$density(y,m,s,extra))))*
sign(res[,,ig])
)
}
dimnames(res)<-list(NULL,
ifelse(is.na(nchar(temp<-dimnames(eval(object$resp))[[2]])[1:dim1[2]]),
paste("V",1:dim1[2],sep=""),temp),paste("Group",1:dim1[3],sep=""))
}
if(post.weight) res<-res*array(wpost[,rep(1:ng,rep(nt,ng))],dim=dim1)
class(res)<-c("residuals.moc","residuals")
attr(res,"type")<-type
attr(res,"post.weight")<-post.weight
attr(res,"within")<-within
attr(res,"moc.name") <- deparse(match.call()$object,control=NULL)
invisible(res)
}
print.moc<-function(x,digits=5,expand=TRUE,transpose=FALSE,...)
{
object<-x
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc\n")
if(!is.null(eval(object$data))){
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
cat("\n\t\t",object$groups,"mixtures MOC model\n\n\n")
cat("Response: ",deparse(object$resp,control=NULL),"\n\n")
if(object$joint) cat("joint ")
cat("Density: ")
cat(deparse(object$call$density,control=NULL),"\n")
if(expand) print(object$density,...)
if(!is.null(object$call$gmu)) {
cat("\nLocation: ")
cat(deparse(object$call$gmu,control=NULL),"( pgmu = ",deparse(object$call$pgmu,control=NULL)," )\n")
if(expand) print.listof(object$gmu,... )
}
if(!is.null(object$call$expected)) {
cat("\nExpectation: ")
cat(deparse(object$call$expected,control=NULL),"\n")
if(expand) print.listof(object$expected,... )
}
if(!is.null(object$call$gshape)) {
cat("\nShape: ")
cat(deparse(object$call$gshape,control=NULL),"( pgshape = ",deparse(object$call$pgshape,control=NULL)," )\n")
if(expand) print.listof(object$gshape,...)
}
if(!is.null(object$call$gextra)) {
cat("\nExtra parameter: ")
cat(deparse(object$call$gextra,control=NULL),"( pgextra = ",deparse(object$call$pgextra,control=NULL)," )\n")
if(expand) print.listof(object$gextra,...)
}
cat("\nMixture: ")
if(!is.null(object$call$gmixture)) {
cat(deparse(object$call$gmixture,control=NULL),"( pgmix = ",deparse(object$call$pgmix,control=NULL)," )\n")
} else {
cat("inv.glogit( pgmix = ",deparse(object$call$pgmix,control=NULL)," )\n") }
if(expand) print(object$gmixture,...)
cat("\n\n\t\t\tMaximum Likelihood Estimates\n\n")
cat(formatC(c("",""," Standard","Wald Chi-Sq:"),width=13),"\n")
cat(formatC(c("Parameter ","Estimate"," Error"," Param = 0"," Prob > Wald"),width=13),"\n")
object.coef<-split(object$coef,rep(c("mu","shape","extra","mix"),object$npar))
object.se<-split(sqrt(diag(object$cov)),rep(c("mu","shape","extra","mix"),object$npar))
nl<-object$npar[1];ns<-object$npar[2];nextra<-object$npar[3];nm<-object$npar[4]
if(nl>0)
{
cat("\nLocation:\n\n")
param<-attr(object$gmu,"parameters")
if (length(param) != nl) param<-" "
coeftable<-matrix(cbind(est<-object.coef$mu,se<-object.se$mu,w<-(est/se)^2,
(1-pchisq(w,1))),nl,4)
coeftable<-formatC(matrix(apply(coeftable,2,format,digits=digits,width=13),nl,4),width=13)
cat(paste(formatC(param,width=-13),apply(coeftable,1,paste,collapse=" "),collapse="\n"),"\n")
object$Location<-coeftable
}
if(ns>0)
{
cat("\nShape:\n\n")
param<-attr(object$gshape,"parameters")
if (length(param) != ns) param<-" "
coeftable<-matrix(cbind(est<-object.coef$shape,se<-object.se$shape,
w<-(est/se)^2,(1-pchisq(w,1))),ns,4)
coeftable<-formatC(matrix(apply(coeftable,2,format,digits=digits,width=13),ns,4),width=13)
cat(paste(formatC(param,width=-13),apply(coeftable,1,paste,collapse=" "),collapse="\n"),"\n")
object$Shape<-coeftable
}
if(nextra>0)
{
cat("\nExtra Parameters:\n\n")
param<-attr(object$gextra,"parameters")
if (length(param) != nextra) param<-" "
coeftable<-matrix(cbind(est<-object.coef$extra,
se<-object.se$extra,w<-(est/se)^2,
(1-pchisq(w,1))),nextra,4)
coeftable<-formatC(matrix(apply(coeftable,2,format,digits=digits,width=13),nextra,4),width=13)
cat(paste(formatC(param,width=-13),apply(coeftable,1,paste,collapse=" "),collapse="\n"),"\n")
object$Extra<-coeftable
}
if(nm>0)
{
cat("\nMixture:\n\n")
param<-attr(object$gmixture,"parameters")
if (length(param) != nm) param<-" "
coeftable<-matrix(cbind(est<-object.coef$mix,
se<-object.se$mix,
w<-(est/se)^2,(1-pchisq(w,1))),nm,4)
coeftable<-formatC(matrix(apply(coeftable,2,format,digits=digits,width=13),nm,4),width=13)
cat(paste(formatC(param,width=-13),apply(coeftable,1,paste,collapse=" "),collapse="\n"),"\n")
object$Mixture<-coeftable
cat("\nMean Mixture Probabilities:\n")
mmix<-apply(object$gmixture(est),2,weighted.mean,eval(object$prior.weights),na.rm=TRUE)
names(mmix)<-paste("Group",1:object$groups,sep="")
print(mmix,digits=5)
object$MixtProb<-mmix
}
modelfit<-cbind("-2*logLikelihood"=-2*object$loglik, AIC=object$AIC,AIC(object,k="BIC")[-1])
dimnames(modelfit)[[1]]<-" "
cat("\n")
print(modelfit)
object$ModelFit<-modelfit
coeftable<-apply(post.moc(object),2,weighted.mean,eval(object$prior.weights),na.rm=TRUE)
object$PostMixtProb<-coeftable
if(object$code>2) {cat("\n\nWARNING - CONVERGENCE NOT ACHIEVED - ERROR",object$code,"\n\n\n")}
if(object$groups>1) {
cat("\n\nMean Posterior Mixture Probabilities:\n")
print(coeftable,digits=5)}
cat("\n")
cat("\nPosterior mean predicted values:\n")
if(transpose) tmp <- t(object$fitted.mean) else tmp <- object$fitted.mean
print(tmp,digits=digits)
cat("\nPosterior mean observed values:\n")
if(transpose) tmp <- t(object$observed.mean) else tmp <- object$observed.mean
print(tmp,digits=digits)
cat("\n")
invisible(object)
}
npmle.gradient <- function(object,parm=object$coef,gradient=TRUE,average=FALSE)
{
new.parm <- parm
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc\n")
if(length(new.parm)!=sum(object$npar)) stop(paste("\n\tYou should supply",sum(object$npar),"parameter values in parm\n"))
if(!is.null(eval(object$data)))
{
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
parm <- split(new.parm,rep(c("mu","shape","extra","mix"),object$npar))
pmix <- object$gmixture(parm$mix)
dens <- sapply(1:object$groups, function(ind) apply(as.matrix(object$density(eval(object$resp),
object$gmu[[ind]](parm$mu), object$gshape[[ind]](parm$shape),
object$gextra[[ind]](parm$extra))), 1, prod, na.rm = TRUE))
mix.dens <- apply(dens*pmix,1,sum)
if(!gradient){
## Compute the empirical distribution
tmp <- as.matrix(apply(format(eval(object$resp),digits=20),1,paste,collapse=""))
tmp.ind <- by(1:dim(tmp)[1],factor(tmp),I)
dens.emp <- matrix(NA,dim(tmp)[1])
remove("tmp")
wts <- eval(object$prior.weights)
for(i in 1:length(tmp.ind)) dens.emp[tmp.ind[[i]]] <- sum(wts[tmp.ind[[i]]])/sum(wts)
val <-as.matrix(dens.emp/mix.dens-1)
dimnames(val) <- list(NULL,"empirical")
} else {
val <- as.matrix(dens/mix.dens-1)
dimnames(val) <- list(NULL,paste("Group",1:object$groups,sep=""))
}
if(average) val <- apply(val,2,mean,na.rm=TRUE)
val
}
confint.moc <- function(object,parm=list(),level=0.95,profiling=c("none","simple","complete"),...)
{
opt.args <- as.list(match.call())
estimate <- parm
l <- length(estimate)
if(l==0) {
estimate <- lapply(paste("~p",1:sum(object$npar),sep=""),as.formula)
l <- sum(object$npar)
}
profiling <- match.arg(profiling,c("none","simple","complete"))
grad <- lapply(estimate,function(expr) deriv(expr,paste("p",1:sum(object$npar),sep="")))
parm <- as.list(structure(object$coef,names=paste("p",1:sum(object$npar),sep="")))
val.grad <- lapply(grad,function(gr) with(parm,eval(gr)))
val <- unlist(val.grad)
grad <- structure(unlist(lapply(val.grad,attr,"gradient")),dim=c(sum(object$npar),l))
cova <- as.matrix(t(grad)%*%object$cov%*%grad)
table.univ <- data.frame(estimate=val,st.dev=sqrt(diag(cova)),"Lower.ACI"=val+qnorm(0.5-level/2)*sqrt(diag(cova)),
"Upper.ACI"=val+qnorm(0.5+level/2)*sqrt(diag(cova)),row.names=
sapply(estimate,deparse,control=NULL,width.cutoff=500))
if(length(estimate)>1){
cov.jointcond <- sapply(1:length(diag(cova)),function(i) cova[i,i]-cova[i,-i]%*%solve(cova[-i,-i])%*%cova[-i,i])
table.joint <- data.frame(estimate=val,st.dev=sqrt( cov.jointcond),"Lower.ACI"=val+qnorm(0.5-level/2)*sqrt( cov.jointcond),
"Upper.ACI"=val+qnorm(0.5+level/2)*sqrt( cov.jointcond),row.names=sapply(estimate,deparse,control=NULL,width.cutoff=500))
} else {table.joint <- "NOT APPLICABLE"}
if(profiling=="simple" | profiling=="complete") {
offscal <- eval(opt.args[[match("offscal",names(opt.args))]])
if (is.null(offscal)) {
offscal <- c(-5,-4,seq(-3,3,0.5),4,5)}
pparm <- t(object$coef + sqrt(c(diag(object$cov)))%o%offscal)
dimnames(pparm) <- list(NULL, paste("p",1:sum(object$npar),sep=""))
devilike <- list()
profci <- matrix(NA,sum(object$npar),3)
profci[,1] <- object$coef
dimnames(profci) <- list(paste("p",1:sum(object$npar),sep=""),c("estimate","Lower.CI","Upper.CI"))
if(profiling == "simple"){
df <- 1 ## A better approximation probably requires adjustment of this
llkfun <- function(object,p,fix) {
llk <- loglike.moc(object,parm=p,evaluate=FALSE)[,2]
c(llk+2*object$loglike,p)}
} else if(profiling == "complete"){
df <- 1 ## Look forward for adjustment of this one too
iterlim <- eval(opt.args[[match("iterlim",names(opt.args))]])
if (is.null(iterlim)) {iterlim <- 15}
llkfun <- function(object,p,fix) {
llk <- try(update.moc(object,parm=p,what=fix,
evaluate=TRUE,iterlim = iterlim,print.level=0))
pmiss <- which(fix!=0)
llk <- c(llk$loglikelihood,append(llk$coefficients,p[pmiss],pmiss-1))
c(-2*llk[1]+2*object$loglikelihood,llk[-1])
}
cat("\n Wait for complete profiling to be done: be aware, this can take a long time!\n")
mocmessf <- textConnection("mocmess","w",local=TRUE)
sink(mocmessf)
on.exit({sink();close(mocmessf)},add=TRUE)
}
for(i in 1:sum(object$npar)) {
devilike[[paste("p",i,sep="")]] <- t(sapply(pparm[,i],function(vp)
llkfun(object,replace(object$coef,i,vp),replace(rep(0,length(object$coef)),i,-1)) ))
dimnames(devilike[[i]]) <- list(NULL,c("deviance",paste("p",1:sum(object$npar),sep="")))
tmp <- approx(devilike[[i]][,i+1],pchisq(devilike[[i]][,1],df),n=75,ties="ordered",rule=2)
profci[i,2] <- approx(c(1,tmp$y[tmp$x<=object$coef[i]]),
c(-Inf,tmp$x[tmp$x<=object$coef[i]]), xout=level,ties="mean",
rule=1,yleft=-Inf,yright=Inf)$y
profci[i,3] <- approx(c(tmp$y[tmp$x>=object$coef[i]],1),
c(tmp$x[tmp$x>=object$coef[i]],Inf),xout=level,ties="mean",
rule=1,yleft=-Inf,yright=Inf)$y
}
} else {profci <- "NOT REQUESTED"; devilike <- "NOT REQUESTED"}
ellip <- function(p) {(val-p)%*%solve(cova)%*%(val-p)}
ellip.env <- new.env()
assign("val",val,envir=ellip.env)
assign("cova",cova,envir=ellip.env)
environment(ellip) <- ellip.env
structure(list(devilike,ellip,table.univ, table.joint,profci),
names=c(paste(profiling,"likelihood profiling"), "ellip",
paste("Univariate",100*level,"% CI"),paste("Joint conditional",100*level,"% CI"),
paste(profiling,"likelihood rejection",100*level,"% CI")),
moc.names=paste(substitute(object)))
}
AIC.moc <- function(object,...,k=2)
{
mlist<- list(object,...)
if(!all(sapply(mlist,inherits,"moc"))) stop("\n\tAll objects must be of class moc\n")
ml<-length(mlist)
cnames<-as.character(match.call()[-1])
names(mlist)<- cnames[1:ml]
nobslist<-sapply(mlist,function(tmp) tmp$nobs)
nglist<-sapply(mlist,function(tmp) tmp$groups)
if((k==0) & ((max(nglist)!=min(nglist)) | (max(nobslist)!=min(nobslist))))
warning("log Likelihood should only be used to compare nested models on the same observations with the same number of mixture.\nTry using AIC or BIC or ICL-BIC instead.\n") else
{if((k!="BIC") & (max(nobslist)!=min(nobslist)))
warning("AIC like statistics should not be used to compare models with differing number of observations, use BIC or ICL-BIC instead.\n")}
if(k=="BIC")
val<-as.data.frame(t(sapply(mlist,function(tmp){
#attach(eval(tmp$data),name=deparse(tmp$data,control=NULL),pos=2)
#on.exit(detach(2),add=TRUE)
bic<-tmp$BIC
po<-post.moc(tmp); entropy<--sum(ifelse(po==0,0,eval(tmp$prior.weight)*po*log(po)))
c(-2*tmp$loglikelihood,bic,entropy,bic+2*entropy,tmp$df)}))) else
val<-as.data.frame(t(sapply(mlist,function(tmp)
c(-2*tmp$loglikelihood+k*sum(tmp$npar),tmp$df))))
names(val)<-c(switch(as.character(k),"0"="-2*logLik","2"=c("AIC"),
BIC=c("-2*logLik","BIC","Entropy","ICL-BIC"),
"generalized AIC"),"Df")
row.names(val)<-cnames[1:ml]
val
}
entropy <- function(object,...) UseMethod("entropy")
entropy.default <- function(object,...)
{
obj2 <- as.matrix(object)
if(!all((obj2 >= 0) & (obj2 <= 1)) | any(abs(apply(obj2,1,sum)-1) > .Machine$double.eps^0.5))
stop("The probabilities must lies in [0,1] ans sum to 1 ")
en <- apply(obj2,1,function(pro) -sum(ifelse(pro==0,0,pro*log(pro))))
en <- cbind(en,en/log(dim(obj2)[2]))
dimnames(en) <- list(dimnames(obj2)[[1]],c("entropy","std.entropy"))
return(en)
}
entropy.moc <- function(object,...)
{
mlist<- list(object,...)
if(!all(sapply(mlist,inherits,"moc"))) stop("\n\tAll objects must be of class moc\n")
ml<-length(mlist)
cnames<-as.character(match.call()[-1])
names(mlist)<- cnames[1:ml]
val<-as.data.frame(t(sapply(mlist,function(tmp){
if(!is.null(eval(tmp$data)))
{
mocData <- list2env(eval(tmp$data))
environment(tmp$density) <- mocData
environment(tmp$gmixture) <- mocData
for (i in 1:tmp$groups) {
environment(tmp$gmu[[i]]) <- mocData
environment(tmp$gshape[[i]]) <- mocData
environment(tmp$gextra[[i]]) <- mocData
environment(tmp$expected[[i]]) <- mocData
}
}
# attach(eval(tmp$data),name=deparse(tmp$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
parm<-split(tmp$coef,rep(c("mu","shape","extra","mix"),tmp$npar))
pri <- tmp$gmixture(parm$mix)
po<-post.moc(tmp)
pri.entropy<--sum(ifelse(pri==0,0,eval(tmp$prior.weight)*pri*log(pri)))
post.entropy<--sum(ifelse(po==0,0,eval(tmp$prior.weight)*po*log(po)))
c(tmp$groups,pri.entropy,post.entropy,
pri.entropy/log(tmp$groups)/sum(eval(tmp$prior.weight)),post.entropy/log(tmp$groups)/sum(eval(tmp$prior.weight)),
1-post.entropy/pri.entropy)})))
names(val)<-c("Groups","Total Prior Entropy","Total Posterior Entropy",
"Mean Prior Standardized Entropy","Mean Posterior Standardized Entropy",
"% Reduction")
row.names(val)<-cnames[1:ml]
val
}
logLik.moc <- function(object,...)
{
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc\n")
val <- object$loglikelihood
attr(val,"df") <- object$df
attr(val,"nobs")<-object$nvar*object$nsubject
class(val) <- "logLik"
attr(val,"moc.name") <- deparse(match.call()$object,control=NULL)
val
}
loglike.moc <- function(object,parm=object$coef, evaluate=FALSE)
{
new.parm <- parm
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc.\n")
if(length(new.parm)!=sum(object$npar)) stop(paste("\n\tYou should supply",sum(object$npar),"parameter values in parm\n"))
parm<-split(new.parm,rep(c("mu","shape","extra","mix"),object$npar))
if(!is.null(eval(object$data))){
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
pmix <- object$gmixture(parm$mix)
dens <- sapply(1:object$groups, function(ind) apply(as.matrix(object$density(eval(object$resp),
object$gmu[[ind]](parm$mu), object$gshape[[ind]](parm$shape),
object$gextra[[ind]](parm$extra))), 1, prod, na.rm = TRUE))
loglike <- .C("mixloglike", as.double(dens), as.double(pmix),
as.double(eval(object$prior.weights)), as.integer(object$nsubject), as.integer(object$groups),
mll = double(1),NAOK=TRUE,PACKAGE="moc")$mll
loglike <- matrix(c(-2*object$loglikelihood,2*loglike),nrow=1,ncol=2,dimnames=list("-2*loglike",c("original","new.parm")))
new.moc <- NULL
if(evaluate)
{ new.call <- object$call
new.call$pgmu <- parm$mu
new.call$pgshape <- parm$shape
new.call$pgextra <- parm$extra
new.call$pgmix <- parm$mix
new.call$print.level <- 0
new.moc <- eval(new.call,envir=parent.frame())
}
val <- cbind(loglike,"new.estimate"=-2*new.moc$loglikelihood)
attr(val,"moc.name") <- deparse(match.call()$object,control=NULL)
attr(val,"parameters") <- list("original"=object$coef,"new.parm"=new.parm,"new.estimate"=new.moc$coef)
val
}
obsfit.moc<-function(object,along=list(cons=rep(1,object$nsubject)),FUN=function(x) x)
{
if(!inherits(object,"moc")) stop("\n\tObject must be of class moc\n")
if(!is.null(eval(object$data))){
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
along.name <- substitute(along)
if(!is.list(along)) eval(parse(text=paste("along <-list(",substitute(along),"=along)")))
n<-object$nsubject
nt<-object$nvar
ng<-object$groups
parm<-split(object$coef,rep(c("mu","shape","extra","mix"),object$npar))
wts<-eval(object$prior.weight)
post.obj<-post.moc(object)*wts
mpost<-by(post.obj,along,sapply,mean,na.rm=TRUE)
wts<-by(wts,along,mean)
tmp <- object$gmixture(parm$mix)
names(tmp) <- paste("Group",1:object$groups,sep="")
dimnames(tmp) <- list(NULL,paste("Group",1:object$groups,sep=""))
gmix.mean <-by(tmp,along,sapply,mean)
tmp<-matrix(NA,n,nt*ng)
for (ig in 1:object$groups) tmp[,(1:nt)+(ig-1)*nt]<-FUN(object$expected[[ig]](object$coef))
fitted.mean<-by(tmp*array(apply(post.obj,2,rep,nt),c(n,nt*ng)), along,sapply,mean,na.rm=TRUE)
mpost.fitted<-by(ifelse(is.na(tmp),NA,1)*array(apply(post.obj,2,rep,nt),c(n,nt*ng)),
along,sapply,mean,na.rm=TRUE)
tmp<-FUN(array(eval(object$resp),c(n,nt*ng)))
observed.mean<-by(tmp*array(apply(post.obj,2,rep,nt),c(n,nt*ng)),along,sapply,mean,na.rm=TRUE)
mpost.observed<-by(ifelse(is.na(tmp),NA,1)*array(apply(post.obj,2,rep,nt),c(n,nt*ng)),
along,sapply,mean,na.rm=TRUE)
nlist<-dim(mpost)
fitmean <- list()
obsmean <- list()
for (i in 1:prod(nlist))
{
if(!is.null(fitted.mean[[i]])){
fitmean[[i]] <- matrix(t(array(fitted.mean[[i]]/mpost.fitted[[i]],c(nt,ng))),ng,nt,
dimnames=list(paste("Group",1:ng,sep=""),
ifelse(is.na(nchar(temp<-dimnames(eval(object$resp))[[2]])[1:nt]) ,
paste("V",1:nt,sep=""),temp)))
}
if(!is.null(observed.mean[[i]]))
{
obsmean[[i]] <- matrix(t(array(observed.mean[[i]]/mpost.observed[[i]],c(nt,ng))),ng,nt,
dimnames=list(paste("Group",1:ng,sep=""),
ifelse(is.na(nchar(temp<-dimnames(eval(object$resp))[[2]])[1:nt]) ,
paste("V",1:nt,sep=""),temp)))
}
gmix.mean[[i]]<-gmix.mean[[i]]/wts[[i]]
}
attributes(fitmean) <- attributes(fitted.mean)
attributes(obsmean) <- attributes(observed.mean)
val<-list("Mean Prior Probabilities"=gmix.mean,
"Mean function Expected Values"=fitmean,
"Mean function Observed Values"=obsmean,
"Mean Posterior Probabilities"=mpost)
structure(val,moc.name=deparse(match.call()$object,control=NULL),FUN=substitute(FUN),along=deparse(along.name,control=NULL))
}
plot.moc<-function(x,against=1:x$nvar,main="",xlab="",ylab="",prob.legend=TRUE,scale=FALSE,group.colors=rainbow(x$groups),...)
{
if(main == "") main <- deparse(match.call()$x)
if(!inherits(x,"moc")) stop("Not an object of class moc")
if(!is.null(eval(x$data))){
mocData <- list2env(eval(x$data))
environment(x$density) <- mocData
environment(x$gmixture) <- mocData
for (i in 1:x$groups) {
environment(x$gmu[[i]]) <- mocData
environment(x$gshape[[i]]) <- mocData
environment(x$gextra[[i]]) <- mocData
environment(x$expected[[i]]) <- mocData
}
# attach(eval(x$data),name=deparse(x$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
if(prob.legend) {
oldpar<-par("oma"=c(0,0,0,8),"las"=1)
legend.text<-paste("Group",1:x$groups,"=",
formatC(apply(x$gmixture(x$coef[(sum(x$npar[1:3])+1):sum(x$npar[1:4])]),
2,mean),digits=4))
on.exit(par(oldpar),add=TRUE)
}
if(dim(as.matrix(against))[2]==1) {w<-cbind(against)} else {w<-cbind(against,against)}
if(scale) {
center<-apply(eval(x$resp),2,function(v)
weighted.mean(v,eval(x$prior.weights),na.rm=TRUE))
scale<-sqrt(apply(eval(x$resp),2,function(v)
mean(eval(x$prior.weights)*v[nav<-!is.na(v)]^2)/
mean(eval(x$prior.weights)[nav]))-center^2)
} else {center<-rep(0,x$nvar);scale<-rep(1,x$nvar)}
matplot(w,cbind((t(x$observed.mean)-center)/scale,(t(x$fitted.mean)-center)/scale),
type="n",main=main,xlab=xlab,ylab=ylab,col=group.colors,...)
matpoints(against,(t(x$observed.mean)-center)/scale,col=group.colors,...)
matlines(against,(t(x$fitted.mean)-center)/scale,type="o",pch=20,cex=.75,col=group.colors,...)
mocname <- deparse(match.call()$x)
if(prob.legend) { mtext(legend.text,side=4,outer=TRUE,at=((1:x$groups)+3)/(x$groups+6),col=group.colors) }
invisible(list(moc.name = mocname,against=against,fitted=x$fitted,observed=x$observed,center=center,scale=scale,graphic.par=par()))
}
density.moc <- function(x,var=NULL,along=NULL,plot=c("none","pp-plot","density","pq-plot"),type="l",...)
{
object <- x
if(is.null(var)) stop("argument var should be supplied, there is no default!")
if(!all(var%in%(1:object$nvar))) stop(paste("var should be given as an integer list between 1 and nvar =",object$nvar))
if(is.null(along)) {
along <- factor(rep(TRUE,object$nsubject))
subtitle <- NULL
} else {
subtitle <- paste(",",deparse(substitute(along),width.cutoff=20,control=NULL),"=",collapse=" ")
along <- factor(along)
}
plot <- match.arg(paste(plot),c("none","pp-plot","density","pq-plot"))
if(!is.null(eval(object$data)))
{
mocData <- list2env(eval(object$data))
environment(object$density) <- mocData
environment(object$gmixture) <- mocData
for (i in 1:object$groups) {
environment(object$gmu[[i]]) <- mocData
environment(object$gshape[[i]]) <- mocData
environment(object$gextra[[i]]) <- mocData
environment(object$expected[[i]]) <- mocData
}
# attach(eval(object$data),name=deparse(object$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
parm<-split(object$coefficients,rep(c("mu","shape","extra","mix"),object$npar))
mix <- object$gmixture(parm$mix)
y <- array(NA,dim(eval(object$resp)))
y[,var] <- eval(object$resp)[,var]
dens.pred <- apply(sapply(1:object$groups,function(ind) apply(as.matrix(object$density(y,object$gmu[[ind]](parm$mu),
object$gshape[[ind]](parm$shape),object$gextra[[ind]](parm$extra))),1,prod,na.rm=TRUE))*mix,1,sum)
vname <- dimnames(eval(object$resp))[[2]][var]
if(is.null(vname)) vname <- c(paste("V",var,sep=""))
remove("y")
val <- data.frame(dens.pred,eval(object$resp)[, var])
names(val) <- c("density",vname)
if(plot %in% c("density","pp-plot","pq-plot")){
npcol <- nlevels(along)-("FALSE" %in% levels(along))
oldpar<-par(mfrow=c(length(var),npcol));on.exit(par(oldpar),add=TRUE)
for(i in 1:length(var)) {
for(lname in levels(along)[which(levels(along)!="FALSE")])
{
select <- which(along==lname)
ind <- order(val[select,i+1],val[select,1],na.last=NA)
tmp <- val[select,c(i+1,1)][ind,]
if(nlevels(along)<=1) subname <- NULL else subname <- lname
if(plot == "density") plot(tmp,type=type,xlab=paste(vname[i],paste(subtitle,subname)),ylab="density",...)
if(plot %in% c("pp-plot","pq-plot")) {
tmp2 <- ecdf(tmp[,1])
tmp <- approx(tmp[,1],tmp[,2],rule=2,method="linear",yleft=0,yright=0,
ties="ordered",n=max(75,length(val[,1])))
if(plot=="pp-plot"){
plot(tmp2(tmp$x),cumsum(tmp$y)/sum(tmp$y),
type=type,xlab="Empirical CDF",ylab="Predicted CDF",xlim=c(0,1),ylim=c(0,1),
sub=paste(vname[i],paste(subtitle,subname)),...)
lines(c(0,1),c(0,1))
} else {
plot(tmp$x,cumsum(tmp$y)/sum(tmp$y),
type=type,xlab=paste(vname[i],paste(subtitle,subname)),
ylab="Predicted CDF",ylim=c(0,1),...)
lines( tmp$x,tmp2(tmp$x),type="s",lty=2)
}}
}}} else {val}
}
profiles.postCI <- function(object,data=NULL,level=0.95,interpolate=TRUE)
{
if(!inherits(object,"moc")) stop("\n\tNot an object of class moc\n")
CI <- level
if(length(CI)==1) CI <- c((1-CI)/2,(1+CI)/2)
if(!all((CI>=0) & (CI<=1))) stop("\nlevel should lie in [0,1]\n")
if(is.null(data)) data <- eval(object$resp)
if(dim(as.matrix(data))[1] != object$nsubject) stop(paste("\ndata should have",object$nsubject,"subjects\n"))
data <- data.frame(data)
vname <- dimnames(data)[[2]]
mpost <- post.moc(object)*eval(object$prior.weights)
ordind <- sapply(data,order)
mpost.na <- matrix(sapply(data,function(x) apply(as.matrix(na.omit(data.frame(x,mpost))[,-1]),2,sum)),object$group,dim(data)[2])
if(interpolate) method="linear" else method="constant"
val <- lapply(1:object$groups,function(gr)
sapply(1:dim(ordind)[2],function(colid)
approx( cumsum(mpost[ordind[,colid],gr])/mpost.na[gr,colid],data[ordind[,colid],colid],
ties="ordered",f=0,method=method,rule=2,xout=CI)$y)
)
names(val) <- paste("Group",1:object$groups,sep="")
for(i in 1:length(val)) {
dimnames(val[[i]]) <- list(c(paste("Lower_",CI[1],sep=""),paste("Upper_",CI[2],sep="")),vname)
val[[i]][1,is.na(val[[i]][1,])] <- -Inf
val[[i]][2,is.na(val[[i]][2,])] <- Inf
}
attr(val,"CI") <- CI
attr(val,"moc.name") <- paste(substitute(object))
val
}
profilesplot<-function(x,...) UseMethod("profilesplot")
profilesplot.moc<-function(x,against=1:x$nvar,main=NULL,xlab="",ylab="",col.legend=TRUE,scale=FALSE,group.colors=rainbow(x$groups),type="subject",...)
{
if(!inherits(x,"moc")) stop("Not an object of class moc")
if(!is.null(eval(x$data)))
{
mocData <- list2env(eval(x$data))
environment(x$density) <- mocData
environment(x$gmixture) <- mocData
for (i in 1:x$groups) {
environment(x$gmu[[i]]) <- mocData
environment(x$gshape[[i]]) <- mocData
environment(x$gextra[[i]]) <- mocData
environment(x$expected[[i]]) <- mocData
}
# attach(eval(x$data),name=deparse(x$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
type <- match.arg(type,c("subject","variable","posterior"))
if (is.null(main)) main <- paste(paste(x$resp,collapse=" "),type,"profiles")
group.colors<-col2rgb(group.colors)/256
if(col.legend) {
oldpar<-par("oma"=c(0,0,0,6),"las"=1);on.exit(par(oldpar),add=TRUE)
legend.text<-paste("Group",1:x$groups)
}
if(scale) {
center<-apply(eval(x$resp),2,mean,na.rm=TRUE)
scale<-apply(eval(x$resp),2,sd,na.rm=TRUE)
} else {center<-rep(0,x$nvar);scale<-rep(1,x$nvar)}
group.rgb<-t(apply(post.moc(x),1,function(y) (group.colors%*%y)))
if(type=="subject") {
matplot((against),(t(eval(x$resp))-center)/scale,type="o",pch=20,cex=0.75,
main=main,xlab=xlab,ylab=ylab,col=rgb(group.rgb[,1],group.rgb[,2],group.rgb[,3]))
} else
{ if(type=="variable") pairs(eval(x$resp),col=rgb(group.rgb[,1],group.rgb[,2],group.rgb[,3]),upper.panel=NULL,main=main)
else if(type=="posterior") pairs(post.moc(x),col=rgb(group.rgb[,1],group.rgb[,2],group.rgb[,3]),upper.panel=NULL,main=main) }
if(col.legend) { mtext(legend.text,side=4,outer=TRUE,at=((1:x$groups)+3)/(x$groups+6),
col=rgb(group.colors[1,],group.colors[2,],group.colors[3,])) }
mocname <- deparse(match.call()$x)
invisible(list("moc.name" = mocname,against=substitute(against),col.legend=col.legend,scale=scale,group.colors=group.colors,
type=type,graphic.par=par()))
}
entropyplot <- function(x,...) UseMethod("entropyplot")
entropyplot.moc <- function(x,main=NULL,std=TRUE,lwd=1.5,col=c("red3","green3","gray95"),shade.gr.col=gray(1-0.5*(0:(x$groups-1))/(x$groups-1)),legend=TRUE,...)
{
if(!inherits(x,"moc")) stop("Not an object of class moc")
if(x$groups==1) stop("Sorry, but there is no entropy for a single group!\n")
if(!is.null(eval(x$data)))
{
mocData <- list2env(eval(x$data))
environment(x$density) <- mocData
environment(x$gmixture) <- mocData
for (i in 1:x$groups) {
environment(x$gmu[[i]]) <- mocData
environment(x$gshape[[i]]) <- mocData
environment(x$gextra[[i]]) <- mocData
environment(x$expected[[i]]) <- mocData
}
# attach(eval(x$data),name=deparse(x$data,control=NULL),pos=2)
# on.exit(detach(2),add=TRUE)
}
parm<-split(x$coef,rep(c("mu","shape","extra","mix"),x$npar))
prior.entropy <- entropy(x$gmixture(parm$mix))
posterior.entropy <- entropy(post.moc(x))
max.ent <- ifelse(std,1,log(x$groups))
if(std) {col.ind <- 2} else { col.ind <- 1}
order.pripost <- order(prior.entropy[,col.ind],posterior.entropy[,col.ind])
n <- length(order.pripost)
moc.name <- deparse(match.call()$x)
if (is.null(main)) main <- paste("Prior and posterior",ifelse(std,"standardized",""),"entropy for",moc.name)
plot(c(0,1),c(0,max.ent),xaxt="n",xlim=c(0,1),ylim=c(0,max.ent),type="n",main=main,xlab="",ylab="")
if(!is.null(shade.gr.col)) {
shade.gr.col<- mix.colors.moc(x,shade.gr.col)
} else { shade.gr.col <- rep(col[3],n) }
for(i in 0:(n-1)) polygon(cbind((i:(i + 1))/(n - 1), ((i + 1):i)/(n - 1)), cbind(prior.entropy[order.pripost[(i+1):(i+2)],
col.ind], posterior.entropy[order.pripost[(i+2):(i+1)], col.ind]),
col=shade.gr.col[order.pripost[(i+1)]],border=NA)
lines((0:(n-1))/(n-1),prior.entropy[order.pripost,col.ind],col=col[1],lwd=lwd)
lines(((n-1):0)/(n-1),posterior.entropy[order.pripost[n:1],col.ind],col=col[2],lwd=lwd)
if(legend) legend(1,0,c("Prior","Posterior"),lwd=lwd,col=col[1:2],ncol=2,xjust=1,bty="n")
}
plot.residuals.moc<-function(x,against="Index",groups=1:dim(x)[3],sunflower=FALSE,group.colors=NULL,...)
{
if(sunflower) thisplot<-sunflowerplot else thisplot<-plot
if(!inherits(x,"residuals.moc"))
stop("Not an object of class residuals.moc !")
if (!all(groups %in% (1:dim(x)[3])))
stop("You requested residuals for non-existing groups !")
if(is.null(group.colors)) group.colors=rainbow(eval(as.name(attr(x,"moc.name")))$groups)
group.rgb <- mix.colors.moc(eval(as.name((attr(x,"moc.name")))),group.colors=group.colors)
dim1<-dim(x)
again<-substitute(against)
oldpar<-par(mfrow=c(length(groups),1),oma=c(0,0,2,0));on.exit(par(oldpar),add=TRUE)
vname<-deparse(substitute(against),control=NULL)
if(again=="Subject") against<-c(rep(1:dim1[1],dim1[2]))
if(again=="Observation") against<-factor(c(rep(1:dim1[2],rep(dim1[1],dim1[2]))))
if(again=="Index") against<-1:(dim1[1]*dim1[2])
tmp <- function(i)
{
z<-na.omit(data.frame(group.rgb,c(against),c(x[,,i])))
tmp.rgb <- as.vector(z[,1])
z <- z[,-1]
dimnames(z)<-list(dimnames(z)[[1]],c(vname,attr(x,"type")))
thisplot(z,main=ifelse(attr(x,"type")=="mixture","All groups",paste("Group",i)),col=tmp.rgb,...)
if(again=="Index") abline(v=(1:dim1[2])*dim1[1]+0.5)
}
res.apply<-sapply(groups,tmp)
mtext(paste("MOC ",attr(x,"type")," residuals of",attr(x,"moc.name")),side=3,outer=TRUE,font=2)
mocname <- deparse(match.call()$x,control = NULL)
attr(res.apply,"moc.name") <- mocname
invisible(list(moc.name = mocname ,against=deparse(again,control=NULL),groups=deparse(substitute(groups),control=NULL),
sunflower=sunflower,group.colors=group.colors,graphic.par=par()))
}
coef.moc <- function(object,split=FALSE,...)
{
if(split) return(split(object$coef,rep(c("mu","shape","extra","mixture"),object$npar)))
else return(object$coef)
}
## Generalized logit and inverse logit with respect to a reference group
inv.glogit<-function(gmix,ref=1) {rbind(append(exp(gmix),1,ref-1))/(1+sum(exp(gmix)))}
glogit<-function(p,ref=1) {log(p[-ref]/p[ref])}
## Mix group colors according to posterior mixture probabilities
mix.colors.moc<-function(object,group.colors=rainbow(object$groups))
{
if(!inherits(object,"moc")) stop("Not an object of class moc")
if(length(group.colors)!=object$groups)
stop("Oooups ! Wrong number of colors: should be the same as number of groups.")
group.rgb<-t(apply(post(object),1,function(y) ((col2rgb(group.colors)/256)%*%y)))
invisible(rgb(group.rgb[,1],group.rgb[,2],group.rgb[,3]))
}
.onAttach <- function(libname,pkgname) {
utils.path <- system.file("Utils",package=pkgname)
packageStartupMessage("\n",paste(readLines(system.file("DESCRIPTION",package=pkgname)),collapse="\n"),
"\n",appendLF = TRUE)
packageStartupMessage(" Supplementary utility functions can be found in directory:\n\t",
utils.path,": { ", paste(dir(utils.path, pattern="*.R$"),collapse=", ")," }\n")
packageStartupMessage(" Extended examples can be found in ",system.file("Examples",package=pkgname))
packageStartupMessage("\n Directory ",system.file("Sweave",package=pkgname),
" contains files\n\t which help using Sweave to make reports of moc models.\n")
packageStartupMessage(" A GUI is also available in ", system.file("GUI","moc-gui.R",package=pkgname))
packageStartupMessage("\n\n You must source those files to use their functionalities.")
packageStartupMessage("\n See the specific files for documentations.\n\n")
}
mocUtils <- function(filename)
{
if(missing(filename)) return(dir(system.file("Utils",package="moc"),pattern="*.[Rr]$"))
name <-paste(filename,sep="")
if((file <- system.file("Utils",filename,package="moc")) != ""){
assign(name,new.env())
source(file,local=eval(parse(text=name)))
# attach(eval(parse(text=name)),name=name)
cat("\n",name," returned!\n")
eval(parse(text=name))
}
cat("\n Did not returned anything ?\n")
}
.onUnload <- function(libpath) library.dynam.unload("moc", libpath)
|
47a215d5fc8222b7c726a9b5fba1b93223feab4d
|
d746fef241f9a0e06ae48cc3b1fe72693c43d808
|
/ark_87287/d7jc7h/d7jc7h-013/rotated.r
|
70d0fcb7cbd95373869c270511d1f9e4aa44da19
|
[
"MIT"
] |
permissive
|
ucd-library/wine-price-extraction
|
5abed5054a6e7704dcb401d728c1be2f53e05d78
|
c346e48b5cda8377335b66e4a1f57c013aa06f1f
|
refs/heads/master
| 2021-07-06T18:24:48.311848
| 2020-10-07T01:58:32
| 2020-10-07T01:58:32
| 144,317,559
| 5
| 0
| null | 2019-10-11T18:34:32
| 2018-08-10T18:00:02
|
JavaScript
|
UTF-8
|
R
| false
| false
| 195
|
r
|
rotated.r
|
r=0.21
https://sandbox.dams.library.ucdavis.edu/fcrepo/rest/collection/sherry-lehmann/catalogs/d7jc7h/media/images/d7jc7h-013/svc:tesseract/full/full/0.21/default.jpg Accept:application/hocr+xml
|
7ed3f95070be28178a4b1dceabc82cf3cc80093f
|
7e4fd49824b4ef4922fb024a227dd704ed78dfb2
|
/cachematrix.R
|
844f73c579d206c2895dbfcfa112ae37580cd721
|
[] |
no_license
|
shprite/coursera
|
37769906b67486e62fd6eb964141913738066301
|
f79bc8c8fe74ced5f988dc766a02efd9b857df71
|
refs/heads/master
| 2021-05-12T10:11:38.198669
| 2018-01-13T15:27:06
| 2018-01-13T15:27:06
| 117,348,903
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,142
|
r
|
cachematrix.R
|
## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
#this function initialises the matrix and the variable to store the inverse of the matrix - inv
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinv <- function(solve) inv <<- solve
getinv <- function() inv
list(set = set, get = get,
setinv = setinv,
getinv = getinv)
}
## Write a short comment describing this function
# this function calculates the inverse of the matrix using the inbuilt solve function in r. first it looks to see whether the inverse has been calculated and stored already. if it hasn't been calculated, it then calculates the inverse of the matrix and then stores is it in the cache where it can be retrieved from
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv <- x$getinv()
if(!is.null(inv)) {
message("getting cached data")
return(inv)
}
data<-x$get()
inv <- solve(data, ...)
x$setinv(inv)
inv
}
|
e530a6f63aa84e54dfae3440398b09b0d6fa3850
|
d0a994376c57bf849f2229b6afdc3b6c515e30d5
|
/pollutantmean.R
|
086ee3101e90c68aaec86b0933a512790aa31a4b
|
[] |
no_license
|
vttoonses/R-Week-2
|
19490c2b49903147adc634f3f977679c3df00af8
|
45c3e0212ce3580cea1b9e4e0e5f025cd7713726
|
refs/heads/master
| 2021-08-31T13:37:54.607859
| 2017-12-21T14:23:02
| 2017-12-21T14:23:02
| 114,809,592
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,305
|
r
|
pollutantmean.R
|
pollutantmean <- function(directory, pollutant, id = 1:332) {
# Validate the function parameters
if (length(directory) != 1) stop("A single directory must be specified for the test data.")
if (!dir.exists(directory)) stop(paste("The specified test data directory", directory, "does not exist."))
if (length(pollutant) != 1) stop("A single pollutant type of \"sulfate\" or \"nitrate\" must be specified.")
if (!(pollutant %in% c("sulfate", "nitrate"))) stop(paste("Unknown pollutant:", pollutant, ". Please specify either \"sulfate\" or \"nitrate\"."))
idLength <- length(id)
if (idLength < 1
|| idLength > 332
|| any(!(id %in% 1:332))
|| anyDuplicated(id) > 0)
stop("The id vector may only contain values between 1 and 332 inclusive with no repeats.")
# Get the data
# Read a single file's data
readFileData <- function (id) {
fileData <- read.csv(sprintf("%03d.csv", id))
targetData <- fileData[pollutant]
targetData[!(is.na(targetData))]
}
# Change to the data directory to prevent excessive string concatenations
originalDirectory = getwd()
setwd(directory)
testData <- unlist(sapply(id, readFileData))
# Return to the original directory
setwd(originalDirectory)
# Return the mean of all that data
mean(testData)
}
|
394afbc0e217fb55198edf6d9ea84a1c8beb4f06
|
7c17d45e324d26d6ac1eb42aed7c1a7db3b0238d
|
/z_r_proj/02_ajusta_modelo.R
|
af732600134f7537adcf27c417a5fb0a7446bb11
|
[] |
no_license
|
mhnk77/dpa-project
|
4e267df5649f44c41795eb19e041709669b1a96e
|
a2cc2b4488f002de0e206726b2f633964c2d6efb
|
refs/heads/main
| 2023-05-14T03:57:45.993203
| 2021-05-24T15:51:48
| 2021-05-24T15:51:48
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,057
|
r
|
02_ajusta_modelo.R
|
# library(stringr)
library(tidymodels)
dta <- readRDS('z_r_proj/dta_clean.rds') %>%
drop_na()
rcp <- recipe(results ~ ., data = dta) %>%
update_role(inspection_id, inspection_date, new_role = 'ID') %>%
# update_role(violations, inspection_date, new_role = 'trash') %>%
step_date(inspection_date, features = c('dow', 'month')) %>%
step_rm(inspection_date) %>%
step_other(facility_type, threshold = 0.0002) %>%
step_other(inspection_type, threshold = 0.0002) %>%
step_string2factor(facility_type, zip, inspection_type, results) %>%
step_string2factor(risk, ordered = T)
rf_mod <- rand_forest(mode = 'classification', trees = 1000) %>%
set_engine('ranger', importance = 'impurity', num.threads = 8)
wflow <- workflow() %>%
add_model(rf_mod) %>%
add_recipe(rcp)
mod_fit <- wflow %>%
fit(data = dta)
mod_fit %>%
saveRDS('z_r_proj/mod_fit_factor.rds')
fit_obj <- pull_workflow_fit(mod_fit)$fit
fit_obj$variable.importance %>%
tidy() %>%
arrange(-x) %>%
mutate(imp_rel = x/sum(x))
fit_obj$predictions
|
aeb1cfcbe4094de03e3b788875a2b40ca1fb06be
|
86920ac8061f5b29b86733ec332469bf83761202
|
/scripts/03_combine_kelp_temp_waves.R
|
db8b8a90f903fa6d042f62a441eb3b9cda04401e
|
[] |
no_license
|
jebyrnes/temp_kelp_change
|
64b5c54dd58213d51b356017e2512867ef156e5e
|
b8891d25038ffab02410e5afd4b81d0f0c4d4cea
|
refs/heads/master
| 2021-10-11T14:11:31.283204
| 2021-10-04T19:53:09
| 2021-10-04T19:53:09
| 51,555,573
| 0
| 0
| null | 2021-10-04T19:53:10
| 2016-02-11T23:36:10
|
R
|
UTF-8
|
R
| false
| false
| 5,723
|
r
|
03_combine_kelp_temp_waves.R
|
library(dplyr)
library(readr)
library(tidyr)
library(readxl)
filename <- "wave_data_reguero/timeseries_v2.xls"
dat_year_filter <- . %>%
filter(Year >= 1952 & Year <= 2008) #first data point in kelp, last da
#### 1) Load the different pieces of the wave timeseries
unique_lat_long_tab <- read_excel(filename, sheet=2) #updated nov 2nd
#unique_lat_long_tab <- read_excel("wave_data_reguero/timeseries_new_points.xls", sheet=2)
#unique_lat_long_tab <- read_excel("wave_data_reguero/timeseries.xls", sheet=2)
#unique_lat_long_tab2 <- read_excel("wave_data_reguero/timeseries.xls", sheet=2)
#unique_lat_long_tab
#lat_long <- unique(paste("X", unique_lat_long_tab$LonGOW, unique_lat_long_tab$LatGOW, sep="_"))
#Unique coordinates in the data
gow_coords <- read_excel(filename, sheet=3)
gow_coords_names <- paste("X", gow_coords$GOWLon, gow_coords$GOWLat, sep="_")
## Most of the data uses the same format, so, here's a function to parse it
parse_wave_data <- function(sheet, month=FALSE){
wave_col_name <- c("Year", "Month", gow_coords_names)
if(!month) wave_col_name <- wave_col_name[-2]
wd <- read_excel(filename, sheet=sheet,
col_names=wave_col_name)
if(month){
#forgive this hack - but something odd was happening with tidyr
#hopefully it will be unnecessary once my issue on github
#is resolved
nameFrame <- tibble(gow_coords = names(wd)[-c(1:2)],
newName = paste("X", 1:(ncol(wd)-2), sep="_"))
names(wd) <- c("Year", "Month", nameFrame$newName)
wd <- wd %>% gather(newName, measurement, -Year, -Month) %>%
left_join(nameFrame) %>%
dplyr::select(-newName)
# wd <- wd %>% gather(gow_coords, measurement, -Year, -Month)
}else{
nameFrame <- tibble(gow_coords = names(wd)[-1],
newName = paste("X", 1:(ncol(wd)-1), sep="_"))
names(wd) <- c("Year", nameFrame$newName)
wd <- gather(wd, newName, measurement, -Year) %>%
left_join(nameFrame) %>%
dplyr::select(-newName)
# wd <- wd %>% gather(gow_coords, measurement, -Year)
}
wd %>%
mutate(gow_coords = gsub("X_", "", gow_coords)) %>%
separate(gow_coords, c("GOWLon", "GOWLat"), "_") %>%
mutate(GOWLon = as.numeric(GOWLon), GOWLat = as.numeric(GOWLat))
}
annual_mean_wave_energy <- parse_wave_data(sheet = 4) %>%
dplyr::rename(mean_wave_energy = measurement)
annual_mean_wave_height <- parse_wave_data(sheet = 5) %>%
dplyr::rename(mean_wave_height = measurement)
annual_q95_wave_height <- parse_wave_data(sheet = 6) %>%
dplyr::rename(q95_wave_height = measurement)
monthly_mean_wave_energy <- parse_wave_data(sheet = 7, month=TRUE)
monthly_sum_wave_energy <- parse_wave_data(sheet = 8, month=TRUE)
annual_max_wave_energy <- monthly_mean_wave_energy %>%
group_by(Year, GOWLon, GOWLat) %>%
dplyr::summarise(max_mean_wave_energy = max(measurement, na.rm=T),
sd_mean_wave_energy = sd(measurement, na.rm=T))
sum_wave_energy <- monthly_sum_wave_energy %>%
group_by(Year, GOWLon, GOWLat) %>%
dplyr::summarise(max_sum_wave_energy = max(measurement, na.rm=T),
total_wave_energy = sum(measurement, na.rm=T),
sd_sum_wave_energy = sd(measurement, na.rm=T))
monthly_max_wave_height <- parse_wave_data(sheet = 9, month=TRUE)
annual_max_wave_height <- monthly_max_wave_height %>%
group_by(Year, GOWLon, GOWLat) %>%
dplyr::summarise(max_wave_height = max(measurement, na.rm=T),
sd_max_wave_height = sd(measurement, na.rm=T))
#
# qplot_wave_trends <- function(adf, preprocess=F, preprocessFun = max){
# if(preprocess) {
# adf <- adf %>% group_by(Year, GOWLon, GOWLat) %>%
# summarize(measurement = preprocessFun(measurement, na.rm=T)) %>%
# ungroup()
# }
#
# ggplot(adf %>% group_by(GOWLon, GOWLat) %>%
# mutate(measurement = measurement-mean(measurement, na.rm=T)) %>%
# ungroup(),
# aes(x=Year, y = measurement,
# color=abs(as.numeric(GOWLat)), group=factor(paste(GOWLon, GOWLat)))) +
# geom_line(alpha=0.1) +
# scale_color_continuous(low="red", high="blue", guide=guide_colorbar(title="GOWLat")) +
# stat_smooth(method="lm", fill=NA, color="grey", alpha=0.5) +
# stat_smooth(method="lm", fill=NA, color="black", alpha=0.5, mapping=aes(group=1)) +
# theme_bw(base_size=17) +
# ylab("anomoly")
# }
##### 2) Merge into one mega-frame
wave_data <- left_join(
left_join(
left_join(annual_mean_wave_energy, annual_mean_wave_height),
left_join(annual_max_wave_energy, sum_wave_energy)),
left_join(annual_max_wave_height, annual_q95_wave_height)) %>%
dplyr::rename(LonGOW = GOWLon, LatGOW = GOWLat)
wave_data_full <- left_join(unique_lat_long_tab, wave_data) %>%
dplyr::rename(Latitude = `orig Lat`, Longitude = `orig Lon`) %>%
dat_year_filter %>%
select(!contains("sd_")) #meh. not going to work with these, & want to minimize columns
##### 3) Calculate averages for each lat/long during the entire dataset period
wave_data_avg <- wave_data_full %>%
group_by(Longitude, Latitude, LonGOW, LatGOW) %>%
summarize(across(mean_wave_energy:q95_wave_height,
~mean(.x, na.rm=TRUE),
.names = "{.col}_site")) %>%
ungroup()
##### 4) Load and merge with wave/temp data
kelp_temp_data <- read.csv("derived_data/raw_data_with_temp.csv")
rd_wave <- list(
kelp_temp_data,
wave_data_full,
wave_data_avg
) %>%
purrr::reduce(left_join) %>%
mutate(across(mean_wave_energy:q95_wave_height,
~.x - get(glue::glue("{cur_column()}_site")),
.names = "{.col}_dev"))
##### 6) Write it all out
write_csv(rd_wave, "derived_data/raw_data_with_temp_waves.csv")
|
3a3d00afcd6e3f7f2face0a500a7cc6ad97febd9
|
299585457e6f3fd9c3e82769db4d82becc67d05a
|
/man/backbone.Rd
|
8330a9b84be672789614e149e3022376acf9fa94
|
[] |
no_license
|
jcfisher/backbone
|
39ca2d01f5e99cab28fefd456a69b02a5a6c9fa8
|
89fd0f4b0d786534d398559d8cf72db1abb65b16
|
refs/heads/master
| 2020-08-05T06:28:38.456269
| 2019-10-03T15:42:41
| 2019-10-03T15:42:41
| 212,430,280
| 0
| 0
| null | 2019-10-03T15:42:45
| 2019-10-02T20:01:09
| null |
UTF-8
|
R
| false
| true
| 2,045
|
rd
|
backbone.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/backbone.R
\docType{package}
\name{backbone}
\alias{backbone}
\alias{backbone-package}
\title{backbone: Extracts the Backbone from Weighted Graphs}
\description{
Provides methods for extracting from a weighted graph
a binary or signed backbone that retains only the significant edges.
The user may input a weighted graph, or a bipartite graph
from which a weighted graph is first constructed via projection.
Backbone extraction methods include the stochastic degree sequence model (Neal, Z. P. (2014). <doi:10.1016/j.socnet.2014.06.001>),
hypergeometric model (Neal, Z. (2013). <doi:10.1007/s13278-013-0107-y>),
the fixed degree sequence model (Zweig, K. A., and Kaufmann, M. (2011). <doi:10.1007/s13278-011-0021-0>),
as well as a universal threshold method.
}
\details{
Some features of the package are:
\itemize{
\item '\code{\link{universal}}': returns a unipartite backbone matrix in which
values are set to 1 if above the given upper parameter threshold,
and set to -1 if below the given lower parameter threshold, and are 0 otherwise.
\item '\code{\link{sdsm}}': computes the proportion of generated edges above or below the observed value using the stochastic degree sequence model. Once computed, use \code{\link{backbone.extract}} to return the backbone matrix for a given alpha value.
\item '\code{\link{fdsm}}': computes the proportion of generated edges above or below the observed value using the fixed degree sequence model. Once computed, use \code{\link{backbone.extract}} to return the backbone matrix for a given alpha value.
\item '\code{\link{hyperg}}': returns a binary or signed adjacency matrix
containing the backbone that retains only the significant edges.
\item '\code{\link{backbone.extract}}': returns a positive or signed adjacency matrix
containing the backbone: only the significant edges.
}
For additional documentation and background on the package functions, see \code{browseVignettes("backbone")}.
}
|
3571ddf3a4da571c670bf3a93ff79ec4db192605
|
41cf136f375fb670d61fb88af26bdb2bbee434c3
|
/man/kill_flow.Rd
|
d90c5328e57894a9ec617752f5ea64a21681bb55
|
[] |
no_license
|
gitter-badger/flowr
|
41503fa2e4a4c0187a9864442307ee17740374ed
|
2db159f2721ee8a3ae3aaf34dc0847b842a1c891
|
refs/heads/master
| 2020-12-03T09:15:08.286834
| 2015-06-05T17:57:00
| 2015-06-05T17:57:00
| 36,954,410
| 0
| 0
| null | 2015-06-05T20:41:25
| 2015-06-05T20:41:25
| null |
UTF-8
|
R
| false
| false
| 755
|
rd
|
kill_flow.Rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/status.R
\name{kill_flow}
\alias{kill_flow}
\title{kill_flow}
\usage{
kill_flow(x, wd, fobj, kill_cmd = "bkill", jobid_col = "job_sub_id")
}
\arguments{
\item{x}{either path to flow [character] or fobj object of class \link{flow}}
\item{wd}{path to a specific which needs to be killed}
\item{fobj}{a object of class \link{flow}}
\item{kill_cmd}{The command used to kill. Default is 'bkill' (LSF). One can used qdel for 'torque', 'sge' etc.}
\item{jobid_col}{Advanced use. The column name in 'flow_details.txt' file used to fetch jobids to kill}
}
\description{
kill_flow
}
\examples{
\dontrun{
## example for terminal
flowr kill_flow wd=path_to_flow_directory
}
}
|
68d0e8e487f92f5efb83953800502c214c6df649
|
20fb140c414c9d20b12643f074f336f6d22d1432
|
/man/NISTradTOgray.Rd
|
5ebf101c9bac9ab2e0d25ab9a7ca4dd4f3e8ab1e
|
[] |
no_license
|
cran/NISTunits
|
cb9dda97bafb8a1a6a198f41016eb36a30dda046
|
4a4f4fa5b39546f5af5dd123c09377d3053d27cf
|
refs/heads/master
| 2021-03-13T00:01:12.221467
| 2016-08-11T13:47:23
| 2016-08-11T13:47:23
| 27,615,133
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 722
|
rd
|
NISTradTOgray.Rd
|
\name{NISTradTOgray}
\alias{NISTradTOgray}
\title{Convert rad to gray }
\usage{NISTradTOgray(rad)}
\description{\code{NISTradTOgray} converts from rad (absorbed dose) (rad) to gray (Gy) }
\arguments{
\item{rad}{rad (absorbed dose) (rad) }
}
\value{gray (Gy) }
\source{
National Institute of Standards and Technology (NIST), 2014
NIST Guide to SI Units
B.8 Factors for Units Listed Alphabetically
\url{http://physics.nist.gov/Pubs/SP811/appenB8.html}
}
\references{
National Institute of Standards and Technology (NIST), 2014
NIST Guide to SI Units
B.8 Factors for Units Listed Alphabetically
\url{http://physics.nist.gov/Pubs/SP811/appenB8.html}
}
\author{Jose Gama}
\examples{
NISTradTOgray(10)
}
\keyword{programming}
|
5e9519fdbfcd8020c353bb2c5c974f10ea5ab76b
|
deaec1ac710c5a9388317a188c6005fd39c67ec2
|
/05_ShotogunMetagenome/15_VisualizeAll.R
|
b41ef99999fe6f208974261cae826bb584b7b417
|
[
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] |
permissive
|
ong8181/interaction-capacity
|
57bf4cd7a833b953a12b28f28013019e8e624f7b
|
7b7f5160225468a91c7241fa78b999baf9cd7efa
|
refs/heads/main
| 2023-04-06T17:11:39.353785
| 2022-05-02T05:05:48
| 2022-05-02T05:05:48
| 235,936,633
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,182
|
r
|
15_VisualizeAll.R
|
####
#### Compare the resuls of phyloFlash and qMiSeq
#### Visualize all figures
####
# Load libraries
library(tidyverse); packageVersion("tidyverse") # 1.3.0, 2021.7.23
library(cowplot); packageVersion("cowplot") # 1.1.1, 2021.7.23
library(ggsci); packageVersion("ggsci") # 2.9, 2021.7.23
theme_set(theme_cowplot())
# Generate output folder
(fn <- basename(rstudioapi::getSourceEditorContext()$path))
output_folder <- paste0(str_sub(fn, end = -3), "Out"); rm(fn)
dir.create(output_folder)
# ---------------------------------------------- #
# Load ggplot objects
# ---------------------------------------------- #
g1 <- readRDS(sprintf("%s/g1_s001.obj", output_folder))
g2 <- readRDS(sprintf("%s/g1_s002.obj", output_folder))
g3 <- readRDS(sprintf("%s/g1_s003.obj", output_folder))
g4 <- readRDS(sprintf("%s/g1_s004.obj", output_folder))
g1 <- g1 + labs(title = "Plot 1\n2017/7/14", subtitle = "No. of detected taxa\nqMiSeq = 136\nShotgun = 118")
g2 <- g2 + labs(title = "Plot 1\n2017/7/15", subtitle = "No. of detected taxa\nqMiSeq = 129\nShotgun = 99")
g3 <- g3 + labs(title = "Plot 5\n2017/7/14", subtitle = "No. of detected taxa\nqMiSeq = 82\nShotgun = 116")
g4 <- g4 + labs(title = "Plot 5\n2017/7/15", subtitle = "No. of detected taxa\nqMiSeq = 91\nShotgun = 115")
# ---------------------------------------------- #
# Compile all figures
# ---------------------------------------------- #
g_legend <- get_legend(g1)
g5 <- g1 + theme(legend.position = "none", plot.margin = margin(.8,.8,.8,.8,"cm"))
g6 <- g2 + theme(legend.position = "none")
g7 <- g3 + theme(legend.position = "none")
g8 <- g4 + theme(legend.position = "none")
g_all <- plot_grid(g5, g6, g7, g8, NULL, g_legend,
ncol = 6, rel_widths = c(1,1,1,1,0.2,1),
labels = c("a","b","c","d",NA,NA),
align = "hv", axis = "lrbt")
g_obj <- list(g1, g2, g3, g4)
# ---------------------------------------------- #
# Save output
# ---------------------------------------------- #
ggsave(file = sprintf("%s/qMiSeq_Shotgun_All.pdf", output_folder),
plot = g_all, width = 16, height = 7)
saveRDS(g_obj, sprintf("%s/qMiSeq_Shotgun_All.obj", output_folder))
|
9fc57f5a1662cba4016f68ad50a812406e8187af
|
99a79794582dfab5cf27cbe290410b3b06c7f4df
|
/00_scripts/20_GBM/GBM_13.R
|
7c94c37d8ab42ea61977473eb2a714cd616260db
|
[] |
no_license
|
PLStenger/Pearl_Oyster_Colour_BS_Seq
|
01db29172513ec8a84689ea187eff787cdd52c87
|
c80162bd330573689f929554edae26cd32038917
|
refs/heads/master
| 2021-07-17T14:44:47.677414
| 2020-06-22T06:20:16
| 2020-06-22T06:20:16
| 183,000,705
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 444
|
r
|
GBM_13.R
|
#!/usr/bin/env Rscript
setwd("/home/datawork-ihpe/Pearl_Oyster_Colour_BS_Seq/06_bismark")
dat <- read.table("Index_7.2-V-613_R1_paired_bismark_bt2_pe.deduplicated.bam_sorted_clean.bam.bed_correspondance.bed_base.txt")
for(i in 1:length(unique(dat$V1))){
sink("Index_7.2-V-613_GBM.txt", append=TRUE)
pos_name <- as.character(unique(dat$V1)[i])
newdata <- dat[ which(dat$V1==pos_name), ]
print(c(pos_name, mean(newdata$V2)))
sink()
}
|
505b2860874fec9b97327dab2798ba5039b6c064
|
bcf64441bd84791260d509236fd45ce1e8bf86f2
|
/R/draw_serve_locations.R
|
14455bf8717e6fba952b023024bd6ea4a17d1a50
|
[] |
no_license
|
buddyscott/esv
|
69e66d93fedb64022701ff717956764e32bedd2a
|
4bf8d8328b3df99c34d974567b7c09161ba83426
|
refs/heads/master
| 2022-12-15T18:46:43.550957
| 2020-09-05T14:30:56
| 2020-09-05T14:30:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,532
|
r
|
draw_serve_locations.R
|
library(ggplot2)
library(dplyr)
library(DT)
source('static_variables.R')
source('plotting_utilities.R')
source('sample_data_prep.R')
g <- ggplot() +
geom_rect(data = draw_tennis_court_surface_ggplot(),
mapping = aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = ID)) +
geom_path(data = draw_tennis_court_lines_ggplot(),
mapping = aes(x = x, y = y, group = ID),
color = 'white') +
geom_path(data = draw_tennis_net_ggplot(),
mapping = aes(x = x, y = y),
color = 'white', linetype = 'dashed') +
geom_point(data = shot_df_sample %>%
filter(plc %in% c('1S', '2S')) %>%
mutate(x = ifelse(striker_x_coordinate < 0,
-striker_x_coordinate,
striker_x_coordinate),
y = ifelse(striker_x_coordinate < 0,
-striker_y_coordinate,
striker_y_coordinate)),
mapping = aes(x = x, y = y),
color = 'white', alpha = 0.25) +
geom_point(data = shot_df_sample %>%
filter(plc %in% c('1S', '2S')) %>%
mutate(x = ifelse(returner_x_coordinate < 0,
returner_x_coordinate,
-returner_x_coordinate),
y = ifelse(returner_x_coordinate < 0,
returner_y_coordinate,
-returner_y_coordinate)),
mapping = aes(x = x, y = y),
color = 'cyan', alpha = 0.25) +
geom_path(data = draw_region_lines_ggplot(),
mapping = aes(x = x, y = y, group = ID),
color = 'white', alpha = 0.5, linetype = 'dotted') +
geom_text(data = draw_region_numbers_ggplot(),
mapping = aes(x = x, y = y, label = region_number),
color = 'white', size = 5, alpha = 0.5, angle = 270) +
geom_text(data = draw_region_numbers_ggplot(),
mapping = aes(x = -x, y = -y, label = region_number),
color = 'white', size = 5, alpha = 0.5, angle = 90) +
scale_fill_manual(values = c('ob_surface' = '#658B4E', 'ib_surface' = '#2A5387')) +
scale_y_continuous(limits = c(-max_y, max_y),
breaks = c(round(-baseline_y - doubles_alley_width, 3),
round(-baseline_y, 3),
0,
round(baseline_y, 3),
round(baseline_y + doubles_alley_width,3)),
expand = c(0,0)) +
scale_x_continuous(limits = c(-max_x, max_x),
breaks = c(round(-baseline_x, 3),
round(-service_line, 3),
0,
round(service_line, 3),
round(baseline_x, 3)),
expand = c(0,0)) +
guides(fill = FALSE, size = FALSE, alpha = FALSE, colour = FALSE) +
ylab('y (m)') + xlab('x (m)') +
coord_fixed(ratio = 1, xlim = NULL, ylim = NULL, expand = TRUE) +
theme(line = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "mm"),
legend.title = element_blank(),
panel.background = element_rect(colour = NULL, fill = NULL))
ggsave('../plots/serve_locations.jpg', g, height = 150, width = 200, unit = 'mm')
|
a3b2be59e974102aa9cd8b657605ec757fd2f098
|
09024c65f99a196f63d742947ce8e3026d4b9088
|
/DEG-analysis.R
|
b5881f1739f45bd1aa788f38b44b1846dab7fc06
|
[] |
no_license
|
Xianglaichi/MSI
|
f8c4527d6effca4606bf5a4fac96ea682b8453c6
|
f57d5888fd4dc3cd8ca2f808b783e7757c0d55ab
|
refs/heads/main
| 2023-02-13T18:05:28.020094
| 2021-01-06T09:04:45
| 2021-01-06T09:04:45
| 327,251,680
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,012
|
r
|
DEG-analysis.R
|
for (i in 1:30){
print(i)
datalist_new1[[i]]<-datalist_new1[[i]][,match(summaryrfs[[i]]$id,colnames(datalist_new1[[i]]))]}
pan_group<-list()
for (i in 1:30){
print(i)
pan_group[[cancername1[i]]]<-summaryrfs[[i]]
pan_group[[cancername1[i]]]<-within(pan_group[[cancername1[i]]],{
High<-NA
Low<-NA
High[MSI == 'MSI-H'] = '1'
High[MSI == 'MSI-L'] = '0'
Low[MSI == 'MSI-H'] = '0'
Low[MSI == 'MSI-L'] = '1'
})
rownames(pan_group[[cancername1[i]]])<-pan_group[[cancername1[i]]]$id
pan_group[[cancername1[i]]]$id<-NULL
pan_group[[cancername1[i]]]<-pan_group[[cancername1[i]]][,-c(1:8)]
}
pan_group<-lapply(pan_group,function(df){
df[] <- lapply(df, function(x) {
if(is.factor(x)) as.numeric(as.character(x)) else as.numeric(as.character(x))
})
return(df)
})
pan_group<-lapply(pan_group,as.matrix)
DEG<-list()
for (i in 27:30){
print(i)
fit <- lmFit(datalist_new1[[i]],pan_group[[i]])
contrast.matrix <- makeContrasts(High - Low,levels=pan_group[[i]])
fit2 <- contrasts.fit(fit,contrast.matrix)
fit2 <- eBayes(fit2)
all_diff <- topTable(fit2, adjust.method = 'fdr',coef=1,p.value = 1,lfc <- log(1,2),number = 60000,sort.by = 'logFC')
all_diff$logP<- -log10(all_diff$adj.P.Val)
DEG[[cancername1[i]]]<-all_diff
DEG[[cancername1[i]]]$ID<-rownames(DEG[[cancername1[i]]])
}
cancernameDEG<-names(DEG)
DEGtest<-DEG
DEG<-DEGtest
for (i in 1:26){
print(i)
DEG[[i]]$cancer<-cancernameDEG[i]
DEG[[i]]$gene<-rownames(DEG[[i]])
}
DEG<-lapply(DEG,function(df){
df<-df%>%filter(df$adj.P.Val<0.05)
})
DEG<-lapply(DEG,function(df){
df<-df%>%filter(abs(df$logFC)>0.5)
})
for (i in 1:26){
print(i)
DEG[[i]]<-within(DEG[[i]],{
group<-NA
group[logFC > 0 ] = 'up-regulated'
group[logFC < 0 ] = '-down-regulated'
})
}
DEG<-lapply(DEG,function(df){
df$group<-as.factor(df$group)
return(df)
})
DEGbar<-data.frame()
for (i in 1:26){
print(i)
DEGbar[2*i-1,1]<-cancernameDEG[i]
DEGbar[2*i-1,2]<-summary(DEG[[i]]$group)[1]
DEGbar[2*i-1,3]<-'High'
DEGbar[2*i-1,4]<-summary(DEG[[i]]$group)[1]+summary(DEG[[i]]$group)[2]
DEGbar[2*i,1]<-cancernameDEG[i]
DEGbar[2*i,2]<-summary(DEG[[i]]$group)[2]
DEGbar[2*i,3]<-'Low'
DEGbar[2*i,4]<-summary(DEG[[i]]$group)[1]+summary(DEG[[i]]$group)[2]
}
colnames(DEGbar)<-c('cancer','value','Group','sum')
DEGbar%>%mutate(cancer = fct_reorder(cancer, desc(sum)))%>% #此处表示按反向排列！管道连接???
ggplot(aes(x=cancer, y=value,fill = Group)) +
geom_bar(stat="identity",alpha=0.7, width=0.8,position=position_dodge(0.8)) +
theme_bw()+
scale_fill_manual(values=c('High' = '#FF3333','Low' = '#0099FF'))+ #表示手动调节颜色???
coord_flip() +
xlab("") + ylab("")+
theme(axis.text.x = element_text(size = 13, angle = 0, hjust = 0, vjust = 0),
axis.text.y = element_text(size = 15, angle = 0, hjust = 0, vjust = 0))
write.csv(DEGbar,file = "/DEGbar.csv")
|
92f25660751dc728a97fe4a877d641545e693ec5
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/fullfact/examples/observLmer.Rd.R
|
be0bf9147a40b4fb5013f3e6d59f92b771c69cc3
|
[] |
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
| 287
|
r
|
observLmer.Rd.R
|
library(fullfact)
### Name: observLmer
### Title: Variance components for normal data
### Aliases: observLmer
### ** Examples
data(chinook_length) #Chinook salmon offspring length
length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length")
length_mod1
|
ed6dddd75c394998eccba121183bfe1e3a9fa203
|
160622f50fc2fe9a6aaa3095849f7a8bd2caa496
|
/man/b_grid.Rd
|
5581746d2cd5e8c8590e059c48edd32a69268b69
|
[] |
no_license
|
jayhesselberth/platetools
|
53fd0d16deca84ec6efd0c1f838d7a9fed835a1b
|
617a33fc4a3482b85fc1fd8b38dcc82a53a10176
|
refs/heads/master
| 2020-03-22T01:45:56.557163
| 2018-06-25T13:36:13
| 2018-06-25T13:36:13
| 139,328,253
| 1
| 0
| null | 2018-07-01T12:42:49
| 2018-07-01T12:42:48
| null |
UTF-8
|
R
| false
| true
| 1,013
|
rd
|
b_grid.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/b_grid.R
\name{b_grid}
\alias{b_grid}
\title{Plots multiple b-scored normalised platemaps}
\usage{
b_grid(data, well, plate_id, plate = 96)
}
\arguments{
\item{data}{Numerical values to be plotted}
\item{well}{Vector of well identifiers e.g "A01"}
\item{plate_id}{Vector of plate identifiers e.g "Plate_1"}
\item{plate}{Number of wells in complete plate (96, 384 or 1536)}
}
\value{
ggplot plot
}
\description{
Transforms numerical values using the b-score normalisation process to
account for row and column effects. Uses well and plate labels to plot the
normalised values in the form of microtitre plates. Works for 96, 384 and
1536 well plates.
}
\examples{
df01 <- data.frame(well = num_to_well(1:96),
vals = rnorm(96),
plate = 1)
df02 <- data.frame(well = num_to_well(1:96),
vals = rnorm(96),
plate = 2)
df <- rbind(df01, df02)
b_grid(data = df$vals,
well = df$well,
plate_id = df$plate,
plate = 96)
}
|
75ed5fdbb72e09cc3034ad857816f9976ecdb01d
|
bc930a6e02dd38d046a7904b31b4d3557ef8a284
|
/R/event_counter.R
|
9aa6c5d23388db738237cff53bc7adbb31dff804
|
[
"MIT"
] |
permissive
|
kalimu/shinyEventLogger
|
ea66443f427f60d4e45b1405c7fcb12b247c977b
|
d6dd6d5ceec57b2e44cff8fa7a83b2e018973396
|
refs/heads/master
| 2021-07-25T09:08:21.928018
| 2019-02-11T06:59:48
| 2019-02-11T07:00:29
| 157,194,640
| 30
| 5
|
NOASSERTION
| 2020-04-28T22:08:28
| 2018-11-12T10:15:58
|
R
|
UTF-8
|
R
| false
| false
| 629
|
r
|
event_counter.R
|
get_event_counter <- function() {
settings_session <-
dynGet("log_settings_session",
minframe = 1L,
inherits = TRUE,
ifnotfound = stop(paste0(
"'log_settings_session' not found. ",
"Have you call 'set_logging_session'?"
)))
settings_session$event_counter
} # end of get_event_counter
increment_event_counter <- function() {
settings_session <-
dynGet("log_settings_session",
minframe = 1L,
inherits = TRUE)
settings_session$event_counter <-
settings_session$event_counter + 1
} # end of increment_event_counter
|
2ec697bde65f7fd94ba5bfea48788b8b55c56478
|
f6c871a87f64a5ca6d8dcf6931e05e6dc0fb0c1f
|
/PreferencesPaper/SweepReaderFC.R
|
a97527ebe0f630c189584265947c5590625c5ba3
|
[] |
no_license
|
CreanzaLab/SongEvolutionModel
|
073342a07eaeca061f92b6798b2ab1ff40567b58
|
449087c85280e11b97924575d2e3c618169a0f8b
|
refs/heads/master
| 2020-06-13T18:23:13.603932
| 2019-08-15T22:57:51
| 2019-08-15T22:57:51
| 194,747,721
| 0
| 1
| null | 2019-08-15T22:57:52
| 2019-07-01T21:52:31
|
R
|
UTF-8
|
R
| false
| false
| 5,978
|
r
|
SweepReaderFC.R
|
setwd("D:/Prelim2")
rm(list=objects())
source("Source_SweepSquareReaderFC.R")
#install_github("CreanzaLab/SongEvolutionModel/R-Package", force=TRUE, ref="FemalChoiceExpansion")
#Stitcher(file.path(getwd(),"StaticFinal"))
#Stitcher(file.path(getwd(),"SkipFinal"))
Data <- LoadData(file.path(getwd(),"StaticFinal", "Stitched"), 2000, 20)
Ordered <- order(Data$FinalData2$Preference,
Data$FinalData2$PLrnStrtgy,
rev(as.logical(Data$FinalData2$Vertical)),
rev(as.logical(Data$FinalData2$Social)),
decreasing = FALSE)
Labels <- list(Label1=paste(c("TRUE", "FALSE")),
Label2=paste(c("TRUE", "FALSE")), #Tutor
Label3=levels(as.factor(Data$FinalData2$PLrnStrtgy)),
Label4="lewl")
write.csv(Data$FinalData2[,], "TableofKeyParams.csv")
pdf("Common.pdf", width=6, height=8.5)
Labels[['Label4']] <- levels(as.factor(Data$FinalData2$Preference))[1]
QuadPlots(Labels, Ordered, subset=1:24, FALSE, mar=c(0,3,1.5,.5), End=FALSE, mfrow=c(5,1),
Color = c(rgb(1,1,1), rgb(1,1,1),rgb(.85,.95,1), rgb(.8,.9,.95),
rgb(.75,.85,.9), rgb(.7,.8,.85),rgb(.65,.75,.8) ))
mtext(side=2, at=c(.5, .9), line=-.45, text=c("Vertical","Social"), cex=.7, font=2)
dev.off()
pdf("Match.pdf", width=6, height=8.5)
Labels[['Label4']] <- levels(as.factor(Data$FinalData2$Preference))[2]
QuadPlots(Labels, Ordered, subset=25:48, FALSE, mar=c(0,3,1.5,.5), End=FALSE, mfrow=c(5,1),
Color = c(rgb(1,1,1), rgb(1,1,1),rgb(.85,.95,1), rgb(.8,.9,.95),
rgb(.75,.85,.9), rgb(.7,.8,.85),rgb(.65,.75,.8) ))
mtext(side=2, at=c(.5, .9), line=-.45, text=c("Vertical","Social"), cex=.7, font=2)
dev.off()
pdf("Noise.pdf", width=6, height=8.5)
Labels[['Label4']] <- levels(as.factor(Data$FinalData2$Preference))[3]
QuadPlots(Labels, Ordered, subset=49:72, FALSE, mar=c(0,3,1.5,.5), End=FALSE, mfrow=c(5,1),
Color = c(rgb(1,1,1), rgb(1,1,1),rgb(.85,.95,1), rgb(.8,.9,.95),
rgb(.75,.85,.9), rgb(.7,.8,.85),rgb(.65,.75,.8) ))
mtext(side=2, at=c(.5, .9), line=-.45, text=c("Vertical","Social"), cex=.7, font=2)
dev.off()
pdf("Rare.pdf", width=6, height=8.5)
Labels[['Label4']] <- levels(as.factor(Data$FinalData2$Preference))[4]
QuadPlots(Labels, Ordered, subset=73:96, FALSE, mar=c(0,3,1.5,.5), End=FALSE, mfrow=c(5,1),
Color = c(rgb(1,1,1), rgb(1,1,1),rgb(.85,.95,1), rgb(.8,.9,.95),
rgb(.75,.85,.9), rgb(.7,.8,.85),rgb(.65,.75,.8) ))
mtext(side=2, at=c(.5, .9), line=-.45, text=c("Vertical","Social"), cex=.7, font=2)
dev.off()
pdf("Rep.pdf", width=6, height=8.5)
Labels[['Label4']] <- levels(as.factor(Data$FinalData2$Preference))[5]
QuadPlots(Labels, Ordered, subset=97:120, FALSE, mar=c(0,3,1.5,.5), End=FALSE, mfrow=c(5,1),
Color = c(rgb(1,1,1), rgb(1,1,1),rgb(.85,.95,1), rgb(.8,.9,.95),
rgb(.75,.85,.9), rgb(.7,.8,.85),rgb(.65,.75,.8) ))
mtext(side=2, at=c(.5, .9), line=-.45, text=c("Vertical","Social"), cex=.7, font=2)
dev.off()
pdf("SexySyls.pdf", width=6, height=8.5)
Labels[['Label4']] <- levels(as.factor(Data$FinalData2$Preference))[6]
QuadPlots(Labels, Ordered, subset=121:144, FALSE, mar=c(0,3,1.5,.5), End=FALSE, mfrow=c(5,1),
Color = c(rgb(1,1,1), rgb(1,1,1),rgb(.85,.95,1), rgb(.8,.9,.95),
rgb(.75,.85,.9), rgb(.7,.8,.85),rgb(.65,.75,.8) ))
mtext(side=2, at=c(.5, .9), line=-.45, text=c("Vertical","Social"), cex=.7, font=2)
dev.off()
pdf("Social.pdf", width=6, height=8.5)
Labels[['Label4']] <- levels(as.factor(Data$FinalData2$Preference))[7]
QuadPlots(Labels, Ordered, subset=145:168, FALSE, mar=c(0,3,1.5,.5), End=FALSE, mfrow=c(5,1),
Color = c(rgb(1,1,1), rgb(1,1,1),rgb(.85,.95,1), rgb(.8,.9,.95),
rgb(.75,.85,.9), rgb(.7,.8,.85),rgb(.65,.75,.8) ))
mtext(side=2, at=c(.5, .9), line=-.45, text=c("Vertical","Social"), cex=.7, font=2)
dev.off()
path <- file.path(getwd(), "StaticFinal")
for(i in 1:168){
if(file.exists(file.path(path, paste0(i, "MSong.csv")))){
P <- ReloadParam(file.path(path, "Stitched", paste0(i, ".semp")))
Songs <- read.csv(file.path(path, paste0(i, "MSong.csv")),
header=FALSE, fileEncoding = "UTF-8-BOM")
Songs <- getAverageSong(P, Songs)
Songs <- matrix(unlist(Songs), ncol=101, nrow=P$MaxRSize)
pdf(paste0("Songs/Songer-",i, ".pdf"))
SongPlot(P,Songs, thin=1)
dev.off()
}
}
SyllableFrequency <- data.frame(Template=numeric(168),
RareTemplate=numeric(168),
NonTemplate=numeric(168))
for(i in 1:168){
if(file.exists(file.path(path, paste0(i, "MSong.csv")))){
P <- ReloadParam(file.path(path, "Stitched", paste0(i, ".semp")))
Songs <- read.csv(file.path(path, paste0(i, "MSong.csv")),
header=FALSE, fileEncoding = "UTF-8-BOM")
Songs <- getAverageSong(P, Songs)
Songs <- matrix(unlist(Songs), ncol=101, nrow=P$MaxRSize)
SyllableFrequency[i,] <-c(mean(Songs[1:5,101])/P$numBirds,
mean(Songs[6:7,101])/P$numBirds,
mean(Songs[8:500,101])/P$numBirds)
}
}
SyllableFrequency[,'Temp-nonTemp'] <-SyllableFrequency$Template - SyllableFrequency$NonTemplate
par(mfrow=c(3,1), mgp=c(1.5, .5, 0), mar=c(2.5,2.5,1,1))
plot(1:168,SyllableFrequency$Template[Ordered], type='b', pch=19, ylim=c(0,1), cex=.6,
lty=6,
panel.first = {abline(v=seq(24, 168, by=25)+.5, col="grey80")})
plot(1:168,SyllableFrequency$NonTemplate[Ordered], type='b', pch=19, ylim=c(0,1), cex=.6,
lty=6,
panel.first = {abline(v=seq(24, 168, by=25)+.5, col="grey80")})
plot(1:168,SyllableFrequency$`Temp-nonTemp`[Ordered], type='b', pch=19, ylim=c(-.2,1), cex=.6,
lty=6,
panel.first = {abline(v=seq(24, 168, by=25)+.5, col="grey80")})
Results <- read.csv("TableofMagic.csv")
|
9ae8b9dfbca3501716ccadadea58abd2f474bd9e
|
77645a8337ba03357128505872dc77cb4d49e2bf
|
/R/lookup-title-descr.R
|
b430727821237440ea6f4bb55add30617457c22e
|
[
"MIT"
] |
permissive
|
jameelalsalam/naicsmatch
|
0316ae97cb212d791229d04ad64789b39ac39191
|
a43a3f408ca2eb3be4b10c2120a03376ad830c0f
|
refs/heads/main
| 2021-07-11T17:02:53.204673
| 2020-04-01T10:09:47
| 2020-04-01T10:09:47
| 162,162,674
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 837
|
r
|
lookup-title-descr.R
|
#' Lookup Titles and Descriptions of NAICS Codes
#'
#' Convenience function. Focus on most recent NAICS code classifications. Lookup for prior may be added in teh
#' @param naics a character vector of NAICS codes to look up
#' @param naics_listing a lookup table of naics codes and titles, defaults to most recent
#' @aliases naics_descr
#' @import dplyr
#' @export
#' @examples
#' naics_title(c("332710", "332722", "332723"))
naics_title <- function(naics, naics_listing = naicsmatch::naics_2017) {
tibble::tibble(naics = naics) %>%
left_join(naics_2017_listing, by = "naics") %>%
pull(title)
}
#' @rdname naics_title
#' @export
#' @examples
#' naics_descr(c("332710", "332722", "332723"))
naics_descr <- function(naics) {
tibble::tibble(naics = naics) %>%
left_join(naics_2017, by = "naics") %>%
pull(descr)
}
|
1b48b3d817dc03d4154deda30f0cbf731e1324fb
|
c0fec0769584c9bd2d2a322f948e99283253ab9e
|
/man/Motif-class.Rd
|
3ff02f33d9ab5ceda62e6da1bf0afd79e36d73eb
|
[
"MIT"
] |
permissive
|
Puriney/signac
|
ed88d42b8f9af470badf9fee4ff6f867a6eaea73
|
cd99cf2212320785f34dfd5db58e14b59e89edc5
|
refs/heads/master
| 2023-01-28T05:41:10.679212
| 2020-12-04T17:13:54
| 2020-12-04T17:13:54
| 273,932,199
| 0
| 0
|
NOASSERTION
| 2020-09-16T15:15:39
| 2020-06-21T15:30:09
|
R
|
UTF-8
|
R
| false
| true
| 1,028
|
rd
|
Motif-class.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{Motif-class}
\alias{Motif-class}
\alias{Motif}
\title{The Motif class}
\description{
The Motif class is designed to store DNA sequence motif information,
including motif PWMs or PFMs, motif positions, and metadata.
}
\section{Slots}{
\describe{
\item{\code{data}}{A sparse, binary, feature x motif matrix. Columns
correspond to motif IDs, rows correspond to genomic features
(peaks or bins). Entries in the matrix should be 1 if the
genomic feature contains the motif, and 0 otherwise.}
\item{\code{pwm}}{A named list of position weight matrices}
\item{\code{motif.names}}{A list containing the name of each motif}
\item{\code{positions}}{A \code{\link[GenomicRanges]{GRangesList}} object containing
exact positions of each motif.}
\item{\code{meta.data}}{A dataframe for storage of additional
information related to each motif. This could include the
names of proteins that bind the motif.}
}}
\concept{motifs}
|
3990d20fbcd9e8247d0d078bcbe717a16f20d93f
|
c7add6406714aad477f5b3690914fcff0befeed9
|
/R/consensusPeakList.R
|
588986f7dc8bec38014519a35cc28658e50e3f86
|
[] |
no_license
|
gtluu/rspeclust
|
37a006aa53e7504a004a9b8475f6226fed37b3d1
|
1a212a18cc11ffefba3ca4e46d051d7201873218
|
refs/heads/master
| 2022-12-05T14:21:40.634741
| 2020-08-20T21:31:46
| 2020-08-20T21:31:46
| 288,889,087
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,458
|
r
|
consensusPeakList.R
|
#' Get Consensus Peak List
#'
#' Generate a consensus peak list with average m/z values from multiple peak
#' lists.
#'
#' @param peakLists \code{list} of single column \code{dataframes} with the
#' column name 'mz'.
#' @param tol \code{double} value to be used for m/z tolerance when merging
#' peak lists.
#' @param cutoff \code{numeric} value; peaks that appear in \code{n} peak lists
#' are kept if \code{n >= cutoff}.
#' @return \code{dataframe} with each row representing shared peaks from
#' multiple peak lists based on m/z value along with a column for number of
#' peak lists it appears in.
#' @example
#'
#' peakList1 <- data.frame('mz'=c(615.3456, 489.6651, 375.1968))
#' peakList2 <- data.frame('mz'=c(615.3589, 453.3596, 357.9618))
#' peakList3 <- data.frame('mz'=c(615.3358, 861.3456, 198.3557))
#'
#' peakLists <- list(peakList1, peakList2, peakList3)
#'
#' consensus <- consensusPeakList(peakLists, tol=0.2, cutoff=0.5)
#'
#' @export
consensusPeakList <- function(peakLists, tol, cutoff) {
# Combine peak lists into single large dataframe.
bigDf <- combinePeakLists(peakLists, tol=tol)
# Find how many peak lists each peak was found in and remove if lower than
# the cutoff.
bigDf$N <- rowSums(!is.na(bigDf))
bigDf <- bigDf[which(bigDf$N >= cutoff),]
# Get average m/z values.
n <- bigDf$N
bigDf <- subset(bigDf, select=-c(N))
bigDf$average <- rowMeans(bigDf, na.rm=TRUE)
bigDf$N <- n
return(bigDf)
}
|
80dc39decdf534fa563f12405d9adebf070b7da7
|
cf770445f090d49f1a93b706f881a32b35b6777d
|
/R/make_target_ls.R
|
b6e29069cce37fd5ed837a48714c935c672e7cf7
|
[] |
no_license
|
devincaughey/estsubpop
|
70ad85b33a52353c62816fe8676082498cac2bfb
|
9bf4a32f8ca04d29c8307c8923e5d3264778a830
|
refs/heads/master
| 2020-03-25T09:22:01.395227
| 2019-06-11T02:26:10
| 2019-06-11T02:26:10
| 143,662,471
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 857
|
r
|
make_target_ls.R
|
#' @export
make_target_ls <- function (design_list, formulae_list, periods,
period_var = "YEAR") {
out <- vector("list", length(periods))
names(out) <- periods
for (y in seq_along(periods)) {
out[[y]] <- list()
for (d in seq_along(design_list)) {
for (f in seq_along(formulae_list[[d]])) {
ds <- subset(design_list[[d]],
design_list[[d]]$variables[, period_var] == periods[y])
df <- as.data.frame(survey::svytable(formulae_list[[d]][[f]], ds))
df$Freq <- df$Freq / length(formulae_list[[d]]) #avoid double-counting
if (sum(df$Freq) > 0) {
out[[y]] <- c(out[[y]], list(df))
names(out[[y]])[length(out[[y]])] <-
paste(d, paste(formulae_list[[d]][[f]], collapse = ""), sep = " | ")
}
}
}
}
return(out)
}
|
6070785668ec8d1941ce1986eb904461dc793e2f
|
a4c82295d300bae1b76287504d968b61836a222f
|
/tests/testthat.R
|
2530bb5684295dd4cc54e7302e20db6611b7bcfb
|
[
"MIT"
] |
permissive
|
sainathadapa/stubthat
|
29ddbd4166509d9abb864a14a24eb908965d866c
|
6fcfcdbba6f605ef5dd093d455822ade30643682
|
refs/heads/master
| 2022-05-12T09:14:02.326260
| 2022-05-07T13:31:08
| 2022-05-07T13:31:08
| 41,420,394
| 20
| 6
|
NOASSERTION
| 2022-05-03T12:39:37
| 2015-08-26T10:47:41
|
R
|
UTF-8
|
R
| false
| false
| 60
|
r
|
testthat.R
|
library(testthat)
library(stubthat)
test_check("stubthat")
|
16976c1289a80ce747854df571acc5a3d605fbfe
|
307daa5d64a3e1e5ab2b6e4e85c83133e9d43e7b
|
/man/getCVLData.Rd
|
c582ea951b8b6e37cd957d2e460cefa771b65747
|
[] |
no_license
|
hkim207/ahri-1
|
80be69695ee74acb2f10fc9b63b74998f02e04f3
|
d2a671671c1b8cf66bc97d6d7135581254ebb30e
|
refs/heads/master
| 2022-12-13T16:51:37.618854
| 2020-09-26T20:05:22
| 2020-09-26T20:05:22
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 358
|
rd
|
getCVLData.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getVLData.R
\name{getCVLData}
\alias{getCVLData}
\title{getCVLData}
\usage{
getCVLData(Args)
}
\arguments{
\item{Args}{requires Args (\code{\link{setArgs}}) for filepath and setting age}
}
\value{
data.frame
}
\description{
Pulls in Community Viral Load data
}
\keyword{internal}
|
6266cd224d3f5d97f88f42a275cda6027f8141c0
|
2132efc961fd422dea6ba2f6ab205e14993556e4
|
/tests/testthat/test_biodiversidad.R
|
e61e138e0d098dfc5fb9afc7031e36303d5993e4
|
[] |
no_license
|
datasketch/GanaderiaSostenible
|
3f62e05879535520a2acc7105a1b120f1928f0af
|
4e78982713bfe6b1e4720d71c8a8ac50d0f7f703
|
refs/heads/master
| 2020-09-09T16:00:53.379680
| 2020-03-02T15:28:00
| 2020-03-02T15:28:00
| 221,488,970
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,977
|
r
|
test_biodiversidad.R
|
context("Biodiversidad")
test_that("Biodiversidad works",{
area <- 10
region <- "Eje Cafetero"
pop2 <- biodiv_area(10, "Eje Cafetero", "bosque_secundario")
pop2
pop2 <- biodiv_area(1000, region, "bosque_secundario")
pop2
area <- 10
region <- "Bajo Magdalena"
tipo_cobertura <- "bosque_secundario"
pop <- biodiv_area(10, region, "bosque_secundario")
pop2
pop2 <- biodiv_area(1000, region, "bosque_secundario")
pop2
areas <- c(100)
region <- "Eje Cafetero"
total <- biodiv_area(areas, region, t = 10, tipo_cobertura = "arboles_dispersos")
total
})
test_that("Biodiversidad aves 2", {
region <- "Bajo Magdalena"
tipo_cobertura <- "bosque_secundario"
area <- 10
region <- "Bajo Magdalena"
tipo_cobertura <- "bosque_secundario"
especies1 <- biodiv_area(area, region, tipo_cobertura)
especies1
especies2 <- biodiv_area2(area, region, tipo_cobertura)
especies2
biodiv_area2(0, region, tipo_cobertura)
biodiv_area2(NA, region, tipo_cobertura)
#biodiv_area2(NULL, region, tipo_cobertura)
expect_equal(0,biodiv_area2(0, "Eje Cafetero", "bosque_primario"))
expect_equal(as.numeric(NA), biodiv_area2(NA, region, tipo_cobertura))
expect_equal(biodiv_area2(1, "Eje Cafetero", "bosque_secundario"),biodiv_area2(1, "Eje Cafetero", "bosque_primario"))
expect_equal(biodiv_area2(1, "Eje Cafetero", "silvopastoriles"),biodiv_area2(1, "Eje Cafetero", "arboles_dispersos"))
expect_equal(biodiv_area2(1, "Eje Cafetero", "cercas_vivas"),biodiv_area2(1, "Eje Cafetero", "arboles_dispersos"))
expect_equal(0, biodiv_area2(area = 0, region = region, tipo_cobertura = 'silvopastoriles'))
##
expect_equal(26,round(biodiv_area2(1, "Eje Cafetero", "bosque_primario")))
expect_equal(4,round(biodiv_area2(1, "Eje Cafetero", "silvopastoriles")))
expect_equal(691,round(biodiv_area2(301, "Eje Cafetero", "bosque_primario")))
expect_equal(38,round(biodiv_area2(1001, "Eje Cafetero", "silvopastoriles")))
# cargar paquete
library (sars) # Species area relationship
### Ejemplo para predecir número de especies
# # cargar las funciones Area - especies
load(system.file("biodiversity", "funciones_area_especies.RData", package = "GanaderiaSostenible"))
# Predecir numero de especies proporcionando el area para cada region.
# Note que el area esta en metros (1ha=10.000 m2)
# sar_pred(Bajo_Magdalena_bosque_secundario, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Bajo_Magdalena_silvopastoriles, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Boyaca_y_Santander_bosque_secundario, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Boyaca_y_Santander_silvopastoriles, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Eje_Cafetero_bosque_secundario, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Eje_Cafetero_silvopastoriles, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Piedemonte_del_Meta_bosque_secundario, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Piedemonte_del_Meta_silvopastoriles, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Valle_del_Rio_Cesar_bosque_secundario, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
# sar_pred(Valle_del_Rio_Cesar_silvopastoriles, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
#sar_pred(Eje_Cafetero_bosque_secundario, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
x <- sars::sar_pred(Eje_Cafetero_bosque_secundario, area = c(10000, 100000, 1000000, 10000000)) # predict 1ha, 10ha, 100ha, 1000ha
expect_equal(x$Prediction[1], biodiv_area2(1, "Eje Cafetero", "bosque_secundario"))
})
|
52101fc949aad3da2b8c2b328cf2eb271c09ab3d
|
b3affc2e8d14674a9db30759047077fc0ae72301
|
/Fin3380/Fin3380_Quiz4/fin3380_quiz4_mywork.R
|
dc0f43ff09324088a7e6bc7a58976f3630367921
|
[] |
no_license
|
HeyBrawlStar/RstudioPractice
|
57e46ed61ac347dfbf229949e097882d9e77f51c
|
b520c658c93a38b8e1fc06421b6e4b7d641349df
|
refs/heads/master
| 2023-01-24T02:45:09.543682
| 2020-12-06T01:56:11
| 2020-12-06T01:56:11
| 295,978,579
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,042
|
r
|
fin3380_quiz4_mywork.R
|
setwd("/Users/guangyaohe/Desktop/FIN3380/Fin3380_Quiz4")
dj.rsi= read.csv("dj.csv")
dj.rsi = dj.rsi[-which(dj.rsi$dji==0),]
dj.rsi = na.omit(dj.rsi)
library(zoo)
library(dplyr)
dj.rsi = dj.rsi %>%
select(date,dji) %>%
arrange(date) %>%
mutate(date=as.Date(date),
delta=dji-lag(dji,1))
head(dj.rsi,3)
dj.rsi = dj.rsi %>%
mutate(up=ifelse(delta>0,1,0),
down=ifelse(delta<0,1,0))
head(dj.rsi)
dj.rsi = dj.rsi %>%
mutate(up.val=delta*up,
down.val=-delta*down)
head(dj.rsi)
dj.rsi = dj.rsi[2:nrow(dj.rsi),] %>%
mutate(up.first.avg=rollapply(up.val,
width=14,
align='right',
fill=NA,FUN=mean,na.rm=T),
down.first.avg=rollapply(down.val,
width=14,
align='right',
fill=NA,FUN=mean,na.rm=T))
head(dj.rsi,20)
# initialise the up.avg and down.avg with up.first.avg and down.first.avg values
dj.rsi$up.avg =dj.rsi$up.first.avg
dj.rsi$down.avg = dj.rsi$down.first.avg
# fill up.avg and down.avg values with updated value for t>=15
for (t in 15:length(dj.rsi$up.avg)){
dj.rsi$up.avg[t] = dj.rsi$up.avg[t-1]*13/14 + dj.rsi$up.val[t]*1/14
dj.rsi$down.avg[t] = dj.rsi$down.avg[t-1]*13/14 + dj.rsi$down.val[t]*1/14
}
head(dj.rsi,20)
# RS = upside wema/downside wema
# RSI = 100-100/(1+RS)
dj.rsi$rs=dj.rsi$up.avg/dj.rsi$down.avg
dj.rsi$rsi=100-100/(1+dj.rsi$rs)
head(dj.rsi,20)
dj.rsi = dj.rsi %>% filter(substr(date,1,4)=='2006')
plot(dj.rsi$rsi~dj.rsi$date,
ylab='RSI (14-Day Moving Avg)',
xlab='Date',
ylim=c(0,100),
lwd=2,
type='l',
main='DJ: RSI (14-Day Moving Avg)')
abline(h=c(30,70),col='red')
par(new=T)
plot(dj.rsi$dji~dj.rsi$date,
xlab='',
ylab='',
yaxt='n',
type='l',col='lightblue')
legend("topleft",
c('14-Day Moving Avg', 'Moving Avg Abline','dji'),
lty=c(1,1,1),
col=c("black","red",'lightblue'))
axis(4)
|
4badcebdcc2412d9c23d24231a04d033d226fb53
|
dae9dece43c6fe692fdf00e45bcab0129b4f3787
|
/examples/api-key-authentication/1-deploy.R
|
9550337c13a93b6565315e97b4f552b6226cc1d3
|
[] |
no_license
|
Sandy4321/RestRserve
|
31b2f83a47b51bff1be8bc9456cf9ec5f425a291
|
1ee89a0864ea9b67ca581a0ccfe2fa4917978d7b
|
refs/heads/master
| 2020-03-17T04:48:39.978576
| 2018-04-16T05:35:21
| 2018-04-16T05:35:21
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 525
|
r
|
1-deploy.R
|
#!/usr/bin/env Rscript
DIR = "run"
APP = "R/app.R"
conf = c(
"http.tls.port" = "8002",
"tls.key" = normalizePath("run/cert/server.key"),
"tls.cert" = normalizePath("run/cert/server.cert"),
# you may need also put public keys (CA certs) provided by Certificate Authority (CA)
# "tls.ca" = normalizePath("run/cert/server.ca"),
"encoding" = "utf8",
"port" = "6312")
RestRserve::restrserve_deploy(APP, dir = DIR, configuration = conf)
file.copy("api-keys.txt", file.path(DIR, "api-keys.txt"), overwrite = TRUE)
|
8223dee4b4b7a8fdd0f506714cbfbe17ccaf7c48
|
587636c9ce7053359f4e094e835b2c04a0662982
|
/riveredge.R
|
7565c77046f654c0e78a310f50164c2688b6be00
|
[] |
no_license
|
RomeoAlphaYankee/DataScienceR
|
99678099a18ad4acc3d5ed6b43b286e933380e86
|
89fb71d38c235317c4e8c203385d76c72d64f484
|
refs/heads/master
| 2022-05-06T20:01:05.590064
| 2022-03-19T15:36:35
| 2022-03-19T15:36:35
| 219,862,131
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,951
|
r
|
riveredge.R
|
library(dplyr)
library(readr)
library(lubridate)
library(ggplot2)
ti <- read.csv("C:/Users/angel/Downloads/multiTimeline.csv", skip = 2)
head(ti)
names(ti) <- c("week", "data")
ti %>% mutate(date = ymd(week)) %>%
mutate(year = year(date), month = month(date)) %>%
filter(month >= 6, month <= 9) %>%
group_by(month, year) %>%
summarise(mean = mean(data), median = median(data))
ti %>% mutate(date = ymd(week)) %>%
mutate(year = year(date), month = month(date)) %>%
filter(month >= 6, month <= 10) %>%
group_by(month, year) %>%
summarise(mean = mean(data), median = median(data)) %>%
ggplot(aes(x = month, y = mean, fill = factor(year))) +
geom_col(position = "dodge") +
ggtitle("Google Searches for Thousand Islands") +
labs(fill = "Year")
ti %>% mutate(date = ymd(week)) %>%
mutate(year = year(date)) %>%
ggplot(aes(x = date, y = data)) +
geom_line(color = "blue", lwd = 1.25) +
ggtitle("Google Searches for Thousand Islands",
"2020 vs. 2019") +
xlab("Date") +
ylab("Searches")
rr <- read_csv("C:/Users/angel/Downloads/Analytics Riveredge Resort Pages 20171205-20201211.csv", skip = 19)
head(rr)
rr$`Month Index` <- as.numeric(rr$`Month Index`)
tail(rr)
rr$month <- 12
rr$month[-1] <- 1:12
rr <- rr[-nrow(rr), ]
tail(rr)
rr$year <- NA
within(rr, rr$year <- ifelse(rr$Month.Index == 0, 2017,
ifelse(rr$Month.Index >= 1 & <= 12, 2018,
ifelse(rr$Month.Index >= 13 & <= 24, 2019, 2020))))
year <- c(2017, rep(2018, 12), rep(2019, 12), rep(2020, 12))
rr$year <- year
head(rr)
str(rr)
rr %>% filter(month >= 6, month <= 10) %>%
ggplot(aes(x = month, y = Pageviews, fill = factor(year))) +
geom_col(position = "dodge") +
ggtitle("Web Traffic to Riveredge Resort",
"Page Views 2018 - 2020") +
xlab("Month") +
labs(fill = "Year")
|
cd05148169b09b1dd534d7702e38366778068689
|
ada4a328c22ba799276e6e827ff10fc2a17aaab4
|
/vectorized.R
|
cad31db9e0f7435695a196eac43528a122a19637
|
[] |
no_license
|
giannelli/slotmachine
|
aee8fb1ac7cbfb70c3f19df6fba12f84812ba0fb
|
b1fb4e0af9e3f4df1a06f5eb946acc0b4c8863c3
|
refs/heads/master
| 2020-04-01T14:44:30.371705
| 2018-10-25T14:40:08
| 2018-10-25T14:40:08
| 153,306,124
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,477
|
r
|
vectorized.R
|
# Hands-On Programming with R
# Chapter 10 : Speed
# Repeat what we need - should package this up.
get_symbols <- function() {
wheel <- c("DD", "7", "BBB", "BB", "B", "C", "0")
sample(wheel, size = 3, replace = TRUE,
prob = c(0.03, 0.03, 0.06, 0.1, 0.25, 0.01, 0.52))
}
score <- function(symbols) {
diamonds <- sum(symbols == "DD")
cherries <- sum(symbols == "C")
# identify case
# since diamonds are wild, only nondiamonds
# matter for three of a kind and all bars
slots <- symbols[symbols != "DD"]
same <- length(unique(slots)) == 1
bars <- slots %in% c("B", "BB", "BBB")
# assign prize
if (diamonds == 3) {
prize <- 100
} else if (same) {
payouts <- c("7" = 80, "BBB" = 40, "BB" = 25,
"B" = 10, "C" = 10, "0" = 0)
prize <- unname(payouts[slots[1]])
} else if (all(bars)) {
prize <- 5
} else if (cherries > 0) {
# diamonds count as cherries
# so long as there is one real cherry
prize <- c(0, 2, 5)[cherries + diamonds + 1]
} else {
prize <- 0
}
# double for each diamond
prize * 2^diamonds
}
play <- function() {
symbols <- get_symbols()
structure(score(symbols), symbols = symbols, class = "slots")
}
# Repeat what we need - END
abs_loop <- function(vec) {
for (i in 1:length(vec)) {
if (vec[i] < 0) {
vec[i] <- -vec[i]
}
}
vec
}
abs_sets <- function(vec) {
negs <- vec < 0
vec[negs] <- vec[negs] * -1
vec
}
long <- rep(c(-1, 1), 5000000)
length(long)
system.time(abs_loop(long))
system.time(abs_sets(long))
system.time(abs(long))
## page 175
change_symbols <- function(vec){
for (i in 1:length(vec)){
if (vec[i] == "DD") {
vec[i] <- "joker"
} else if (vec[i] == "C") {
vec[i] <- "ace"
} else if (vec[i] == "7") {
vec[i] <- "king"
}else if (vec[i] == "B") {
vec[i] <- "queen"
} else if (vec[i] == "BB") {
vec[i] <- "jack"
} else if (vec[i] == "BBB") {
vec[i] <- "ten"
} else {
vec[i] <- "nine"
}
}
vec
}
vec <- c("DD", "C", "7", "B", "BB", "BBB", "0")
change_symbols(vec)
many <- rep(vec, 1000000)
system.time(change_symbols(many))
change_vec <- function (vec) {
vec[vec == "DD"] <- "joker"
vec[vec == "C"] <- "ace"
vec[vec == "7"] <- "king"
vec[vec == "B"] <- "queen"
vec[vec == "BB"] <- "jack"
vec[vec == "BBB"] <- "ten"
vec[vec == "0"] <- "nine"
vec
}
system.time(change_vec(many))
change_vec2 <- function(vec){
tb <- c("DD" = "joker", "C" = "ace", "7" = "king", "B" = "queen",
"BB" = "jack", "BBB" = "ten", "0" = "nine")
unname(tb[vec])
}
system.time(change_vec2(many))
# page 178
system.time({
output <- rep(NA, 1000000)
for (i in 1:1000000) {
output[i] < i + 1
}
})
system.time({
output <- NA
for (i in 1:1000000) {
output[i] <- i + 1
}
})
system.time({
winnings <- vector(length = 1000000)
for (i in 1:1000000) {
winnings[i] <- play()
}
print(paste("Average win: ", mean(winnings)))
})
## 0.9366984 - very close to calculated expected value worked out in chapter 9
###### Vectorize simulation Page 180 ###############
get_many_symbols <- function(n) {
wheel <- c("DD", "7", "BBB", "BB", "B", "C", "0")
vec <- sample(wheel, size = 3 * n, replace = TRUE,
prob = c(0.03, 0.03, 0.06, 0.1, 0.25, 0.01, 0.52))
matrix(vec, ncol = 3)
}
get_many_symbols(10)
# symbols should be a matrix with a column for each slot machine window
score_many <- function(symbols) {
# Step 1: Assign base prize based on cherries and diamonds ---------
## Count the number of cherries and diamonds in each combination
cherries <- rowSums(symbols == "C")
diamonds <- rowSums(symbols == "DD")
## Wild diamonds count as cherries
prize <- c(0, 2, 5)[cherries + diamonds + 1]
## ...but not if there are zero real cherries
### (cherries is coerced to FALSE where cherries == 0)
prize[!cherries] <- 0
# Step 2: Change prize for combinations that contain three of a kind
same <- symbols[, 1] == symbols[, 2] &
symbols[, 2] == symbols[, 3]
payoffs <- c("DD" = 100, "7" = 80, "BBB" = 40,
"BB" = 25, "B" = 10, "C" = 10, "0" = 0)
prize[same] <- payoffs[symbols[same, 1]]
# Step 3: Change prize for combinations that contain all bars ------
bars <- symbols == "B" | symbols == "BB" | symbols == "BBB"
all_bars <- bars[, 1] & bars[, 2] & bars[, 3] & !same
prize[all_bars] <- 5
# Step 4: Handle wilds ---------------------------------------------
## combos with two diamonds
two_wilds <- diamonds == 2
### Identify the nonwild symbol
one <- two_wilds & symbols[, 1] != symbols[, 2] &
symbols[, 2] == symbols[, 3]
two <- two_wilds & symbols[, 1] != symbols[, 2] &
symbols[, 1] == symbols[, 3]
three <- two_wilds & symbols[, 1] == symbols[, 2] &
symbols[, 2] != symbols[, 3]
### Treat as three of a kind
prize[one] <- payoffs[symbols[one, 1]]
prize[two] <- payoffs[symbols[two, 2]]
prize[three] <- payoffs[symbols[three, 3]]
## combos with one wild
one_wild <- diamonds == 1
### Treat as all bars (if appropriate)
wild_bars <- one_wild & (rowSums(bars) == 2)
prize[wild_bars] <- 5
### Treat as three of a kind (if appropriate)
one <- one_wild & symbols[, 1] == symbols[, 2]
two <- one_wild & symbols[, 2] == symbols[, 3]
three <- one_wild & symbols[, 3] == symbols[, 1]
prize[one] <- payoffs[symbols[one, 1]]
prize[two] <- payoffs[symbols[two, 2]]
prize[three] <- payoffs[symbols[three, 3]]
# Step 5: Double prize for every diamond in combo ------------------
unname(prize * 2^diamonds)
}
play_many <- function(n) {
symb_mat <- get_many_symbols(n = n)
data.frame(w1 = symb_mat[,1], w2 = symb_mat[,2],
w3 = symb_mat[,3], prize = score_many(symb_mat))
}
system.time(play_many(1000000))
## Vectorised scoring - Ten Million!
system.time({
plays <- play_many(10000000)
print(mean(plays$prize))
})
# [1] 0.9371745
# user system elapsed
# 8.706 1.917 10.622
## Non vectorised scoring - 'Only' One Million
system.time({
winnings <- vector(length = 1000000)
for (i in 1:1000000) {
winnings[i] <- play()
}
print(paste("Average win: ", mean(winnings)))
})
# [1] "Average win: 0.93637"
# user system elapsed
# 39.424 0.002 39.420
# #######################
# symbols <- matrix(
# c("DD", "DD", "DD",
# "C", "DD", "0",
# "B", "B", "B",
# "B", "BB", "BBB",
# "C", "C", "0",
# "7", "DD", "DD"), nrow = 6, byrow = TRUE)
# symbols
|
ef78545214e168ab79bfa992f16d007f6af6752b
|
bbcec89cbc999b348f012b091525af29e267c4c8
|
/man/impute_missing_per_quantile.Rd
|
7a99c43f18e3d1571aca2697d9fcab1042b42f46
|
[] |
no_license
|
shinyfluba/covidEnsembles
|
3ec08a70205d145bd0597d75bd1c763b2907f2fb
|
fedbc5c8921afd221ec4584a3bd63152eb4fba8b
|
refs/heads/master
| 2023-04-04T08:10:35.323665
| 2021-03-25T14:49:15
| 2021-03-25T14:49:15
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,102
|
rd
|
impute_missing_per_quantile.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qra_fit_convenience.R
\name{impute_missing_per_quantile}
\alias{impute_missing_per_quantile}
\title{Impute missing values for each quantile level in a quantile forecast matrix
It is assumed that in each row, all quantiles for a given model are either
missing or available.}
\usage{
impute_missing_per_quantile(qfm, impute_method = "mean")
}
\arguments{
\item{qfm}{a QuantileForecastMatrix}
\item{impute_method}{character string specifying method for imputing;
currently only 'mean' is supported}
}
\value{
list of two items:
\enumerate{
\item 'qfm_imputed' the input QuantileForecastMatrix object with missing values
imputed
\item 'weight_transfer' a square matrix of dimension equal to the number of
unique models in qfm. Entry \link{i, j} is the proportion of imputed observations
for model j that are attributable to model i.
}
}
\description{
Impute missing values for each quantile level in a quantile forecast matrix
It is assumed that in each row, all quantiles for a given model are either
missing or available.
}
|
1da95af3a5ba19aadbdbc72210070c89914e8cd5
|
7a7a325a0f01d92a41e7c35278e7f938452fed84
|
/man/YPR.Rd
|
25d842db1edcb43b58ad0fbf15c61f37665bc896
|
[] |
no_license
|
IMPRESSPROJECT/Rfishpop
|
cca94973e345a7841634b5ab4a7ba5fdebf42e24
|
c4055e61b7126d9ab0b4264855f39584405a8a16
|
refs/heads/master
| 2022-08-30T10:35:13.002516
| 2022-08-26T16:51:16
| 2022-08-26T16:51:16
| 252,695,701
| 0
| 5
| null | 2020-11-05T10:49:10
| 2020-04-03T10:04:49
|
R
|
UTF-8
|
R
| false
| true
| 3,069
|
rd
|
YPR.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/YPR.R
\name{YPR}
\alias{YPR}
\title{Yield-per-Recruit}
\usage{
YPR(Pop.Mod, f.grid, Fish.years, Bio.years, plot, Method, par)
}
\arguments{
\item{Pop.Mod}{A list containing the components returned by Population.Modeling function (main function).}
\item{f.grid}{A sequence of fishing efforts.}
\item{Fish.years}{The number of recent years to estimate the mean of SEL (selectivity, see information about such elements in Sum.Pop.Mod function).}
\item{Bio.years}{The number of recent years to estimate the mean of M and WC (natural mortality and catch weight, see information about such elements in Population.Modeling function and Sum.Pop.Mod function).}
\item{plot}{A vector of two elements. The first one is a logical parameter. By default is equal to TRUE, which means that a biomass per recruit graph is done. The second element refers to which iteration must be plotted.}
\item{Method}{The procedure to obtain the age vector of weight (catches), selectivity and natural mortality. By default is "mean" which means that the mean of the last "Bio.years" is used. The alternative option is "own", the user can introduce these elements.}
\item{par}{If Method="own" it is a list containing the matrices whose columns report for each iteration the age vector of weight (catches), natural mortality, and selectivity. In other case is equal to NULL.}
}
\value{
An array whose third dimension corresponds to the iterations. For each iteration the array contains a matrix reporting the yield-per-recruit for a range of overall fishing mortalities.
}
\description{
Return yield-per-Recruit for each iteration.
}
\details{
The function return the yield-per-recruit.
}
\examples{
ctrPop<-list(years=seq(1980,2020,by=1),niter=2,N0=15000,ages=0:15,minFage=2,
maxFage=5,tc=0.5,seed=NULL)
number_ages<-length(ctrPop$ages);number_years<-length(ctrPop$years)
Mvec=c(1,0.6,0.5,0.4,0.35,0.35,0.3,rep(0.3,9))
M<-matrix(rep(Mvec,number_years),ncol = number_years)
colnames(M)<-ctrPop$years
rownames(M)<-ctrPop$ages
ctrBio<-list(M=M,CV_M=0.2, L_inf=20, t0=-0.25, k=0.3, CV_L=0, CV_LC=0, a=6*10^(-6), b=3,
a50_Mat=1, ad_Mat=-0.5,CV_Mat=0)
ctrSEL<-list(type="cte", par=list(cte=0.5),CV_SEL=0)
f=matrix(rep(0.5,number_years),ncol=number_years,nrow=2,byrow=TRUE)
ctrFish<-list(f=f,ctrSEL=ctrSEL)
a_BH=15000; b_BH=50; CV_REC_BH=0
SR<-list(type="BH",par=c(a_BH,b_BH,CV_REC_BH))
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)
f.grid<-seq(0.00,1.5,by=0.01)
YPR(Pop.Mod,f.grid,3,3,plot=c(TRUE,1), Method="mean",par=NULL)
# The following commented lines refers to par argument.
# If par is not NULL must be something like (assuming that WC, M,
# SEL are defined previously).
# par=list(); par$WC<-WC; par$SEL<-SEL; par$M<-M
# YPR(Pop.Mod,f.grid,plot=c(TRUE,1),Method="own",par=par)
}
\author{
\itemize{
\item{Marta Cousido-Rocha}
\item{Santiago Cerviño López}
\item{Maria Grazia Pennino}
}
}
|
5205eaec2c952a1ccc8b0c6679944da2db3ac558
|
71a051ad748a7b22f427edd81d791443f4c90dea
|
/man/rgt.Rd
|
8739b3e6489153a2f337e91aec7428542511d9e3
|
[] |
no_license
|
knausb/GeneticDiff
|
d13c419503591de43979dc1016383770d5094b1e
|
6058edb2bd4e68152b42e80ca2807bfc370204f3
|
refs/heads/master
| 2021-01-01T19:45:04.216663
| 2017-09-24T21:42:58
| 2017-09-24T21:42:58
| 98,671,915
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 4,679
|
rd
|
rgt.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{rgt}
\alias{rgt}
\title{rgt - random genotypes}
\usage{
rgt(nsamp = 4L, nvar = 3L, pphased = as.numeric(c(0.5)),
pploid = as.numeric(c(0, 1)), pallele = as.numeric(c(0.5, 0.5)),
verbose = 0L)
}
\arguments{
\item{nsamp}{number of samples (columns) to simulate}
\item{nvar}{number of variants (rows) to simulate}
\item{pphased}{probability each genotype will be phased}
\item{pploid}{probability each genotype will be a ploidy level}
\item{pallele}{probability of each allele}
\item{verbose}{should verbose output be produced (1) or not (0)}
}
\value{
A matrix with variants (loci) in rows and samples in columns.
}
\description{
Generate a matrix of random genotypes.
}
\details{
The function \code{rgt} generates a matrix of random genotypes.
This matrix consists of loci or variants in rows and samples in columns where the number of loci and samples is arbitrary.
Genotypes can be delimited by either the forward slash (/), for unphased genotypes, or the pipe (|), for phased genotypes.
The matrix of genotypes generated by this function is intended to be compatible with a matrix of genotypes extracted from variant cal format data (VCF).
The alleles are encoded in a zero-based numeric manner consistent with the VCF specification.
Here, 0 is the first (typically the reference) allele, 1 is the first alternate allele, 2 is the second alternate allele, and so on.
While these genotypes are intended to conform to the VCF specification it is hoped they will be generalizable to broader applications.
The parameter \strong{nvar} determines the number of loci or variants (rows) to simulate.
This integer value is arbitrary to accomodate a range of locus counts.
The parameter \strong{nsamp} determines the number of samples (columns) to simulate.
This integer value is arbitrary to accomodate a range of sample sizes.
The parameter \strong{pphased} indicates the probability that a genotype will be phased.
This parameter consists of a vector containing one element ranging in value from zero to one and describes the probability an individual genotype will be phased.
For example, \code{rgt(pphased=c(0))} will produce genotypes that have a probability of zero for being phased.
The code \code{rgt(pphased=c(1))} will produce genotypes that have a probability of one for being phased.
The code \code{rgt(pphased=c(0.2))} will produce genotypes that have a probability of 0.2 that they will be phased.
The probability for each genotype returned is calculated independently.
The parameter \strong{pploid} indicates the probability a genotype will be of a particular ploidy or copy number.
This is an n-element vector where each element indicates the probability a genotype will be n-ploid.
For example, \code{rgt(pploid=c(1))} will produce genotypes that have a probability of 1 for being one-ploid (haploid).
The code \code{rgt(pploid=c(0,1))} will produce genotypes that have a probability of 0 for being one-ploid and a probability of 1 for being two-ploid (diploid).
The code \code{rgt(pploid=c(0,0,1))} will produce genotypes that have a probability of 0 for being one-ploid, a probability of 0 for being two-ploid (diploid), and a probability of 1 for being three-ploid (triploid).
The code \code{rgt(pploid=c(0.1,0.5,0.4))} will produce genotypes that have a probability of 0.1 for being one-ploid, 0.5 for being two-ploid, and 0.4 for being three-ploid.
The values in this vector are of arbitrary length but should sum to one.
The parameter \strong{pallele} indicates the probability of each allele to occur in a genotype.
This is an n-element vector where each element indicates the probability of the nth allele occuring.
Here the first element is the probability of the 0th allele.
For example, \code{rgt(pallele = c(1))} will produce genotypes where the 0th allele (0) will occur with a probability of 1.
The code \code{rgt(pallele = c(0,1))} will produce genotypes where the 0th allele (0) will occur with a probability of 0 and the 1st allele will occur with a probability of 1.
The code \code{rgt(pallele = c(0,0,1))} will produce genotypes where the 0th allele (0) will occur with a probability of 0, the 1st allele will occur with a probability of 0, and the 2nd allele will occur with a probability of 1.
The code \code{rgt(pallele = c(0.2,0.7,0.1))} will produce genotypes where the 0th allele (0) will occur with a probability of 0.2, the 1st allele will occur with a probability of 0.7, and the 2nd allele will occur with a probability of 0.1.
The values in this vector are of arbitrary length but should sum to one.
}
\examples{
rgt()
}
|
4ea988c024b617ee7e4eafd64de8367118d3b821
|
ee79ad37d2885aac5f5cecb9eea61fafee117a11
|
/simultaneous-selection/analysis/analyses_rep/model_4.R
|
0fd970f658dc8c32c61792ad06ee193ef7d9609b
|
[] |
no_license
|
vullab/ColorBinding
|
d33a3609b89f0916a026cae846742e1f065938ea
|
b3c6e718f1233ad9b4568a51f794cf3b5966145f
|
refs/heads/master
| 2021-03-16T10:26:02.634673
| 2018-12-16T04:04:29
| 2018-12-16T04:04:29
| 65,759,803
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,521
|
r
|
model_4.R
|
library("VGAM")
#library(R.basic)
#looking good, debatable whether or not should do the whole color or flipping - whole color should not rely on part maybe?
#need seperate colors for asymetrical bars
rotate90<- function(x) {
t(mirror.matrix(x))
}
# Rotate matrix 180 clockworks
rotate180 <- function(x) {
xx <- rev(x);
dim(xx) <- dim(x);
xx;
}
# Rotate matrix 270 clockworks
rotate270 <- function(x) {
mirror.matrix(t(x))
}
fitBindingModel <- function(x,data,plot = FALSE){
if (all(x>=0) & all(x[0:3] <=1)){
#fill out paramters
# p_whole_item = x[1]
# p_part_A = x[2]
# p_part_B = x[3]
# p_color_A = x[4]
# p_color_B = x[5]
# sd_whole = x[6]
# sd_part_A = x[7]
# sd_part_B = x[8]
# sd_color_A = x[9]
# sd_color_B = x[10]
# p_whole_item = x[1]
# p_part_A = x[2]
# p_part_B = x[2]
# p_color_A = x[3]
# p_color_B = x[3]
# sd_whole = x[4]
# sd_part_A = x[5]
# sd_part_B = x[5]
# sd_color_A = x[6]
# sd_color_B = x[6]
p_whole_item = x[1]
p_part_A = x[2]
p_part_B = x[2]
p_color_A = x[3]
p_color_B = x[3]
sd_whole = x[4]
sd_part_A = x[5]
sd_part_B = x[5]
sd_color_A = x[6]
sd_color_B = x[6]
prob_flip = 0
# (p_part_A + p_part_B) <= 1 &
if ( (p_color_A + p_color_B) <= 1){
#get the model fit
fit <- runBindingModel(p_whole_item,
p_part_A,
p_part_B,
p_color_A,
p_color_B,
sd_whole,
sd_part_A,
sd_part_B,
sd_color_A,
sd_color_B,
prob_flip)
#find the residual
residual = fit - data
if (plot){
quartz()
image(sqrt(matrix(rev(fit),nrow=10,byrow=TRUE)[(10:1),] ),col=gray((0:128)/128))
quartz()
image(matrix(rev(residual),nrow=10,byrow=TRUE)[(10:1),] ,col=gray((0:128)/128))
}
}else{
residual = 10000
}
}else{
residual = 10000
}
return(sum(residual^2))
}
fitBindingModel2 <- function(x,data,plot = FALSE){
if (all(x>=0) & all(x[0:3] <=1) & all(x[7] <=1)){
#fill out paramters
# p_whole_item = x[1]
# p_part_A = x[2]
# p_part_B = x[3]
# p_color_A = x[4]
# p_color_B = x[5]
# sd_whole = x[6]
# sd_part_A = x[7]
# sd_part_B = x[8]
# sd_color_A = x[9]
# sd_color_B = x[10]
# p_whole_item = x[1]
# p_part_A = x[2]
# p_part_B = x[2]
# p_color_A = x[3]
# p_color_B = x[3]
# sd_whole = x[4]
# sd_part_A = x[5]
# sd_part_B = x[5]
# sd_color_A = x[6]
# sd_color_B = x[6]
p_whole_item = x[1]
p_part_A = x[2]
p_part_B = x[2]
p_color_A = x[3]
p_color_B = x[3]
sd_whole = x[4]
sd_part_A = x[5]
sd_part_B = x[5]
sd_color_A = x[6]
sd_color_B = x[6]
prob_flip = x[7]
# (p_part_A + p_part_B) <= 1 &
if ( (p_color_A + p_color_B) <= 1){
#get the model fit
fit <- runBindingModel(p_whole_item,
p_part_A,
p_part_B,
p_color_A,
p_color_B,
sd_whole,
sd_part_A,
sd_part_B,
sd_color_A,
sd_color_B,
prob_flip)
#find the residual
residual = fit - data
if (plot){
quartz()
image(sqrt(matrix(rev(fit),nrow=10,byrow=TRUE)[(10:1),] ),col=gray((0:128)/128))
quartz()
image(matrix(rev(residual),nrow=10,byrow=TRUE)[(10:1),] ,col=gray((0:128)/128))
}
}else{
residual = 10000
}
}else{
residual = 10000
}
return(sum(residual^2))
}
runBindingModel <- function(p_whole_item,
p_part_A,
p_part_B,
p_color_A,
p_color_B,
sd_whole,
sd_part_A,
sd_part_B,
sd_color_A,
sd_color_B,
prob_flip){
prob_norm <- function(x){ return(x/sum(x))}
#browser()
#laplace for each level
whole_prob<- prob_norm(dlaplace(-2:2,0,sd_whole))
partA_prob<- prob_norm(dlaplace(-2:2,0,sd_part_A))
partB_prob<- prob_norm(dlaplace(-2:2,0,sd_part_B))
colorA_prob<- prob_norm(dlaplace(-2:2,0,sd_color_A))
colorB_prob<- prob_norm(dlaplace(-2:2,0,sd_color_B))
guess_prob<- rep(1/10,10)
#set up individual matricies
whole_matrix <- matrix(0, nrow = 10, ncol = 10)
whole_swap_matrix <- matrix(0, nrow = 10, ncol = 10)
Apart_Bpart_matrix <- matrix(0, nrow = 10, ncol = 10)
Apart_Acolor_matrix <- matrix(0, nrow = 10, ncol = 10)
Apart_Bcolor_matrix <- matrix(0, nrow = 10, ncol = 10)
Apart_G_matrix <- matrix(0, nrow = 10, ncol = 10)
Acolor_Bpart_matrix <- matrix(0, nrow = 10, ncol = 10)
Acolor_Acolor_matrix <- matrix(0, nrow = 10, ncol = 10)
Acolor_Bcolor_matrix <- matrix(0, nrow = 10, ncol = 10)
Acolor_DR_matrix <- matrix(0, nrow = 10, ncol = 10)
Bcolor_Bpart_matrix <- matrix(0, nrow = 10, ncol = 10)
Bcolor_Acolor_matrix <- matrix(0, nrow = 10, ncol = 10)
Bcolor_Bcolor_matrix <- matrix(0, nrow = 10, ncol = 10)
Bcolor_DR_matrix <- matrix(0, nrow = 10, ncol = 10)
G_Bpart_matrix <- matrix(0, nrow = 10, ncol = 10)
#Acolor_DR_matrix <- matrix(0, nrow = 10, ncol = 10)
#Bcolor_DR_matrix <- matrix(0, nrow = 10, ncol = 10)
GG_matrix <- matrix(0, nrow = 10, ncol = 10)
Aoptions<- 1:5
Boptions<- 6:10
#A is rows, B is columns
whole_matrix[Aoptions,Boptions] <- diag(whole_prob)
Apart_Bpart_matrix[Aoptions,Boptions] <- partA_prob %*% t(partB_prob)
Apart_Acolor_matrix[Aoptions,Aoptions] <- partA_prob %*% t(colorA_prob)
Apart_Bcolor_matrix[Aoptions,Boptions] <- partA_prob %*% t(colorB_prob)
Apart_G_matrix[Aoptions,1:10] <- partA_prob %*% t(guess_prob)
Acolor_Bpart_matrix[Aoptions,Boptions] <- colorA_prob %*% t(partB_prob)
Acolor_Acolor_matrix[Aoptions,Aoptions] <- colorA_prob %*% t(colorA_prob)
Acolor_Bcolor_matrix[Aoptions,Boptions] <- colorA_prob %*% t(colorB_prob)
Acolor_DR_matrix[Aoptions,Aoptions] <- diag(colorA_prob)
Bcolor_Bpart_matrix[Boptions,Boptions] <- colorB_prob %*% t(partB_prob)
Bcolor_Acolor_matrix[Boptions,Aoptions] <- colorB_prob %*% t(colorA_prob)
Bcolor_Bcolor_matrix[Boptions,Boptions] <- colorB_prob %*% t(colorB_prob)
Bcolor_DR_matrix[Boptions,Boptions] <- diag(colorB_prob)
G_Bpart_matrix[1:10,Boptions]<- guess_prob %*% t(partB_prob)
GG_matrix[1:10,1:10]<- guess_prob %*% t(guess_prob)
#only used if considering the possiblity of whole with no parts
whole_swap_matrix[Boptions,Aoptions] <- diag(whole_prob)
#browser()
#if you want to allow whole colors only.
#browser()
#putting everything together
#get the whole thing
finalMatrix <- p_whole_item * whole_matrix +
#p_whole_item * (1 - (1 - p_part_A) * (1 - p_part_B)) * whole_matrix +
#p_whole_item * ((1 - p_part_A) * (1 - p_part_B)) * whole_swap_matrix +
#dont get the whole thing
(1 - p_whole_item) * (
#get part A
(p_part_A * p_part_B) * Apart_Bpart_matrix + # TFL-Sample parts
(1-p_part_B) * ( # TFL-If you don't remember part B then
(p_part_A * p_color_A) * Apart_Acolor_matrix + # TFL-Guess an A color
(p_part_A * p_color_B) * Apart_Bcolor_matrix + # TFL-Guess a B color
(p_part_A * (1 - p_color_A - p_color_B)) * Apart_G_matrix ) + # TFL-Or just guess
#dont get part A
(1-p_part_A) * (
(p_color_A * p_part_B) * Acolor_Bpart_matrix + # TFL-Guess A color for A
(1-p_part_B) * ( # TFL-If you don't remember B part but remember color of A
(p_color_A * p_color_B) * Acolor_Acolor_matrix + # TFL-Guess an A color
(p_color_A * p_color_B) * Acolor_Bcolor_matrix + # TFL-Guess a B color
(p_color_A * (1 - p_color_A - p_color_B)) * Acolor_DR_matrix ) + # TFL--Guess any color
(p_color_B * p_part_B) * Bcolor_Bpart_matrix + # TFL-Guess a B color for A
(1-p_part_B) * (
(p_color_B * p_color_A) * Bcolor_Acolor_matrix +
(p_color_B * p_color_B) * Bcolor_Bcolor_matrix +
(p_color_B * (1 - p_color_A - p_color_B)) * Bcolor_DR_matrix ) +
((1 - p_color_A - p_color_B) * p_part_B) * G_Bpart_matrix + # TFL-Guess color for A
(1-p_part_B) * (
((1 - p_color_A - p_color_B) * p_color_A) * Acolor_DR_matrix +
((1 - p_color_A - p_color_B) * p_color_B) * Bcolor_DR_matrix +
((1 - p_color_A - p_color_B) * (1 - p_color_A - p_color_B)) * GG_matrix )))
return((1-prob_flip) * finalMatrix + prob_flip * rotate180(t(rotate180(finalMatrix))) )
}
#image(sqrt(matrix(rev(finalMatrix),nrow=10,byrow=TRUE)[(10:1),] ),col=gray((0:128)/128))
|
27d6676a6eb9f4e5567cda6d62bd83799736d1e9
|
5ec06dab1409d790496ce082dacb321392b32fe9
|
/clients/r/generated/R/ComDayCqDamIdsImplIDSPoolManagerImplProperties.r
|
476ec884ca2009a97902c7874aa3d646013c8e38
|
[
"Apache-2.0"
] |
permissive
|
shinesolutions/swagger-aem-osgi
|
e9d2385f44bee70e5bbdc0d577e99a9f2525266f
|
c2f6e076971d2592c1cbd3f70695c679e807396b
|
refs/heads/master
| 2022-10-29T13:07:40.422092
| 2021-04-09T07:46:03
| 2021-04-09T07:46:03
| 190,217,155
| 3
| 3
|
Apache-2.0
| 2022-10-05T03:26:20
| 2019-06-04T14:23:28
| null |
UTF-8
|
R
| false
| false
| 8,237
|
r
|
ComDayCqDamIdsImplIDSPoolManagerImplProperties.r
|
# Adobe Experience Manager OSGI config (AEM) API
#
# Swagger AEM OSGI is an OpenAPI specification for Adobe Experience Manager (AEM) OSGI Configurations API
#
# OpenAPI spec version: 1.0.0-pre.0
# Contact: opensource@shinesolutions.com
# Generated by: https://openapi-generator.tech
#' ComDayCqDamIdsImplIDSPoolManagerImplProperties Class
#'
#' @field max.errors.to.blacklist
#' @field retry.interval.to.whitelist
#' @field connect.timeout
#' @field socket.timeout
#' @field process.label
#' @field connection.use.max
#'
#' @importFrom R6 R6Class
#' @importFrom jsonlite fromJSON toJSON
#' @export
ComDayCqDamIdsImplIDSPoolManagerImplProperties <- R6::R6Class(
'ComDayCqDamIdsImplIDSPoolManagerImplProperties',
public = list(
`max.errors.to.blacklist` = NULL,
`retry.interval.to.whitelist` = NULL,
`connect.timeout` = NULL,
`socket.timeout` = NULL,
`process.label` = NULL,
`connection.use.max` = NULL,
initialize = function(`max.errors.to.blacklist`, `retry.interval.to.whitelist`, `connect.timeout`, `socket.timeout`, `process.label`, `connection.use.max`){
if (!missing(`max.errors.to.blacklist`)) {
stopifnot(R6::is.R6(`max.errors.to.blacklist`))
self$`max.errors.to.blacklist` <- `max.errors.to.blacklist`
}
if (!missing(`retry.interval.to.whitelist`)) {
stopifnot(R6::is.R6(`retry.interval.to.whitelist`))
self$`retry.interval.to.whitelist` <- `retry.interval.to.whitelist`
}
if (!missing(`connect.timeout`)) {
stopifnot(R6::is.R6(`connect.timeout`))
self$`connect.timeout` <- `connect.timeout`
}
if (!missing(`socket.timeout`)) {
stopifnot(R6::is.R6(`socket.timeout`))
self$`socket.timeout` <- `socket.timeout`
}
if (!missing(`process.label`)) {
stopifnot(R6::is.R6(`process.label`))
self$`process.label` <- `process.label`
}
if (!missing(`connection.use.max`)) {
stopifnot(R6::is.R6(`connection.use.max`))
self$`connection.use.max` <- `connection.use.max`
}
},
toJSON = function() {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject <- list()
if (!is.null(self$`max.errors.to.blacklist`)) {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject[['max.errors.to.blacklist']] <- self$`max.errors.to.blacklist`$toJSON()
}
if (!is.null(self$`retry.interval.to.whitelist`)) {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject[['retry.interval.to.whitelist']] <- self$`retry.interval.to.whitelist`$toJSON()
}
if (!is.null(self$`connect.timeout`)) {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject[['connect.timeout']] <- self$`connect.timeout`$toJSON()
}
if (!is.null(self$`socket.timeout`)) {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject[['socket.timeout']] <- self$`socket.timeout`$toJSON()
}
if (!is.null(self$`process.label`)) {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject[['process.label']] <- self$`process.label`$toJSON()
}
if (!is.null(self$`connection.use.max`)) {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject[['connection.use.max']] <- self$`connection.use.max`$toJSON()
}
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject
},
fromJSON = function(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesJson) {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject <- jsonlite::fromJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesJson)
if (!is.null(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$`max.errors.to.blacklist`)) {
max.errors.to.blacklistObject <- ConfigNodePropertyInteger$new()
max.errors.to.blacklistObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$max.errors.to.blacklist, auto_unbox = TRUE))
self$`max.errors.to.blacklist` <- max.errors.to.blacklistObject
}
if (!is.null(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$`retry.interval.to.whitelist`)) {
retry.interval.to.whitelistObject <- ConfigNodePropertyInteger$new()
retry.interval.to.whitelistObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$retry.interval.to.whitelist, auto_unbox = TRUE))
self$`retry.interval.to.whitelist` <- retry.interval.to.whitelistObject
}
if (!is.null(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$`connect.timeout`)) {
connect.timeoutObject <- ConfigNodePropertyInteger$new()
connect.timeoutObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$connect.timeout, auto_unbox = TRUE))
self$`connect.timeout` <- connect.timeoutObject
}
if (!is.null(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$`socket.timeout`)) {
socket.timeoutObject <- ConfigNodePropertyInteger$new()
socket.timeoutObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$socket.timeout, auto_unbox = TRUE))
self$`socket.timeout` <- socket.timeoutObject
}
if (!is.null(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$`process.label`)) {
process.labelObject <- ConfigNodePropertyString$new()
process.labelObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$process.label, auto_unbox = TRUE))
self$`process.label` <- process.labelObject
}
if (!is.null(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$`connection.use.max`)) {
connection.use.maxObject <- ConfigNodePropertyInteger$new()
connection.use.maxObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$connection.use.max, auto_unbox = TRUE))
self$`connection.use.max` <- connection.use.maxObject
}
},
toJSONString = function() {
sprintf(
'{
"max.errors.to.blacklist": %s,
"retry.interval.to.whitelist": %s,
"connect.timeout": %s,
"socket.timeout": %s,
"process.label": %s,
"connection.use.max": %s
}',
self$`max.errors.to.blacklist`$toJSON(),
self$`retry.interval.to.whitelist`$toJSON(),
self$`connect.timeout`$toJSON(),
self$`socket.timeout`$toJSON(),
self$`process.label`$toJSON(),
self$`connection.use.max`$toJSON()
)
},
fromJSONString = function(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesJson) {
ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject <- jsonlite::fromJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesJson)
ConfigNodePropertyIntegerObject <- ConfigNodePropertyInteger$new()
self$`max.errors.to.blacklist` <- ConfigNodePropertyIntegerObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$max.errors.to.blacklist, auto_unbox = TRUE))
ConfigNodePropertyIntegerObject <- ConfigNodePropertyInteger$new()
self$`retry.interval.to.whitelist` <- ConfigNodePropertyIntegerObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$retry.interval.to.whitelist, auto_unbox = TRUE))
ConfigNodePropertyIntegerObject <- ConfigNodePropertyInteger$new()
self$`connect.timeout` <- ConfigNodePropertyIntegerObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$connect.timeout, auto_unbox = TRUE))
ConfigNodePropertyIntegerObject <- ConfigNodePropertyInteger$new()
self$`socket.timeout` <- ConfigNodePropertyIntegerObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$socket.timeout, auto_unbox = TRUE))
ConfigNodePropertyStringObject <- ConfigNodePropertyString$new()
self$`process.label` <- ConfigNodePropertyStringObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$process.label, auto_unbox = TRUE))
ConfigNodePropertyIntegerObject <- ConfigNodePropertyInteger$new()
self$`connection.use.max` <- ConfigNodePropertyIntegerObject$fromJSON(jsonlite::toJSON(ComDayCqDamIdsImplIDSPoolManagerImplPropertiesObject$connection.use.max, auto_unbox = TRUE))
}
)
)
|
0cd70364ce97e4007ebc740ec25403115d0b0554
|
16e799f7242507c6168cbea9c2da9c7522d36a85
|
/assignments/fishery_stats.R
|
932ff2a696ab11ebe38485b8e79ee2cd787c18d3
|
[] |
no_license
|
eristig/ESM262-1
|
6fc035328563d684da502fab3d6e4c85f73ccdec
|
b2414d073f624424f312eeca0d69a3ff91ae4310
|
refs/heads/master
| 2021-02-19T03:47:50.641382
| 2020-03-05T21:45:21
| 2020-03-05T21:45:21
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 934
|
r
|
fishery_stats.R
|
### Assignment 2: Fishing
# create vector of possible fish
possible.fish = c("salmon","steelhead","shark","tuna","cod")
possible.locations = c("A","B","C","D","E")
fish.prices = runif(min=5, max=10, n=length(possible.fish))
# we can use sample to simulate a random recording of catch by fisherman, lets say we pick 20 fish from the net
prices = data.frame(fish = possible.fish, price = fish.prices)
catch_reef = sample(possible.fish, size=50, prob = c(0.2, 0.2, 0.1, 0.1, 0.4), replace=T)
catch_ocean = sample(possible.fish, size=50, prob = c(0.4, 0.2, 0.1, 0.1, 0.2), replace=T)
catch= data.frame(catch_reef, catch_ocean)
catch
fishery_stats = function(catch) {
data = as.data.frame(matrix(nrow=ncol(catch), ncol=3))
for(i in 1:ncol(catch)){
data[i,1] = colnames(catch)[i]
data[i,2] = names(which.max(summary(catch[[i]])))
data[i,3] = max(summary(catch[[i]]))
}
return(data)
}
fishery_stats(catch)
|
15092a39aa8e57bd5cf78d620f36cf5116aab83e
|
8114393f498302505eb893a8f3f62b5200c65fe5
|
/man/predict.CauseSpecificCox.Rd
|
2294479d8ab53787af0a5aa2fd5a5135bb84be22
|
[] |
no_license
|
PablitoCho/riskRegression
|
838fae9645819eaa0aa5fc51f9b462c22ccc84ad
|
d5e995d991a2de2196ab9751b69b606fea347743
|
refs/heads/master
| 2020-12-13T19:57:41.632436
| 2017-06-26T12:37:23
| 2017-06-26T12:37:23
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 4,661
|
rd
|
predict.CauseSpecificCox.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.CauseSpecificCox.R
\name{predict.CauseSpecificCox}
\alias{predict.CauseSpecificCox}
\title{Predicting absolute risk from cause-specific Cox models}
\usage{
\method{predict}{CauseSpecificCox}(object, newdata, times, cause,
landmark = NA, keep.times = 1L, keep.newdata = 1L, keep.strata = 1L,
se = FALSE, band = FALSE, iid = FALSE, average.iid = FALSE,
nSim.band = 10000, logTransform = FALSE, productLimit = TRUE,
conf.level = 0.95, store.iid = "full", ...)
}
\arguments{
\item{object}{The fitted cause specific Cox model}
\item{newdata}{A data frame containing the values of the variables
in the right hand side of 'coxph' for each subject.}
\item{times}{Vector of times at which to return the estimated
absolute risk.}
\item{cause}{Identifies the cause of interest among the competing
events.}
\item{landmark}{the starting time for the computation of the cumulative risk}
\item{keep.times}{Logical. If \code{TRUE} add the evaluation times
to the output.}
\item{keep.newdata}{Logical. If \code{TRUE} add the value of the covariates used to make the prediction in the output list.}
\item{keep.strata}{Logical. If \code{TRUE} add the value of the strata used to make the prediction in the output list.}
\item{se}{Logical. If \code{TRUE} add the standard errors to the output.}
\item{band}{Logical. If \code{TRUE} add the confidence band to the output.}
\item{iid}{Logical. If \code{TRUE} add the influence function to the output.}
\item{average.iid}{Logical. If \code{TRUE} add the average of the influence function over \code{newdata} to the output.}
\item{nSim.band}{the number of simulations used to compute the quantiles
for the confidence bands.}
\item{logTransform}{Should the confidence intervals/bands be computed on the
log(-log) scale and be backtransformed.
Otherwise they are computed on the original scale and truncated (if necessary).}
\item{productLimit}{Logical. If true the survival is computed using the product limit estimator.
Otherwise the exponential approximation is used (i.e. exp(-cumulative hazard)).}
\item{conf.level}{Level of confidence.}
\item{store.iid}{Implementation used to estimate the influence function and the standard error.
Can be \code{"full"} or \code{"minimal"}. See the details section of \code{\link{calcSeCSC}}.}
\item{...}{not used}
}
\value{
A list containing:
\itemize{
\item{absRisk}: (data table) the predictions for each subject (in rows) and each time (in columns).
\item{absRisk.se}: (data table) the standard errors of the predictions.
\item(absRisk.iid): (array) the value of the influence of each subject used to fit the object (dim 3)
for each subject in newdata (dim 1) and each time (dim 2).
\item{times}: (vector) the evaluation times.
}
}
\description{
Apply formula to combine two or more Cox models into absolute risk (cumulative incidence function)
}
\details{
Note: for Cox regression models with time varying
covariates it does not make sense to use this function, because
the predicted risk has to be a measurable function of the data
available at the time origin.
When setting \code{logTransform} to \code{TRUE}, the standard error that is returned is
before back-transformation to the original scale.
}
\examples{
set.seed(5)
d <- sampleData(80,outcome="comp")
nd <- sampleData(4,outcome="comp")
d$time <- round(d$time,1)
ttt <- sort(sample(x = unique(d$time), size = 10))
# coxph function
CSC.fit <- CSC(Hist(time,event)~ X3+X8,data=d, method = "breslow")
x= predict(CSC.fit,newdata=nd,times=1:10,cause=1,se=1L)
px=print(x)
px
x2 = predict(CSC.fit,newdata=nd,times=1:10,cause=1,se=1L,
logTransform = TRUE)
predCSC <- predict(CSC.fit, newdata = d, cause = 2, times = ttt)
predCSC.se <- predict(CSC.fit, newdata = d[1:5,], cause = 2, times = ttt,
se = TRUE,keep.newdata=TRUE)
predCSC.iid <- predict(CSC.fit, newdata = d[1:5,],
cause = 2, times = ttt, iid = TRUE)
# predCSC.se$absRisk.se
# sqrt(apply(predCSC.iid$absRisk.iid[,1,]^2,1,function(x){sum(x)}))
## strata
CSC.fit.s <- CSC(list(Hist(time,event)~ strata(X1)+X2+X9,
Hist(time,event)~ X2+strata(X4)+X8+X7),data=d, method = "breslow")
predict(CSC.fit.s,cause=1,times=ttt,se=1L)
# cph function
CSC.cph <- CSC(Hist(time,event)~ X1+X2,data=d, method = "breslow", fitter = "cph")
predict(CSC.cph, newdata = d, cause = 2, times = ttt)
# landmark analysis
T0 <- 1
predCSC_afterT0 <- predict(CSC.fit, newdata = d, cause = 2, times = ttt[ttt>T0], landmark = T0)
predCSC_afterT0
}
\author{
Brice Ozenne broz@sund.ku.dk, Thomas A. Gerds
tag@biostat.ku.dk
}
|
4ef4c7fdf9dd047830dbd312f6c61c0b72201829
|
5d1e0788c8732862077768cf3bf27e2e1dd9affa
|
/plot1.R
|
bf500f6c7bafa03202127dea921d4eeb1159652f
|
[] |
no_license
|
trier257/ExData_Plotting1
|
2fe346ca3b676353edf4fec97e0e7726c7155c6b
|
d86572b2a800942b9f34c75beecdca90e78c0871
|
refs/heads/master
| 2020-11-30T16:17:27.282069
| 2014-06-07T01:03:16
| 2014-06-07T01:03:16
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,373
|
r
|
plot1.R
|
# Read in the data.
# This code requires the zip file be downloaded and unzipped to the current directory.
# The data is big but not so large that it won't fit in my PC's memory.
allData <- read.table("household_power_consumption.txt", sep=";", header=TRUE, stringsAsFactors=FALSE, na.strings="?")
# The assignment only deals with the data for Feb 1, 2007 to end of Feb 2, 2007.
# Need to convert data's dates, then subset with them.
# Convert character date and time to a date-time value using strptime.
# http://www.stat.berkeley.edu/classes/s133/dates.html
allData$DateTime <- paste(allData$Date, allData$Time)
allData$DateTime <- strptime(allData$DateTime, format="%d/%m/%Y %H:%M:%S")
# Subset the data to only required days.
# For plot 1 only need the Global_active_power column (column 3).
bDate <- strptime("01/02/2007 00:00:00", format="%d/%m/%Y %H:%M:%S")
eDate <- strptime("03/02/2007 00:00:00", format="%d/%m/%Y %H:%M:%S")
gapData <- subset(allData, DateTime >= bDate & DateTime < eDate, select=c(3,10))
# Open the PNG device with the required 480x480.
# Note the background of the professor's figure is transparent.
png(file="plot1.png", bg="transparent", width=480, height=480)
# Create a histogram to match the assignment's plot 1
hist(gapData$Global_active_power, main="Global Active Power", xlab="Global Active Power (kilowatts)", col="red")
dev.off()
|
4e4673d5a60f39c6d5380e15e7b77c6314da205c
|
c4eceeca4f0ef6f9aa3cc803c50db867da22fc12
|
/R/hgrm2.R
|
3eb43db34adace4a8cfbc46ae997b926034b6c38
|
[] |
no_license
|
xiangzhou09/hIRT
|
f55191377777aa8e59d21d1c6cef6349d7e3b284
|
b33d90f13287f60f7feffd0c061858340dff40a6
|
refs/heads/master
| 2021-12-30T05:05:26.010542
| 2021-12-14T21:14:23
| 2021-12-14T21:14:23
| 97,876,422
| 8
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,011
|
r
|
hgrm2.R
|
#' Hierarchical Graded Response Models with Known Item Parameters
#'
#' \code{hgrm2} fits a hierarchical graded response model where the item parameters
#' are known and supplied by the user.
#'
#' @param y A data frame or matrix of item responses.
#' @param x An optional model matrix, including the intercept term, that predicts the
#' mean of the latent preference. If not supplied, only the intercept term is included.
#' @param z An optional model matrix, including the intercept term, that predicts the
#' variance of the latent preference. If not supplied, only the intercept term is included.
#' @param item_coefs A list of known item parameters. The parameters of item \eqn{j} are given
#' by the \eqn{j}th element, which should be a vector of length \eqn{H_j}, containing
#' \eqn{H_j - 1} item difficulty parameters (in descending order) and one item discrimination
#' parameter.
#' @param control A list of control values
#' \describe{
#' \item{max_iter}{The maximum number of iterations of the EM algorithm.
#' The default is 150.}
#' \item{eps}{Tolerance parameter used to determine convergence of the
#' EM algorithm. Specifically, iterations continue until the Euclidean
#' distance between \eqn{\beta_{n}} and \eqn{\beta_{n-1}} falls under \code{eps},
#' where \eqn{\beta} is the vector of item discrimination parameters.
#' \code{eps}=1e-4 by default.}
#' \item{max_iter2}{The maximum number of iterations of the conditional
#' maximization procedures for updating \eqn{\gamma} and \eqn{\lambda}.
#' The default is 15.}
#' \item{eps2}{Tolerance parameter used to determine convergence of the
#' conditional maximization procedures for updating \eqn{\gamma} and
#' \eqn{\lambda}. Specifically, iterations continue until the Euclidean
#' distance between two consecutive log likelihoods falls under \code{eps2}.
#' \code{eps2}=1e-3 by default.}
#' \item{K}{Number of Gauss-Legendre quadrature points for the E-step. The default is 21.}
#' \item{C}{[-C, C] sets the range of integral in the E-step. \code{C}=3 by default.}
#' }
#'
#' @return An object of class \code{hgrm}.
#' \item{coefficients}{A data frame of parameter estimates, standard errors,
#' z values and p values.}
#' \item{scores}{A data frame of EAP estimates of latent preferences and
#' their approximate standard errors.}
#' \item{vcov}{Variance-covariance matrix of parameter estimates.}
#' \item{log_Lik}{The log-likelihood value at convergence.}
#' \item{N}{Number of units.}
#' \item{J}{Number of items.}
#' \item{H}{A vector denoting the number of response categories for each item.}
#' \item{ylevels}{A list showing the levels of the factorized response categories.}
#' \item{p}{The number of predictors for the mean equation.}
#' \item{q}{The number of predictors for the variance equation.}
#' \item{control}{List of control values.}
#' \item{call}{The matched call.}
#' @importFrom rms lrm.fit
#' @importFrom pryr compose
#' @importFrom pryr partial
#' @import stats
#' @export
#' @examples
#'
#' y <- nes_econ2008[, -(1:3)]
#' x <- model.matrix( ~ party * educ, nes_econ2008)
#' z <- model.matrix( ~ party, nes_econ2008)
#'
#' n <- nrow(nes_econ2008)
#' id_train <- sample.int(n, n/4)
#' id_test <- setdiff(1:n, id_train)
#'
#' y_train <- y[id_train, ]
#' x_train <- x[id_train, ]
#' z_train <- z[id_train, ]
#'
#' mod_train <- hgrm(y_train, x_train, z_train)
#'
#' y_test <- y[id_test, ]
#' x_test <- x[id_test, ]
#' z_test <- z[id_test, ]
#'
#' item_coefs <- lapply(coef_item(mod_train), `[[`, "Estimate")
#'
#' model_test <- hgrm2(y_test, x_test, z_test, item_coefs = item_coefs)
hgrm2 <- function(y, x = NULL, z = NULL, item_coefs, control = list()) {
# match call
cl <- match.call()
# check y and convert y into data.frame if needed
if(missing(y)) stop("`y` must be provided.")
if ((!is.data.frame(y) && !is.matrix(y)) || ncol(y) == 1L)
stop("'y' must be either a data.frame or a matrix with at least two columns.")
if(is.matrix(y)) y <- as.data.frame(y)
# number of units and items
N <- nrow(y)
J <- ncol(y)
# convert each y_j into an integer vector
y[] <- lapply(y, factor, exclude = c(NA, NaN))
ylevels <- lapply(y, levels)
y[] <- lapply(y, as.integer)
if (!is.na(invalid <- match(TRUE, vapply(y, invalid_grm, logical(1L)))))
stop(paste(names(y)[invalid], "does not have at least two valid responses"))
H <- vapply(y, max, integer(1L), na.rm = TRUE)
# extract item parameters
if(missing(item_coefs))
stop("`item_coefs` must be supplied.")
if(!is.list(item_coefs) || length(item_coefs) != J)
stop("`item_coefs` must be a list of `ncol(y)` elements")
item_coefs_H <- vapply(item_coefs, length, integer(1L))
if(!all.equal(item_coefs_H, H))
stop("`item_coefs` do not match the number of response categories in `y`")
alpha <- lapply(item_coefs, function(x) c(Inf, x[-length(x)], -Inf))
beta <- vapply(item_coefs, function(x) x[[length(x)]], double(1L))
# check x and z (x and z should contain an intercept column)
x <- x %||% as.matrix(rep(1, N))
z <- z %||% as.matrix(rep(1, N))
if (!is.matrix(x)) stop("`x` must be a matrix.")
if (!is.matrix(z)) stop("`z` must be a matrix.")
if (nrow(x) != N || nrow(z) != N) stop("both 'x' and 'z' must have the same number of rows as 'y'")
p <- ncol(x)
q <- ncol(z)
colnames(x) <- colnames(x) %||% paste0("x", 1:p)
colnames(z) <- colnames(z) %||% paste0("x", 1:q)
# control parameters
con <- list(max_iter = 150, max_iter2 = 15, eps = 1e-03, eps2 = 1e-03, K = 25, C = 4)
con[names(control)] <- control
# set environments for utility functions
environment(loglik_grm) <- environment(theta_post_grm) <- environment(dummy_fun_grm) <- environment(tab2df_grm) <- environment()
# GL points
K <- con[["K"]]
theta_ls <- con[["C"]] * GLpoints[[K]][["x"]]
qw_ls <- con[["C"]] * GLpoints[[K]][["w"]]
# imputation
y_imp <- y
if(anyNA(y)) y_imp[] <- lapply(y, impute)
# pca for initial values of theta_eap
theta_eap <- {
tmp <- princomp(y_imp, cor = TRUE)$scores[, 1]
(tmp - mean(tmp, na.rm = TRUE))/sd(tmp, na.rm = TRUE)
}
# initial values of gamma and lambda
lm_opr <- tcrossprod(solve(crossprod(x)), x)
gamma <- lm_opr %*% theta_eap
lambda <- rep(0, q)
fitted_mean <- as.double(x %*% gamma)
fitted_var <- rep(1, N)
# EM algorithm
for (iter in seq(1, con[["max_iter"]])) {
# store previous parameters
# alpha_prev <- alpha
# beta_prev <- beta
gamma_prev <- gamma
lambda_prev <- lambda
# construct w_ik
posterior <- Map(theta_post_grm, theta_ls, qw_ls)
w <- {
tmp <- matrix(unlist(posterior), N, K)
t(sweep(tmp, 1, rowSums(tmp), FUN = "/"))
}
# # maximization
# pseudo_tab <- Map(dummy_fun_grm, y, H)
# pseudo_y <- lapply(pseudo_tab, tab2df_grm, theta_ls = theta_ls)
# pseudo_lrm <- lapply(pseudo_y, function(df) lrm.fit(df[["x"]], df[["y"]], weights = df[["wt"]])[["coefficients"]])
# beta <- vapply(pseudo_lrm, function(x) x[[length(x)]], double(1L))
# alpha <- lapply(pseudo_lrm, function(x) c(Inf, x[-length(x)], -Inf))
# EAP and VAP estimates of latent preferences
theta_eap <- t(theta_ls %*% w)
theta_vap <- t(theta_ls^2 %*% w) - theta_eap^2
# variance regression
gamma <- lm_opr %*% theta_eap
r2 <- (theta_eap - x %*% gamma)^2 + theta_vap
if (ncol(z)==1) lambda <- log(mean(r2)) else{
s2 <- glm.fit(x = z, y = r2, intercept = FALSE, family = Gamma(link = "log"))[["fitted.values"]]
loglik <- -0.5 * (log(s2) + r2/s2)
LL0 <- sum(loglik)
dLL <- 1
for (m in seq(1, con[["max_iter2"]])) {
gamma <- lm.wfit(x, theta_eap, w = 1/s2)[["coefficients"]]
r2 <- (theta_eap - x %*% gamma)^2 + theta_vap
var_reg <- glm.fit(x = z, y = r2, intercept = FALSE, family = Gamma(link = "log"))
s2 <- var_reg[["fitted.values"]]
loglik <- -0.5 * (log(s2) + r2/s2)
LL_temp <- sum(loglik)
dLL <- LL_temp - LL0
if (dLL < con[["eps2"]])
break
LL0 <- LL_temp
}
lambda <- var_reg[["coefficients"]]
}
fitted_mean <- as.double(x %*% gamma)
fitted_var <- exp(as.double(z %*% lambda))
cat(".")
if (sqrt(mean((gamma/gamma_prev - 1)^2)) < con[["eps"]]) {
cat("\n converged at iteration", iter, "\n")
break
} else if (iter == con[["max_iter"]]) {
stop("algorithm did not converge; try increasing `max_iter` or decreasing `eps`")
break
} else next
}
gamma <- setNames(as.double(gamma), paste("x", colnames(x), sep = ""))
lambda <- setNames(as.double(lambda), paste("z", colnames(z), sep = ""))
# inference
pik <- matrix(unlist(Map(partial(dnorm, x = theta_ls), mean = fitted_mean, sd = sqrt(fitted_var))),
N, K, byrow = TRUE) * matrix(qw_ls, N, K, byrow = TRUE)
Lijk <- lapply(theta_ls, function(theta_k) exp(loglik_grm(alpha = alpha, beta = beta, rep(theta_k, N)))) # K-list
Lik <- vapply(Lijk, compose(exp, partial(rowSums, na.rm = TRUE), log), double(N))
Li <- rowSums(Lik * pik)
# log likelihood
log_Lik <- sum(log(Li))
# outer product of gradients
environment(sj_ab_grm) <- environment(si_gamma) <- environment(si_lambda) <- environment()
# s_ab <- unname(Reduce(rbind, lapply(1:J, sj_ab_grm)))
s_gamma <- vapply(1:N, si_gamma, double(p))
s_lambda <- vapply(1:N, si_lambda, double(q))
# covariance matrix
s_all <- rbind(s_gamma, s_lambda)
s_all[is.na(s_all)] <- 0
covmat <- tryCatch(solve(tcrossprod(s_all)),
error = function(e) {warning("The information matrix is singular; SE calculation failed.");
matrix(NA, nrow(s_all), nrow(s_all))})
# reorganize se_all
sH <- sum(H)
gamma_indices <- (sH - 1):(sH + p - 2)
lambda_indices <- (sH + p - 1):(sH + p + q - 2)
se_all <- c(rep(0, sH), sqrt(diag(covmat)))
# name se_all and covmat
names_ab <- unlist(lapply(names(alpha), function(x) {
tmp <- alpha[[x]]
paste(x, c(paste0("y>=", seq(2, length(tmp)-1)), "Dscrmn"))
}))
names(se_all) <- c(names_ab, names(gamma), names(lambda))
rownames(covmat) <- colnames(covmat) <- c(names(gamma), names(lambda))
# item coefficients
coef_item <- Map(function(a, b) c(a[-c(1L, length(a))], Dscrmn = b), alpha, beta)
# all coefficients
coef_all <- c(unlist(coef_item), gamma, lambda)
coefs <- data.frame(Estimate = coef_all, Std_Error = se_all, z_value = coef_all/se_all,
p_value = 2 * (1 - pnorm(abs(coef_all/se_all))))
rownames(coefs) <- names(se_all)
# ability parameter estimates
theta <- data.frame(post_mean = theta_eap, post_sd = sqrt(theta_vap),
prior_mean = fitted_mean, prior_sd = sqrt(fitted_var))
# output
out <- list(coefficients = coefs, scores = theta, vcov = covmat, log_Lik = log_Lik,
N = N, J = J, H = H, ylevels = ylevels, p = p, q = q, control = con, call = cl)
class(out) <- c("hgrm", "hIRT")
out
}
|
30b2864595e7aaa5479a9e4ef4aa4779a8398705
|
00cf6a882ff6436daf8e7cb511c6334dc0c387cf
|
/00_build_product_metadata.R
|
1f118a605fb2591590d78b37ac41b26ff2f7f810
|
[] |
no_license
|
dahcase/earth_engine
|
09183f95c4fc114e45d10dbb573f9549ca01e72f
|
43d8fdc500ffba0f274b5234edbe70fdefd3aca0
|
refs/heads/master
| 2021-06-26T12:55:39.076946
| 2019-02-10T01:40:25
| 2019-02-10T01:40:25
| 116,109,534
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,764
|
r
|
00_build_product_metadata.R
|
library('data.table')
#Land surface temperature (MOD11A2 & MYD11A2)
lst = setDT(expand.grid(product =c('MOD11A2','MYD11A2'),
version = '006',
variables = c('LST_Day_1km', 'LST_Night_1km'),
lower = 7500,
upper = 65535,
qa_na = 0,
scale = .02,
sensor = 'MODIS',
qa_bits = 8,
year_start = 2001,
year_end = 2016, stringsAsFactors = F))
lst[ , qa_layer := paste0('QC_', tstrsplit(variables, "_")[[2]])]
lst[ , qa_product := product]
lst[ product == 'MYD11A2',year_start := 2002]
#NDVI and EVI from MOD13A1 and MYD13A1
vis = setDT(expand.grid(product = c('MOD13A1','MYD13A1'),
version = '006',
variables = c('EVI', 'NDVI'),
lower = -2000,
upper = 10000,
qa_na = 65535,
scale = .0001,
qa_layer = 'DetailedQA',
qa_bits = 16,
sensor = 'MODIS',
year_start = 2001,
year_end = 2016,
pixel_size = 500,
stringsAsFactors = F))
vis[,qa_product:= product]
#night time lights
ntl = setDT(expand.grid(product = 'DMSPNTL',
version = 4,
sensor = 'DMSPNTL',
gee_collection_id = 'NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4', stringsAsFactors = F))
#Reflectance
reflect = setDT(expand.grid(product = c('MOD09A1'),
version = '006',
variables = paste0('sur_refl_b0',1:7),
lower = -100,
upper = 16000,
qa_na = 4294967295,
scale = .0001,
qa_layer = 'QA',
qa_bits = 32,
sensor = 'MODIS',
year_start = 2001,
year_end = 2016, stringsAsFactors = F))
#Albedo reflect
albedo = setDT(expand.grid(product = c('MCD43A3'),
version = '006',
variables = c(paste0('Albedo_BSA_Band',c(1:7,'_vis','_nir','_shortwave')),
paste0('Albedo_WSA_Band',c(1:7,'_vis','_nir','_shortwave'))),
lower = 0,
upper = 32766,
qa_na = 2,
scale = .001,
qa_layer = 'QA',
qa_bits = 1,
sensor = 'MODIS',
year_start = 2001,
year_end = 2016, stringsAsFactors = F))
#overwrite the QA layer names
albedo[,qa_layer:= paste0('BRDF_Albedo_Band_Mandatory_Quality_',
c(paste0('Band',1:7),'vis','nir','shortwave'))]
#brdf reflect
brdf = setDT(expand.grid(product = 'MCD43A4',
version = '006',
variables = c(paste0('Nadir_Reflectance_Band',1:7)),
lower = 0,
upper = 32766,
qa_product = 'MCD43A4', #MCD43A2 is the full QA
scale = .001,
qa_na = -9999,
sensor = 'MODIS',
year_start = 2001,
year_end = 2016, stringsAsFactors = F))
#overwrite the QA layer names
brdf[,qa_layer:= paste0('BRDF_Albedo_Band_Mandatory_Quality_',
c(paste0('Band',1:7)))]
|
a95b6579538dafa0e870253da2a49859395e383a
|
a24efd391f493d356c0fe3f94ebc36e6eebd4714
|
/R/days_to_numeric.r
|
d4d284a7aa4dc0052b3e621bf6d74f8a619d94f2
|
[] |
no_license
|
dabidou025/Lockdown-Forecast
|
7eb0bfe8fedb5f2289b9796cc5c1b6bc3023848c
|
400f10c18c85e53bb5851c488471bce80c957dc0
|
refs/heads/main
| 2023-04-01T07:40:25.668489
| 2021-04-04T11:41:24
| 2021-04-04T11:41:24
| 354,528,256
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 461
|
r
|
days_to_numeric.r
|
days_to_numeric = function (data) {
data$WeekDays[data$WeekDays=='Monday'] = 1
data$WeekDays[data$WeekDays=='Tuesday'] = 2
data$WeekDays[data$WeekDays=='Wednesday'] = 3
data$WeekDays[data$WeekDays=='Thursday'] = 4
data$WeekDays[data$WeekDays=='Friday'] = 5
data$WeekDays[data$WeekDays=='Saturday'] = 6
data$WeekDays[data$WeekDays=='Sunday'] = 7
data$WeekDays = as.numeric(data$WeekDays)
return (data$WeekDays)
}
|
d673d9b9a2661dfb5a3cb4c7b4c18629c34776ee
|
a4bcc17bab2f4ac27c37f8f0027168211a9bc24d
|
/man/lookupGenes.Rd
|
41d42707bb4298724c2794264127d34c0b562fde
|
[] |
no_license
|
hcnh174/hlsgr
|
7da6307f535038d00e9030eab61e1a0045fc745a
|
23b0d0cce887eef200c90fccb5a667b4fc1f37cd
|
refs/heads/master
| 2023-04-13T18:44:28.519716
| 2023-04-03T05:38:45
| 2023-04-03T05:38:45
| 262,727,333
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 455
|
rd
|
lookupGenes.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rnaseq.R
\name{lookupGenes}
\alias{lookupGenes}
\title{Return a vector gene names corresponding to a vector of Ensembl IDs}
\usage{
lookupGenes(ensembl_ids)
}
\arguments{
\item{ensembl_ids}{vector of ensembl IDs}
}
\value{
}
\description{
Return a vector gene names corresponding to a vector of Ensembl IDs
}
\examples{
lookupGenes(c('ENSG00000211459', 'ENSG00000211958 '))
}
|
f66993d4ba8a1afc2c1b0a134e4dd73c4c3f4718
|
be2d33a5547d69d7339351366c5a1a309f4455dd
|
/fcd/R/plan.R
|
c9b4b096a7e8469c91106c1aca082d1a189a573c
|
[
"MIT"
] |
permissive
|
thebioengineer/drake-examples
|
3e3f1f6e0ae9942b70333238875dd65849650ee7
|
e7009496a9fa8495faa32806eb191ad9ae33786d
|
refs/heads/master
| 2020-08-04T19:49:58.862394
| 2019-10-09T16:28:03
| 2019-10-09T16:28:03
| 212,259,584
| 0
| 0
| null | 2019-10-02T05:07:05
| 2019-10-02T05:07:04
| null |
UTF-8
|
R
| false
| false
| 383
|
r
|
plan.R
|
#Experimental Conditions
conds <- fcdConds(nPeople = c(10000,1000,100), pEdge = c(0.75,0.5,0.25,0.1), topN = c(NA, 3, 5, 10))
plan <- drake_plan(
ddist_i = target(
fcdSim(nSims = 50, nPeople = nPeople,pEdge = pEdge, topN = topN),
transform = map(nPeople, pEdge, topN, .data = !!conds)
),
ddist = target(
bind_rows(ddist_i),
transform = combine(ddist_i)
)
)
|
a05afa87352479a24c6788ecf347fc3e86be442a
|
ccd5803bb5b558dcd89b4818b7a42b00cf63e610
|
/R/format.R
|
a318fc8fdb8049eec20651b5562ca5fd26fbeee6
|
[
"MIT"
] |
permissive
|
labouz/recipes
|
bbcdedce9116745df3bb69d88d9aacce26026fe8
|
620ebfe67d59e3e440fe203b51bdb87ceb542702
|
refs/heads/master
| 2021-11-15T09:57:24.236122
| 2021-04-22T18:41:14
| 2021-04-22T18:41:14
| 237,512,319
| 0
| 0
|
NOASSERTION
| 2021-04-23T23:39:04
| 2020-01-31T20:40:30
|
HTML
|
UTF-8
|
R
| false
| false
| 1,043
|
r
|
format.R
|
#' Helpers for printing step functions
#'
#' @param x A vector of objects.
#' @param sep A character string for separating values.
#' @param width An integer for when to split the output over lines.
#' @return A character string
#' @keywords internal
#' @export
format_ch_vec <-
function(x,
sep = ", ",
width = options()$width - 9) {
widths <- nchar(x)
sep_wd <- nchar(sep)
adj_wd <- widths + sep_wd
if (sum(adj_wd) >= width) {
keepers <- max(which(cumsum(adj_wd) < width)) - 1
if (length(keepers) == 0 || keepers < 1) {
x <- paste(length(x), "items")
} else {
x <- c(x[1:keepers], "...")
}
}
paste0(x, collapse = sep)
}
#' @keywords internal
#' @rdname format_ch_vec
#' @export
format_selectors <- function(x, width = options()$width - 9) {
## convert to character without the leading ~
x_items <- lapply(x, function(x) {
expr_deparse(quo_get_expr(x))
})
x_items <- unlist(x_items)
format_ch_vec(x_items, width = width, sep = ", ")
}
|
75c8c10127ac91ef6081bc9db1b9392fc1292731
|
4bcffce1d7b8094e5dd462705ad73834da8aed0e
|
/plot4.R
|
ba87a636aba245a677b6c46e0cf29f0a99859458
|
[] |
no_license
|
NicoHeredia/ExData_Plotting1
|
ff02715aa8aacc3c8ff344c7528459b53bd83708
|
0b47ef482913dd7488cb60806496e9205e007639
|
refs/heads/master
| 2022-11-14T07:26:44.862702
| 2020-07-12T20:45:12
| 2020-07-12T20:45:12
| 279,129,446
| 0
| 0
| null | 2020-07-12T19:00:08
| 2020-07-12T19:00:07
| null |
UTF-8
|
R
| false
| false
| 1,211
|
r
|
plot4.R
|
FileDat <- "household_power_consumption.txt"
PowerData <- read.table(FileDat, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
SubsetDat <- PowerData[PowerData$Date %in% c("1/2/2007","2/2/2007") ,]
#str(SubsetDat)
GlobalActPower <- as.numeric(SubsetDat$Global_active_power)
TimeDat <- strptime(paste(SubsetDat$Date, SubsetDat$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
SubMet1 <- as.numeric(SubsetDat$Sub_metering_1)
SubMet2 <- as.numeric(SubsetDat$Sub_metering_2)
SubMet3 <- as.numeric(SubsetDat$Sub_metering_3)
GlobalReactPower <- as.numeric(SubsetDat$Global_reactive_power)
Volt <- as.numeric(SubsetDat$Voltage)
#plotting
png("plot4.png", width=480, height=480)
par(mfrow=c(2,2))
plot(TimeDat, GlobalActPower, type="l", xlab="", ylab="Global Active Power")
plot(TimeDat, Volt, type="l", xlab="datetime", ylab="Voltage")
plot(TimeDat, SubMet1, type="l", xlab="", ylab="Energy sub metering")
lines(TimeDat, SubMet2, type="l", col="red")
lines(TimeDat, SubMet3, type="l", col="blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, lwd=2.5, col=c("black", "red", "blue"))
plot(TimeDat, GlobalReactPower, type="l", xlab="datetime", ylab="Global_reactive_power")
dev.off()
|
38645f54fdf875f9ee7c951901b7d230bdf2409c
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/taRifx/examples/munch.Rd.R
|
db766865e270d49851232e72b4902fc55575584f
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 250
|
r
|
munch.Rd.R
|
library(taRifx)
### Name: munch
### Title: Recursively delete entries containing 'what' before entry
### pointed to by 'which'
### Aliases: munch
### ** Examples
x <- c("a","","b","","","","","c","d","","","","e","")
munch( x, c(3,8,9,13) )
|
15c1b669319f7f8a4937aa738f5d1fcf5211a535
|
212e49d0b5df150e4d0681451925689b9e152eba
|
/MESSAR_WEBSERVER/helper.r
|
280bb300966644aba086178a2dbdc94c356c3439
|
[] |
no_license
|
daniellyz/MESSAR
|
7e3fa9a6fbfba378a37ded366bad6db89d6a226b
|
ebbe4d0b849d074d36d447d5488464ac4773b991
|
refs/heads/master
| 2023-04-14T02:19:20.990578
| 2023-03-28T17:06:07
| 2023-03-28T17:06:07
| 153,620,833
| 1
| 0
| null | 2019-07-03T14:57:32
| 2018-10-18T12:33:50
|
R
|
UTF-8
|
R
| false
| false
| 9,930
|
r
|
helper.r
|
<<<<<<< HEAD
=======
mcc_calculator<-function(rules){
P_A_B = rules$support/rules$Py
tpr = rules$support
fpr = (1 - P_A_B)*rules$Py
fnr = (1 - rules$confidence)*rules$Px
tnr = 1 - tpr - fpr - fnr
MCC = (tpr*tnr - fpr*fnr)/sqrt((tpr+fpr)*(tpr+fnr)*(tnr+fpr)*(tnr+fnr))
return(MCC)
}
>>>>>>> 7b7522d0affa0d7a817f5252d04e620560b84c0e
search_rules<-function(ref_feature, ref_feature_type, mass, mass_diff, ppm_search){
# Combine masses:
test_feature = c(mass, mass_diff)
test_feature_type = c(rep('mass', length(mass)), rep("mass_diff", length(mass_diff)))
NF = length(ref_feature)
# Search ref features:
matched_feature = c()
for (i in 1:NF){
valid = 1
feature = ref_feature[[i]]
feature_type = ref_feature_type[[i]]
for (f in 1:length(feature)){
errors = abs((feature[f]-test_feature)/feature[f])*1000000
min_error = min(errors)
index_error = which.min(errors)
if ((min_error <= ppm_search) & (test_feature_type[index_error] == feature_type[f])){
} else {
valid = 0
break
}
}
if (valid == 1){ # If always valid
matched_feature = c(matched_feature, i)
}
}
return(matched_feature)}
ppm_calc <- function(x, ref){
errors = abs(x-ref)/ref*1000000
return(errors)
}
compute_similariy<-function(candidates,true_structure){
# Compare list of candidates (SMILE code) with the true structure
candidates_finger <- lapply(parse.smiles(candidates), get.fingerprint, type='maccs')
true_structure_finger <- get.fingerprint(parse.smiles(true_structure)[[1]],type="maccs")
fp.distance=sapply(candidates_finger,function(x) 1-distance(x,true_structure_finger))
return(fp.distance)
}
fn <- function(x, y){
# Compare list of candidates
x_finger <- try(get.fingerprint(parse.smiles(x)[[1]],type='maccs'),silent=T)
y_finger <- try(get.fingerprint(parse.smiles(y)[[1]],type='maccs'),silent=T)
if ((class(x_finger)!="try-error") && (class(y_finger)!="try-error")){
fp.distance=1-distance(x_finger,y_finger)} else {
fp.distance = 1}
return(fp.distance)
}
numextract <- function(string){as.numeric(str_extract(string, "\\-*\\d+\\.*\\d*"))}
aggregate_rules <- function(rules_extracted){
<<<<<<< HEAD
=======
>>>>>>> 7b7522d0affa0d7a817f5252d04e620560b84c0e
# Group rules by their substructure
combined_type = aggregate(rules_extracted$SPECTRAL_FEATURE_TYPE, list(rules_extracted$SUBSTRUCTURE), toString)[,2]
combined_features = aggregate(rules_extracted$SPECTRAL_FEATURE, list(rules_extracted$SUBSTRUCTURE), toString)[,2]
combined_type = sapply(combined_type, clean_feature_text)
combined_features = sapply(combined_features, clean_feature_text)
<<<<<<< HEAD
sum_confidence = aggregate(rules_extracted$CONFIDENCE, list(rules_extracted$SUBSTRUCTURE), sum)
sum_lift = aggregate(rules_extracted$LIFT, list(rules_extracted$SUBSTRUCTURE), sum)[,2]
sum_mcc = aggregate(rules_extracted$MCC, list(rules_extracted$SUBSTRUCTURE), sum)[,2]
sum_f1 = aggregate(rules_extracted$F1, list(rules_extracted$SUBSTRUCTURE), sum)[,2]
sum_aggregated = cbind.data.frame(sum_confidence,sum_lift,sum_mcc, sum_f1)
colnames(sum_aggregated) = c("SUBSTRUCTURE", "CONFIDENCE", "LIFT","MCC","F1")
median_confidence = aggregate(rules_extracted$CONFIDENCE, list(rules_extracted$SUBSTRUCTURE), median)
median_lift = aggregate(rules_extracted$LIFT, list(rules_extracted$SUBSTRUCTURE), median)[,2]
median_mcc = aggregate(rules_extracted$MCC, list(rules_extracted$SUBSTRUCTURE), median)[,2]
median_f1 = aggregate(rules_extracted$F1, list(rules_extracted$SUBSTRUCTURE), median)[,2]
median_aggregated = cbind.data.frame(median_confidence,median_lift,median_mcc,median_f1)
colnames(median_aggregated) = c("SUBSTRUCTURE", "CONFIDENCE", "LIFT","MCC","F1")
=======
sum_confidence = aggregate(rules_extracted$CONFIDENCE, list(rules_extracted$SUBSTRUCTURE), sum)
sum_lift = aggregate(rules_extracted$LIFT, list(rules_extracted$SUBSTRUCTURE), sum)[,2]
sum_mcc = aggregate(rules_extracted$MCC, list(rules_extracted$SUBSTRUCTURE), sum)[,2]
sum_aggregated = cbind.data.frame(sum_confidence,sum_lift,sum_mcc)
colnames(sum_aggregated) = c("SUBSTRUCTURE", "CONFIDENCE", "LIFT","MCC")
median_confidence = aggregate(rules_extracted$CONFIDENCE, list(rules_extracted$SUBSTRUCTURE), median)
median_lift = aggregate(rules_extracted$LIFT, list(rules_extracted$SUBSTRUCTURE), median)[,2]
median_mcc = aggregate(rules_extracted$MCC, list(rules_extracted$SUBSTRUCTURE), median)[,2]
median_aggregated = cbind.data.frame(median_confidence,median_lift,median_mcc)
colnames(median_aggregated) = c("SUBSTRUCTURE", "CONFIDENCE", "LIFT","MCC")
>>>>>>> 7b7522d0affa0d7a817f5252d04e620560b84c0e
max_confidence = aggregate(rules_extracted$CONFIDENCE, list(rules_extracted$SUBSTRUCTURE), max)
max_lift = aggregate(rules_extracted$LIFT, list(rules_extracted$SUBSTRUCTURE), max)[,2]
max_mcc = aggregate(rules_extracted$MCC, list(rules_extracted$SUBSTRUCTURE), max)[,2]
<<<<<<< HEAD
max_f1 = aggregate(rules_extracted$F1, list(rules_extracted$SUBSTRUCTURE), max)[,2]
max_aggregated = cbind.data.frame(max_confidence,max_lift,max_mcc,max_f1)
colnames(max_aggregated) = c("SUBSTRUCTURE", "CONFIDENCE", "LIFT","MCC","F1")
=======
max_aggregated = cbind.data.frame(max_confidence,max_lift,max_mcc)
colnames(max_aggregated) = c("SUBSTRUCTURE", "CONFIDENCE", "LIFT","MCC")
>>>>>>> 7b7522d0affa0d7a817f5252d04e620560b84c0e
return(list(type=combined_type, features=combined_features,
sum_aggregated=sum_aggregated, median_aggregated=median_aggregated, max_aggregated=max_aggregated))
}
eval_rules_aggregated <- function(rule_aggregated, aggregate_type, score_type, pb_threshold){
# Pick a type of aggregated rule evaluation
if (aggregate_type=="SUM"){score = rule_aggregated$sum_aggregated[, score_type]}
if (aggregate_type=="MEDIAN"){score = rule_aggregated$median_aggregated[, score_type]}
if (aggregate_type=="MAX"){score = rule_aggregated$max_aggregated[, score_type]}
aggregated = cbind.data.frame(rule_aggregated$sum_aggregated$SUBSTRUCTURE, score)
colnames(aggregated) = c("SUBSTRUCTURE", score_type)
threshold = quantile(aggregated[,2], probs=pb_threshold/100, na.rm=T)
aggregated = aggregated[aggregated[,2]>=threshold, ]
aggregated = aggregated[order(aggregated[,2], decreasing = T),]
return(aggregated)
}
img_uri <- function(x) {sprintf('<img src="%s"/>', knitr::image_uri(x))}
clean_feature_text <- function(feature){
feature = str_replace_all(feature, "\\[", "")
feature = str_replace_all(feature, " ", "")
feature = str_replace_all(feature, "\\]", "")
feature = str_replace_all(feature, "\\'","")
return(feature)
}
<<<<<<< HEAD
=======
fdr_compute <- function(rules, decoy, fdr_thr){
combined_label = c(rep("Target", nrow(rules)), rep("Decoy", nrow(decoy)))
combined_mcc = c(rules$MCC, decoy$MCC)
combined_lift = c(rules$LIFT, decoy$LIFT)
NC = length(combined_label)
NG = 100 # Number of grids
grid_mcc = seq(min(combined_mcc), max(combined_mcc), length.out = NG)
grid_lift = seq(min(combined_lift), max(combined_lift), length.out = NG)
fdr_matrix = matrix(1, NG, NG)
valid_matrix = matrix(0, NG, NG) # Nb of valid (target) rules
for (i in 1:NG){
for (j in 1:NG){
valid = which(combined_mcc>=grid_mcc[i] & combined_lift>=grid_lift[j])
if (length(valid)>0){
selected_label = combined_label[valid]
fdr = sum(selected_label=="Decoy")/length(valid)
fdr_matrix[i,j] = fdr
valid_matrix[i,j] = sum(selected_label=="Target")
}
}
}
if (min(fdr_matrix)<=fdr_thr){
index_fdr = which(fdr_matrix<=fdr_thr, arr.ind=T) # Index that have a fdr smaller than fdr_thr
LS = valid_matrix[index_fdr] # Nb of rules of all minimal fdrs
max_size = which(LS == max(LS)) # Maximal number of rules that allow minimal fdr
grid_fdr = index_fdr[max_size[length(max_size)],] # Apply highest possibe threshold to maximize
mcc_min = grid_mcc[grid_fdr[1]]
lift_min = grid_lift[grid_fdr[2]]
fdr = min(fdr_matrix)
} else { # No filter applied if FDR optimization not possible
max_size = nrow(rules)
mcc_min = 0
lift_min = 1
fdr = -1 # Failed!
}
return(list(max_size = max_size, mcc_min = mcc_min, lift_min = lift_min, fdr = fdr))
}
>>>>>>> 7b7522d0affa0d7a817f5252d04e620560b84c0e
rule_mass_match<-function(rule_types, rule_features, masslist, ppm_search){
# The function match back comobined rules to raw masslist
rule_types = strsplit(rule_types,",")[[1]]
rule_features = as.numeric(strsplit(rule_features,",")[[1]])
# Match to raw masslist:
index1 = c() # Index of matched mass in raw masslist
rule_fragments = rule_features[rule_types=="mass"]
if (length(rule_fragments)>0){
for (fragment in rule_fragments){
errors = ppm_calc(masslist, fragment)
valid = which(errors<=ppm_search)
if (length(valid)>0){index1 = c(index1, valid[1])}
}}
# Match to raw mass diff:
index2 = list() # List of matched mass difference (from...to...)
rule_mdiff = rule_features[rule_types=="mass_diff"]
rule_mdiff = unique(rule_mdiff)
dist_mass = data.matrix(dist(masslist))
k = 0
if (length(rule_mdiff)>0){
for (mdiff in rule_mdiff){
errors = abs((dist_mass-mdiff)/mdiff*1000000)
valid = which(errors<ppm_search, arr.ind = T)
if (nrow(valid)>0){
k = k+1
index2[[k]] = as.numeric(valid[1,])
}}
}
return(list(index1=index1, index2=index2))
}
<<<<<<< HEAD
get_spectrum_list <- function(dat){
spectrum_list = list()
for (i in 1:length(dat)){
spectrum = cbind(dat[[i]]@mz, dat[[i]]@intensity)
spectrum = matrix(spectrum, ncol=2)
spectrum_list[[i]] = spectrum
}
return(spectrum_list)
}
=======
>>>>>>> 7b7522d0affa0d7a817f5252d04e620560b84c0e
|
b1a97b33b93fb325844de4c2532143569d8348ee
|
9b40acbd06b56f1df9de7438516a3230ba8c2cc4
|
/server.R
|
9244cd4b24caa9e992224d9b8d6e03538eec7021
|
[
"MIT"
] |
permissive
|
Huade/UN_IdealPoints
|
7ca7c4ec059df758bda10c7e2677b4eefbf4290e
|
9276aca27ad69dd4efacaa64217c15d7671201a3
|
refs/heads/master
| 2016-09-06T07:03:32.325359
| 2015-04-21T15:17:33
| 2015-04-21T15:17:33
| 31,746,396
| 2
| 3
| null | 2015-04-01T14:17:15
| 2015-03-06T01:31:12
|
R
|
UTF-8
|
R
| false
| false
| 3,252
|
r
|
server.R
|
library(shiny)
library(ggplot2)
library(plotly)
library(ggthemes)
shinyServer(function(input, output, session) {
output$trendPlot <- renderGraph({
if (length(input$name)==0) print("Please select at least one country")
else {
df_trend <- subset(Ideal_Point_Data, Name %in% input$name)
# Graph title
if (length(input$name)>2) {
j_names_comma <- paste(input$name[-length(input$name)], collapse = ', ')
j_names <- paste(j_names_comma, ", and ", input$name[length(input$name)],
sep="")
}
else{
j_names <- paste(input$name, collapse = ' and ')
}
graph_title <- paste("Ideal Points for ", j_names, sep="")
ggideal_point <- ggplot(df_trend)+
geom_hline(aes(yintercept = 2), color = "white")+
geom_hline(aes(yintercept = -2), color = "white")+
geom_line(aes(x=Year, y=Ideal.point, by=Name, color=Name))+
labs(x = "Year")+
labs(y = "Ideology")+
labs(title = graph_title)+
scale_colour_hue("clarity",l=70, c=150)+
theme_few()
# This converts the ggplot2 graph into Plotly's format.
# This is a list of lists which declaratively describe every attribute
# of the plotly graph
fig <- gg2list(ggideal_point)
data <- list()
for(i in 1:(length(fig)-1)){data[[i]]<-fig[[i]]}
layout <- fig$kwargs$layout
layout$annotations <- NULL # Remove the existing annotations (the legend label)
layout$annotations <- list()
# Add colored text annotations next to the end of each line
# More about plotly annotations: https://plot.ly/r/reference/#annotation
# Each key that we update is documented in that link above.
for(i in 1:(length(data))){ # data is a list of the lines in the graph
layout$annotations[[i]] <- list(
text = data[[i]]$name, # The text label of the annotation, e.g. "Canada"
font = list(color = data[[i]]$line$color), # Match the font color to the line color
showarrow = FALSE, # Don't show the annotation arrow
y = data[[i]]$y[[length(data[[i]]$y)]], # set the y position of the annotation to the last point of the line
yref = "y1", # the "y" coordinates above are with respect to the yaxis
x = 1, # set the x position of the graph to the right hand side of the graph
xref = "paper", # the x coordinates are with respect to the "paper", where 1 means the right hand side of the graph and 0 means the left hand side
xanchor = "left" # position the x coordinate with respect to the left of the text
);
}
layout$showlegend <- FALSE # remove the legend
layout$margin$r <- 170 # increase the size of the right margin to accommodate more room for the annotation labels
# Send this message up to the browser client, which will get fed through to
# Plotly's javascript graphing library embedded inside the graph
return(list(
list(
id="trendPlot",
task="newPlot",
data=data,
layout=layout
)
))
}
})
})
|
36c6f23ecb6b74239c55c7672a8b68c3120c551a
|
a67fa37fb1ae2a624f2fc306f1b6eab457fc8197
|
/Hidden Markov Models/HMM.R
|
cae505347c13d5caf94a66a5a522cd4520571a57
|
[
"MIT"
] |
permissive
|
PedramKasebzadeh/Machine_Learning
|
a350ef5a05c2ae953624a69c6bb66d8f6331d0ca
|
3ac60aa38722d1a6817a8347e58954892f9ebbb0
|
refs/heads/main
| 2023-01-24T02:19:30.162358
| 2020-12-11T12:15:20
| 2020-12-11T12:15:20
| 320,540,864
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,006
|
r
|
HMM.R
|
# Task 1 - Build a hidden Markov model (HMM)
# Defining the trasmissions and the emissions probabilites matrices.
set.seed(12345)
library("HMM")
# creating the emissons probability matrix
ep= diag(0.2,10)
ep[row(ep) == (col(ep) -1)] = 0.2
ep[row(ep) == (col(ep) -2)] = 0.2
ep[row(ep) == (col(ep) +1)] = 0.2
ep[row(ep) == (col(ep) +2)] = 0.2
ep[c(1,1,2,9,10,10),c(9,10,10,1,1,2)]=0.2
ep[9,2]=0;ep[2,9]=0
ep
# transProbs matrix
tp = diag(0.5,10)
tp[row(tp)==(col(tp)-1)]=0.5
tp[10,1]=0.5
tp
Sectors=paste0("S",1:10)
hmm = initHMM(Sectors,Sectors,
startProbs = rep(0.1,10),transProbs = tp,
emissionProbs = ep)
# Task 2 - Simulate the HMM for 100 time steps*
simulations <- simHMM(hmm, 100)
# Task 3 - Discard the hidden states from the sample obtained above.
# observations
simulations$observation
observation=simulations$observation
# filtering
logfo = forward(hmm,observation)
loge = exp(logfo)
# smoothing
observation=simulations$observation
posterior = posterior(hmm,observation)
# the most probable path is
mostpro = viterbi(hmm, simulations$observation)
# Task 4 - Compute the accuracy of the filtered and smoothed probability distributions, and of the most probable path.
#head(prop.table(exp(logfo)))
forward = prop.table(loge,2)
# The accuracy for filtered probability distributions
#simulations$states
forpat = apply(forward,2,which.max)
forpath= rownames(forward)[forpat]
for_acc = length(which(forpath==simulations$states))/100
for_acc
# The accuracy for smoothed probability distributions
smoo = apply(posterior,MARGIN = 2,FUN=which.max)
smoopath= rownames(posterior)[smoo]
smoo_acc = length(which(smoopath==simulations$states))/100
smoo_acc
# The accuracy for most probable path:
mos_acc = length(which(mostpro==simulations$states))/100
mos_acc
# Smoothing gives us the highest accuracy and thats is due to the fact that it uses both previous and the future values to make a predictrion.
# Task 5 -Repeat the previous task with different simulated samples. In general, the smoothed distributions should be more accurate than the filtered distributions.
f100 = rep(0,100)
smoo100=rep(0,100)
mos100=rep(0,100)
for(i in 1:100){
simulations <- simHMM(hmm, 100)
observation=simulations$observation
logfo = forward(hmm,observation)
posterior = posterior(hmm,observation)
mostpro = viterbi(hmm, simulations$observation)
#forward acc
filtered = prop.table(exp(logfo),2)
forpat = apply(filtered, 2,which.max)
forwpath = rownames(logfo)[forpat]
#forwpath= table(forpat==simulations$states)
f100[i] = length(which(forwpath==simulations$states))/100
# post acc
smoo = apply(posterior,MARGIN = 2,FUN=which.max)
smoopath= rownames(posterior)[smoo]
smoo100[i] = length(which(smoopath==simulations$states))/100
# mo acc
mos100[i] = length(which(mostpro==simulations$states))/100
}
cat("The accuracy for filter is:",mean(f100))
cat("\nThe accuracy for smoothing is:",mean(smoo100))
cat("\nThe accuracy for path is:",mean(mos100))
# The reason for the smoothing to be the most accurate is that it uses all observations, including observations in the future,
# which the filtering function doesn't. And viterbi has a logical constraint which could be useful but in this case will result in
# less accuracy comparing to the smoothing.
# Task 6 - Is it true that the more observations you have the better you know where the robot is?
library(entropy)
ento=apply(prop.table(loge),2,entropy.empirical)
plot(ento,type = "l")
# As we can see in the plot the entropy does not converges to a stable value, which means having more data doesn't help us with accuracy.
# Task 7 - Consider any of the samples above of length 100. Compute the probabilities of the hidden states for the time step 101.
#filterProbs * trasmission to get 101
probablities = t(forward[,100])%*%tp
mostp = which.max(probablities)
mostp
probablities
# The probabilities of the hidden state for the time step 101 are: `r probablities` and the most probable is: `r mostp`
|
bf04ac00cde577bdfd319df1c028a025b66d2209
|
cbd3b0f76677e4e5c00bafced19939549182ff3f
|
/cachematrix.R
|
3badc6ef8885fa05fa6ef54e6325e6e29fb4c342
|
[] |
no_license
|
ginoandolini/ProgrammingAssignment2
|
d6dc2922c2dac18420bc270c692ed1bba8a6558f
|
7b4043852fb8ed6eb773ae434c9252929139334f
|
refs/heads/master
| 2020-04-01T19:19:37.525165
| 2014-05-20T10:41:06
| 2014-05-20T10:41:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,255
|
r
|
cachematrix.R
|
## makeCacheMatrix makes a special matrix
## Sets the value of the matrix, set
## Gets the value of the matrix , get
## Sets the value of the matrix inverse, setinverse
## Gets the value of the matrix inverse, getinverse
makeCacheMatrix <- function(x = numeric()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinverse <- function(solve) m <<- solve
getinverse <- function() m
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## Calculates the inverse of the special "matrix" created with makeCacheMatrix
## First checks to see if the inverse has already been calculated
## If so gets the inverse from the cache and skips the computation
## Otherwise, it calculates the inverse of the data and sets the value of the inverse in the cache via the setinverse function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
m <- x$getinverse()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setinverse(m)
m
}
|
c9d64ac63c1a3b7f19c451097476055a3d33a196
|
ecd850028010252cd24d7d67aa3ef3b8065bf109
|
/man/parallelCountNbd.Rd
|
81f51188730bed3e4daa424abf88265e443dbe5c
|
[] |
no_license
|
EricMarcon/SpatDiv
|
de98877555e9d92ec7859d4ce18bf5dbb47bf2a1
|
c0346e1105130d18dc8b978a415569f653ae0cf7
|
refs/heads/master
| 2023-05-26T18:42:38.506723
| 2023-05-21T08:42:49
| 2023-05-21T08:42:49
| 111,210,097
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 657
|
rd
|
parallelCountNbd.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{parallelCountNbd}
\alias{parallelCountNbd}
\title{parallelCountNbd}
\usage{
parallelCountNbd(r, NbSpecies, x, y, Type, Weight)
}
\arguments{
\item{r}{The vector of distances to take into account.}
\item{NbSpecies}{The number of species of the community.}
\item{x, y}{The coordinates of the points.}
\item{Type}{A vector containing the species of each point (as integer, i.e. the factor code).}
\item{Weight}{A vector containing the weights of points.}
}
\description{
Create a 3-D array containing the number of neighbors around each point, per species
}
|
8f37165ade6eae5855a7723931f56be58d0738a0
|
f04c8b91b2e26b0c4342525533032a0d33ad1333
|
/R/link_tag.R
|
4035f482c2f7e7bef2e8f4ee77068794d5c24eff
|
[] |
no_license
|
polymathematic/Longform
|
c567c9d883ba2c02a03a484546a7a62ad9a1b973
|
d03557ac07d6e39e0baf30c4dfa26d070c60a0b6
|
refs/heads/master
| 2020-03-15T21:22:07.825613
| 2020-01-14T18:00:50
| 2020-01-14T18:00:50
| 132,353,511
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 343
|
r
|
link_tag.R
|
#' Add a link tag
#'
#' Format an HTML image tag
#'
#' @param text The text of the link
#' @param url The URL the link points to
#' @export
link_tag <- function(text, url){
#Create tag
output <- sprintf("[%s](%s)",
text, url)
#Class as image tag
class(output) <- "link_tag"
#Return output
return(output)
}
|
2c34b013e33aca29c72663b2205888299878b947
|
31325bac0088dbc4725f01794ff72413d5856c64
|
/R/I_quadModMat.R
|
16589c32707e11f9a573c5e9b1385dd304376d59
|
[] |
no_license
|
mfasiolo/synlik_dev
|
6b2bae4f357cbe028050917f5a264e1698fcfe76
|
2508506cffe09e752c6fa221e6431997f739168e
|
refs/heads/master
| 2021-03-27T10:56:50.668629
| 2018-05-30T10:37:00
| 2018-05-30T10:37:00
| 17,673,353
| 0
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 506
|
r
|
I_quadModMat.R
|
# Creates model matrix for quadratic multivariate regression
.quadModMat <- function(X, center = FALSE, scale = FALSE, deriv = FALSE)
{
nPar <- ncol(X)
X <- scale(X, center = center, scale = scale)
M <- cbind(1, X, X ^ 2 / ifelse(deriv, 2, 1))
# Creating all mixed terms
if(nPar > 1){
comb <- combn(nPar, 2)
tmp <- lapply(1:ncol(comb), function(jj) X[ , comb[1, jj]] * X[ , comb[2, jj]])
M <- cbind(M, do.call("cbind", tmp))
}
return( M )
}
|
ffa6d326540f1b20c72bd427c20368fa17f81312
|
c3e2451daec7c223e6bca5e8ec5d29ea3efa5c6a
|
/inst/Example_AR1_AR2.R
|
2c392b7119d40e116380d86e754d54aa1802a16a
|
[] |
no_license
|
pierrejacob/bayeshscore
|
d0336efc16dd0456ffa2c3f6fbe51aabbcf3f3f8
|
8f148f4074e09de4911d5645a9781c8aa844d38d
|
refs/heads/master
| 2021-09-22T10:56:27.652463
| 2018-09-08T18:21:28
| 2018-09-08T18:21:28
| 63,806,619
| 5
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 12,235
|
r
|
Example_AR1_AR2.R
|
##################################################################################################
# Example - AR(1) vs AR(2)
##################################################################################################
library(bayeshscore)
library(ggplot2)
library(gridExtra)
library(doParallel)
library(foreach)
library(wesanderson)
set.seed(19)
# Define model
nu0 = 1
sigma02 = 1
nb_models = 2
model = function(i){
if (i==1){return(get_model_AR1(nu0, sigma02))}
if (i==2){return(get_model_AR2(nu0, sigma02))}
}
# set algorithmic parameters
algorithmic_parameters = list()
algorithmic_parameters$Ntheta = 2^10
algorithmic_parameters$verbose = TRUE
algorithmic_parameters$resampling = function(normw) ssp_resampling_n(normw, runif(length(normw)))
# The remaining algorithmic parameters are set to their default values via util_default.R
#--------------------------------------------------------------------------------------------
repl = 5 #number of replications
registerDoParallel(cores=5) #number of workers in parallel
#--------------------------------------------------------------------------------------------
nobservations = 1000
#############################################################################################
# Case 3: true model = AR(1)
#############################################################################################
true_model = 1
true_theta = c(0.6,0.8)
observations1 = simulateData(model(true_model),true_theta,nobservations)
# observations in a matrix of dimensions dimy x nobservations
#--------------------------------------------------------------------------------------------
results_all1 = data.frame()
#--------------------------------------------------------------------------------------------
### Compute logevidence and hscore for each model
for (m in 1:nb_models){
results = foreach(i=1:repl,.packages=c('bayeshscore'),.verbose = TRUE) %dorng% {
hscore(observations1, model(m), algorithmic_parameters)
}
for (r in 1:repl){
results_all1 = rbind(results_all1,data.frame(logevidence = results[[r]]$logevidence,
hscore = results[[r]]$hscore,
time = 1:nobservations,
model = factor(m),
repl = r))
}
}
#############################################################################################
# Case 4: true model = AR(2)
#############################################################################################
true_model = 2
true_theta = c(0.25,0.5,0.75^2)
observations2 = simulateData(model(true_model),true_theta,nobservations)
# observations in a matrix of dimensions dimy x nobservations
#--------------------------------------------------------------------------------------------
results_all2 = data.frame()
#--------------------------------------------------------------------------------------------
### Compute logevidence and hscore for each model
for (m in 1:nb_models){
results = foreach(i=1:repl,.packages=c('bayeshscore'),.verbose = TRUE) %dorng% {
hscore(observations2, model(m), algorithmic_parameters)
}
for (r in 1:repl){
results_all2 = rbind(results_all2,data.frame(logevidence = results[[r]]$logevidence,
hscore = results[[r]]$hscore,
time = 1:nobservations,
model = factor(m),
repl = r))
}
}
##############################################################################################
#--------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------
# Generate plots for paper
#--------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------
# Compute the Hyvarinen factor
results_all = list(results_all1,results_all2)
logbayesfactors = data.frame()
h_factors = data.frame()
BF_plots = list()
HF_plots = list()
for (r in 1:repl) {
for (i in 1:nb_models) {
results = results_all[[i]]
logbayes_factor = subset(results,model==1&repl==r)$logevidence - subset(results,model==2&repl==r)$logevidence
logbayesfactors = rbind(logbayesfactors,data.frame(case = factor(i),
time = 1:nobservations,
repl = r,
logbayesfactor = logbayes_factor,
type = factor(paste("Case ",toString(i)))))
h_factor = subset(results,model==2&repl==r)$hscore - subset(results,model==1&repl==r)$hscore
h_factors = rbind(h_factors,data.frame(case = factor(i),
time = 1:nobservations,
repl = r,
hfactor = h_factor,
type = factor(paste("Case ",toString(i)))))
}
}
# log Bayes factor
ggplot(logbayesfactors) +
geom_line(aes(time, logbayesfactor, color = case, group = interaction(case,repl))) +
geom_hline(yintercept = 0,linetype="dotted",size=1) +
ylab("log-Bayes factor [1 vs 2]") + facet_grid(. ~ type) + xlab("Number of observations") +
# guides(colour = guide_legend(override.aes = list(size = 2))) +
theme(strip.text.y = element_text(size = 12, colour = "black")) +
theme(legend.text=element_text(size=12)) +
theme(legend.title=element_text(size=12)) +
theme(legend.position="none") +
theme(axis.title.y=element_text(margin=margin(0,10,0,0))) +
theme(axis.title.x=element_text(margin=margin(10,0,0,0)))
# ggsave("example_AR1_AR2_logBF_1_vs_2.png",dpi = 300,width = 10,height = 5)
# Hyvarinen factor
labels.df = data.frame(x = rep(375,2), y = c(-220,30),
text = c("Case 4","Case 3"),
type = factor(c("Case 4","Case 3")))
colors = c(wes_palette("Darjeeling")[1],"mediumblue")
axis_ticktextsize = 10
axis_titlesize = 12
ggplot() +
geom_label(data = labels.df, aes(x,y,label = text,color=type), color = colors, fontface = "bold") +
theme(legend.position="none") +
scale_color_manual(values = colors[2:1]) +
geom_line(data = h_factors, aes(time, hfactor, color = case, group = interaction(case,repl)),alpha=0.6) +
geom_hline(yintercept = 0,linetype=2) +
xlab("Number of observations") +
ylab("H-factor [1 vs. 2]") +
ylim(c(min(h_factors$hfactor),30+max(h_factors$hfactor))) +
theme(strip.text.y = element_text(size = 12, colour = "black")) +
theme(legend.text=element_text(size=12)) +
theme(legend.title=element_text(size=12)) +
theme(legend.position="none") +
theme(axis.text.x = element_text(size = axis_ticktextsize),
axis.text.y = element_text(size = axis_ticktextsize),
axis.title.x = element_text(size = axis_titlesize, margin=margin(10,0,0,0)),
axis.title.y = element_text(size = axis_titlesize, angle = 90, margin = margin(0,10,0,0)),
strip.text.x = element_text(size = axis_titlesize, colour = "black"),
strip.background = element_rect(fill="gray88"),
panel.background = element_rect(fill="gray95",linetype = "solid", colour="white"),
legend.position = "none")
# ggsave("example_AR1_AR2_Hyvarinen_factor_1_vs_2_10_by_5.png",dpi = 300,width = 10,height = 5)
# ggsave("example_AR1_AR2_Hyvarinen_factor_1_vs_2_5_by_5.png",dpi = 300,width = 5,height = 5)
# log Bayes factor
labels.df = data.frame(x = rep(375,2), y = c(-70,12),
text = c("Case 4","Case 3"),
type = factor(c("Case 4","Case 3")))
colors = c("tomato3","dodgerblue")
ggplot() +
geom_label(data = labels.df, aes(x,y,label = text,color=type), color = colors, fontface = "bold") +
theme(legend.position="none") +
scale_color_manual(values = colors[2:1]) +
geom_line(data = logbayesfactors, aes(time, logbayesfactor, color = case, group = interaction(case,repl)),alpha=0.6) +
geom_hline(yintercept = 0,linetype=2) +
xlab("Number of observations") +
ylab("log-Bayes factor [1 vs. 2]") +
ylim(c(min(h_factors$hfactor),30+max(h_factors$hfactor))) +
theme(strip.text.y = element_text(size = 12, colour = "black")) +
theme(legend.text=element_text(size=12)) +
theme(legend.title=element_text(size=12)) +
theme(axis.text.x = element_text(size = axis_ticktextsize),
axis.text.y = element_text(size = axis_ticktextsize),
axis.title.x = element_text(size = axis_titlesize, margin=margin(10,0,0,0)),
axis.title.y = element_text(size = axis_titlesize, angle = 90, margin = margin(0,10,0,0)),
strip.text.x = element_text(size = axis_titlesize, colour = "black"),
strip.background = element_rect(fill="gray88"),
panel.background = element_rect(fill="gray95",linetype = "solid", colour="white"),
legend.position = "none")
# ggsave("example_AR1_AR2_logBF_1_vs_2_10_by_5.png",dpi = 300,width = 10,height = 5)
# ggsave("example_AR1_AR2_logBF_1_vs_2_5_by_5.png",dpi = 300,width = 5,height = 5)
#--------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------
crit.df = data.frame()
crit.df = rbind(crit.df,data.frame(logbayesfactors[-4], value = logbayesfactors$logbayesfactor, crit = factor("LBF")))
crit.df = rbind(crit.df,data.frame(h_factors[-4], value = h_factors$hfactor, crit = factor("HF")))
relabelling = function(x){
if(x==levels(crit.df$type[1])[1]){return (factor(3))}
if(x==levels(crit.df$type[1])[2]){return (factor(4))}
}
crit.df$type = sapply(crit.df$type, function(x)relabelling(x))
#--------------------------------------------------------------------------------------------
case_label <- list(
'3'=expression(paste("Case 3: ",M[1]," nested in ", M[2],", both well-specified",sep="")),
'4'=expression(paste("Case 4: ",M[1]," nested in ", M[2], ", only ", M[2]," well-specified",sep=""))
)
case_labeller <- function(variable,value){
return(case_label[value])
}
labels.df = data.frame(x = c(900,900,rep(875,2)),
y = c(5.5,1.5,-280,-65),
text = rep(c("HF 1 vs. 2","log-BF 1 vs. 2"),4),
crit = factor(rep(c("HF","LBF"),4)),
type = factor(rep(3:4,each=2)))
hline.df = data.frame(y = c(-5,10), type = factor(3))
axis_titlesize = 18
axis_ticktextsize = 15
ggplot() +
geom_label(data = labels.df, aes(x,y,label = text,color=crit), fontface = "bold",size=5) +
theme(legend.position="none") +
scale_color_manual(values = colors[2:1]) +
geom_line(data = crit.df, aes(time, value, color = crit, group = interaction(crit,repl)),alpha=0.85) +
geom_hline(yintercept = 0,linetype=2) +
geom_hline(data = hline.df, aes(yintercept = y),linetype=2, alpha = 0) +
xlab("Number of observations") +
ylab("") +
# ylim(c(min(h_factors$hfactor),30+max(h_factors$hfactor))) +
facet_wrap( ~ type, ncol=2, scales="free", labeller = case_labeller) +
theme(axis.text.x = element_text(size = axis_ticktextsize),
axis.text.y = element_text(size = axis_ticktextsize),
axis.title.x = element_text(size = axis_titlesize, margin=margin(20,0,0,0)),
axis.title.y = element_text(size = axis_titlesize, angle = 90, margin = margin(0,20,0,0)),
strip.text.x = element_text(size = axis_titlesize, colour = "black"),
strip.text.y = element_text(size = axis_titlesize, colour = "black"),
strip.background = element_rect(fill="gray88"),
panel.background = element_rect(fill="gray95",linetype = "solid", colour="white"),
legend.position = "none")
# ggsave("example_AR1_AR2_15_by_6.png",dpi = 300,width = 15,height = 6)
# ggsave("example_AR1_AR2_15_by_6.pdf",dpi = 300,width = 15,height = 6)
|
e85c9aa76fe4fd9ec4b57c4cd1e1ac1b827bd56f
|
4bef412a97da9bde0e505bcaa4fe0c91d7226b6b
|
/R/BB_Prior_prob_phi.R
|
b1bddda0d5d85b083a68314a45360f8ed2504535
|
[] |
no_license
|
cran/BEDASSLE
|
3578af6a84a08fe6cbe3dc157c0139e053c4bbbd
|
e5fb9b7a2430614f806ff16e9465384e3600d496
|
refs/heads/master
| 2022-04-30T11:00:10.441717
| 2022-04-10T18:12:30
| 2022-04-10T18:12:30
| 17,677,835
| 2
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 76
|
r
|
BB_Prior_prob_phi.R
|
BB_Prior_prob_phi <-
function(phi){
stats::dexp(1/phi,rate=5,log=TRUE)
}
|
9cce927a9746a1a287e08165029beab1604acdb2
|
e3ce3ad557ebd51429ed7acfea936723149a8d4c
|
/R/makeMultiObjectiveFunction.R
|
b824d9c94f25530c8f686f1fb809f5ff2cf5b44c
|
[] |
permissive
|
jakobbossek/smoof
|
87512da9d488acfe3a7cc62aa3539a99e82d52ba
|
d65247258fab57d08a5a76df858329a25c0bb1b8
|
refs/heads/master
| 2023-03-20T02:05:12.632661
| 2023-03-08T13:59:27
| 2023-03-08T13:59:27
| 22,465,741
| 32
| 27
|
BSD-2-Clause
| 2022-01-21T10:02:19
| 2014-07-31T10:39:43
|
R
|
UTF-8
|
R
| false
| false
| 1,869
|
r
|
makeMultiObjectiveFunction.R
|
#' Generator for multi-objective target functions.
#'
#' @template arg_name
#' @template arg_id
#' @template arg_description
#' @template arg_fn
#' @template arg_has_simple_signature
#' @template arg_par_set
#' @param n.objectives [\code{integer(1)}]\cr
#' Number of objectives of the multi-objective function.
#' @template arg_noisy
#' @template arg_fn_mean
#' @param minimize [\code{logical}]\cr
#' Logical vector of length \code{n.objectives} indicating if the corresponding
#' objectives shall be minimized or maximized.
#' Default is the vector with all components set to \code{TRUE}.
#' @template arg_vectorized
#' @template arg_constraint_fn
#' @param ref.point [\code{numeric}]\cr
#' Optional reference point in the objective space, e.g., for hypervolume computation.
#' @return [\code{function}] Target function with additional stuff attached as attributes.
#' @examples
#' fn = makeMultiObjectiveFunction(
#' name = "My test function",
#' fn = function(x) c(sum(x^2), exp(x)),
#' n.objectives = 2L,
#' par.set = makeNumericParamSet("x", len = 1L, lower = -5L, upper = 5L)
#' )
#' print(fn)
#' @export
makeMultiObjectiveFunction = function(
name = NULL,
id = NULL,
description = NULL,
fn,
has.simple.signature = TRUE,
par.set,
n.objectives = NULL,
noisy = FALSE,
fn.mean = NULL,
minimize = NULL,
vectorized = FALSE,
constraint.fn = NULL,
ref.point = NULL) {
smoof.fn = makeObjectiveFunction(
name, id, description, fn,
has.simple.signature, par.set, n.objectives,
noisy, fn.mean, minimize, vectorized, constraint.fn
)
if (!is.null(ref.point)) {
assertNumeric(ref.point, len = n.objectives, any.missing = FALSE, all.missing = FALSE)
}
smoof.fn = setAttribute(smoof.fn, "ref.point", ref.point)
class(smoof.fn) = c("smoof_multi_objective_function", class(smoof.fn))
return(smoof.fn)
}
|
7376f58618041f78ca0c75c8345ce649693a5bf0
|
8ea032a59e2ef9b5d822d86b537e3e84e13d8322
|
/BOOK/Doit_R/가연/07_데이터 정제 - 빠진 데이터, 이상한 데이터 제거하기.R
|
d008b40839df80caf8300c0e4258f7fcc7527876
|
[] |
no_license
|
KimJiSeong1994/Data-Analysis_Study_Changwon
|
987e9067f3f067196d92f163f26688952919d440
|
f6e221e76f7557863263dd8e7e4849eb50f75b62
|
refs/heads/master
| 2021-08-17T09:42:15.679370
| 2020-08-26T08:35:50
| 2020-08-26T08:35:50
| 216,145,887
| 6
| 3
| null | null | null | null |
UHC
|
R
| false
| false
| 4,552
|
r
|
07_데이터 정제 - 빠진 데이터, 이상한 데이터 제거하기.R
|
#####07-1 빠진 데이터를 찾아라! - 결측치 정제하기#####
#결측치 찾기
df <- data.frame(sex=c("M","F",NA,"M","F"),
score=c(5,4,3,4,NA))
is.na(df) #결측치 확인(결측치=TRUE)
table(is.na(df)) #결측치 빈도 출력
table(is.na(df$sex)) #sex 결측치 빈도 출력
table(is.na(df$score)) #score 결측치 빈도 출력
mean(df$sex)
mean(df$score) #결측치가 포함된 데이터를 함수 적용하면 NA가 출력
#결측치 제거하기
library(dplyr)
df %>% filter(is.na(score)) #score가 NA인 데이터만 출력
df %>% filter(!is.na(score))
df_n <- df %>% filter(!is.na(score))
mean(df_n$score);sum(df_n$score)
df_n1 <- df %>% filter(!is.na(score)&!is.na(sex)) #여러 변수 결측치 동시제거
df_n2 <- na.omit(df) #결측치가 하나라도 있으면 모두 제거
#함수의 결측치 제외 기능
mean(df$score, na.rm=T) #na.rm=T 파라미터 기능 사용
sum(df$score, na.rm=T)
exam <- read.csv("C:/rtest/Doit_R/Data/csv_exam.csv")
exam[c(3,8,15),"math"] <- NA #3,8,15행의 math에 NA 할당
exam %>% summarise(mean_math=mean(math))
exam %>% summarise(mean_math=mean(math,na.rm=T))
exam %>% summarise(mean_math=mean(math,na.rm=T),
sum_math=sum(math,na.rm=T),
median_math=median(math,na.rm=T))
#평균값으로 결측치 대체하기
mean(exam$math, na.rm=T) #결측치 제외하고 math 평균 산출
exam$math <- ifelse(is.na(exam$math),55,exam$math) #math가 NA면 55로 대체
table(is.na(exam$math))
mean(exam$math)
#####연습문제#####
library(ggplot2)
mpg <- as.data.frame(ggplot2::mpg)
mpg[c(65,124,131,153,212),"hwy"] <- NA
#Q1. drv 변수와 hwy변수에 결측치가 몇 개 있는지 알아보기
table(is.na(mpg$drv))
table(is.na(mpg$hwy))
#Q2. filter()이용해 hwy 결측치 제거하고 어떤 drv(구동방식)의 hwy평균이 높은지 알아보기. 하나의 dplyr 구문으로 만들어야함.
mpg %>%
select(drv,hwy) %>%
filter(!is.na(hwy)) %>%
group_by(drv) %>%
summarise(mean_hwy=mean(hwy)) %>%
arrange(desc(mean_hwy))
mpg %>%
group_by(drv) %>%
summarise(mean_hwy=mean(hwy,na.rm=T)) %>%
arrange(desc(mean_hwy))
#Q2. 정답은 이거(select 없어도 됨)
mpg %>%
filter(!is.na(hwy)) %>%
group_by(drv) %>%
summarise(mean_hwy=mean(hwy))
#####07-2 이상한 데이터를 찾아라! - 이상치 정제하기#####
outlier <- data.frame(sex=c(1,2,1,3,2,1),
score=c(5,4,3,4,2,6))
#이상치 확인하기
table(outlier$sex)
table(outlier$score)
#결측 처리하기
outlier$sex <- ifelse(outlier$sex==3,NA,outlier$sex)
outlier
outlier$score <- ifelse(outlier$score>5,NA,outlier$score)
outlier
outlier %>%
filter(!is.na(sex)&!is.na(score)) %>%
group_by(sex) %>%
summarise(mean_score=mean(score))
#이상치 제거하기 - 극단적인 값
#극단치가 있으면 분석 결과가 왜곡될 수 있기 때문에 분석 전 제거해야함
#상자 그림으로 극단치 기준 정하기
boxplot(mpg$hwy)
#상자 그림 통계치 출력
#출력 결과는 차례대로 아래쪽 극단치 경계/1사분위수/중앙값/3사분위수/위쪽 극단치 경계
boxplot(mpg$hwy)$stats
#결측 처리하기
#12~37 벗어나면 NA 할당
mpg$hwy <- ifelse(mpg$hwy<12|mpg$hwy>37,NA,mpg$hwy)
table(mpg$hwy)
table(is.na(mpg$hwy))
mpg %>%
group_by(drv) %>%
summarise(mean_hwy=mean(hwy,na.rm = T))
#####연습문제2#####
mpg[c(10,14,58,93),"drv"] <- "k"
mpg[c(29,43,129,203),"cty"] <- c(3,4,39,42)
#Q1. drv에 이상치가 있는지 확인. 이상치 결측 처리 후 확인. 결측 처리 할 때는 %in% 기호 활용하기.
table(mpg$drv)
mpg$drv <- ifelse(mpg$drv=="k",NA,mpg$drv)
#Q1. 정답은 이거
table(mpg$drv)
mpg$drv <- ifelse(mpg$drv %in% c("4","f","r"), mpg$drv, NA)
table(mpg$drv)
#Q2. 상자 그림 이용해 cty에 이상치가 있는지 확인하기. 상자 그림의 통계치를 이용해 정상 범위 벗어난 값을 결측 처리하고 다시 상자 그림으로 확인
boxplot(mpg$cty)$stats
mpg$cty <- ifelse(mpg$cty>26|mpg$cty<9,NA,mpg$cty)
boxplot(mpg$cty)
#Q3. 이상치를 제외한 drv별로 cty평균이 어떻게 다른지 알아보기. 하나의 dplyr구문으로 만들어야함
mpg %>%
filter(!is.na(drv)&!is.na(cty)) %>%
group_by(drv) %>%
summarise(cty_mean=mean(cty))
|
e156792dc8f179c43edf59ec5638e6a8d342a0cd
|
e948abc55d3973e14fd3d2201796069213860272
|
/code/NYC_HVS_Analysis.R
|
32420c77189e0c986333875ba299f427b15abbae
|
[] |
no_license
|
madisonvolpe/evictions_nyc
|
426eba0911cd64a89fa3eea5b4eab385168a7bb0
|
7309888b3d9786879e644d242b854af75a2408c7
|
refs/heads/master
| 2020-06-05T10:29:02.971180
| 2019-06-17T19:51:09
| 2019-06-17T19:51:09
| 192,409,157
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 18,410
|
r
|
NYC_HVS_Analysis.R
|
suppressPackageStartupMessages(library(raster))
suppressPackageStartupMessages(library(survey))
suppressPackageStartupMessages(library(plyr))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(sf)) # for shapefiles
suppressPackageStartupMessages(library(maptools))
suppressPackageStartupMessages(library(rgdal))
suppressPackageStartupMessages(library(rgeos))
suppressPackageStartupMessages(library(reshape2))
suppressPackageStartupMessages(library(kableExtra))
suppressPackageStartupMessages(library(gridExtra))
suppressPackageStartupMessages(library(plotly))
suppressPackageStartupMessages(library(geosphere))
## Pre-processing!
options(scipen = 999)
# Before anything else read in shapefile and join the sub-borough areas to dataset
# read in shapefile
shp <- rgdal::readOGR(dsn = "data/NYC_HVS/NYC_Sub_borough_Area")
shp@data$id = shp@data$bor_subb
# transform shape for ggplot2
shp.points = fortify(shp, region="id")
shp.df = plyr::join(shp.points, shp@data, by="id")
rm(shp.points)
names(shp.df)[8] <- "sba"
# Bring in code for creating SEs from replicate weights
source("code/ReplicateWeights.R")
# Some notes this is data aggregated by the NYC HPD for their 2019 Data Expo - it is simplified, which is good for me
# It only includes only household-level occupied records
# It has both household weights + replicate weights !
# Load in Data
hvs <- read.csv("data/NYC_HVS/NYC_HVS_2017_HO.csv")
# The more detailed variable names are the first row - extract them out
varnames <- hvs[1,]
# remove more detailed names
hvs <- hvs[-1,]
# add sub-borough name from shpfile data to dataset it will make analysis easier!
hvs$sba <- as.numeric(as.character(hvs$sba))
hvs$sba.name <- shp.df$NAME[match(hvs$sba, shp.df$sba)]
sum(is.na(hvs$sba.name)) #check none missing
# recode race - hispanic origin
levels(hvs$X_1e) <- list(No=c("1"), Yes=c("2", "3", "4", "5", "6", "7"))
# recode race - all
levels(hvs$X_1f) <- list(White = c("1"), Black = c("2"), Asian = c("4", "5", "6", "7", "8"), PI = c("9"), Native = c("3"),
TwoRaces = c("10"))
# make a white v. minority categpry for simpler evaluation
hvs <- hvs %>%
mutate(RaceSimplified = case_when(
X_1e == "No" & X_1f == "White" ~ "White", #white people
X_1e == "Yes"& X_1f == "White" ~ "Minority", # white hispanics as minority
X_1e == "No" & X_1f != "White" ~ "Minority", # nonwhite non hispanics as minority
X_1e == "Yes"& X_1f != "White" ~ "Minority"))
# recode income, rather make continuous and then make income categories
hvs$hhinc <- as.numeric(as.character(hvs$hhinc))
hvs$hhinc[hvs$hhinc < 0] <- -1
hvs$hhinc[hvs$hhinc == 9999999] <- 0
#hvs$hhinc<-
cut(hvs$hhinc, seq(-1,3000000,25000))
# recode hh moving variable (add labels)
hvs$X_6 <- factor(hvs$X_6, levels = c("1","2","3","4","5","6","7","8",
"9", "10", "11", "12", "13", "14",
"15", "16", "17", "18", "19", "20",
"98", "99", "Reason for Moving"),
labels = c("Change in Employment Status", "Looking for Work", "Commuting Reasons", "School",
"Other Financial/ Employment Reason", "Needed Larger House or Apartment",
"Widowed, Divorced, Deceased", "Newly Married", "To be close to Relatives",
"Establish Separate Household", "Other Family Reason", "Wanted this neighborhood/ better services",
"Other neighborhood reason", "Wanted to own residence", "Wanted to rent residence",
"Wanted greater housing affordability", "Wanted better quality housing",
"Evicted, displaced, landlord harassment", "Other housing reason", "Any other reason",
"Not Reported", "Not Applicable", "X"))
# Examine the Reason for HH Moving Variable
# Subset moved people
moved <- filter(hvs, X_6 != "Not Reported" & X_6 != "Not Applicable")
# Okay so we see that (from respondents there are only 3,266 observations that moved) - obviously
# this is more when we add up the sampling weights etc, so let's do that now
# The full Sample ('Recently Moved') Estimated
# just add up the household weights!
# note household weights have 5 implied decimal places (so after will divide by 100,000)
moved$hhweight <- as.numeric(as.character(moved$hhweight))
sum(moved$hhweight)/100000
# The Full-Sample Recently Moved Estimate for all of NYC is : 781,263 (those who moved after 2013)
# Calculate the SE for this estimate
rep.wts.SE(moved) # the SE is 10,945.73
# Which reason for moving categories dominated?
moved %>%
select(X_6) %>%
group_by(X_6) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
ggplot(aes(x=X_6, y = n)) +
geom_bar(stat = "identity") +
coord_flip() +
ggtitle('Moved Reason - 2017 NYC HVS')
# When people said 'they were evicted/displaced', where do they reside now?
moved %>%
select(sba.name, X_6) %>%
filter(X_6 == 'Evicted, displaced, landlord harassment') %>%
group_by(sba.name) %>%
summarise(n=n()) %>%
arrange(desc(n)) %>%
ggplot(aes(x=sba.name, y=n))+
geom_bar(stat = 'identity') +
coord_flip() +
ggtitle("Where do Evicted/Displaced People Move?")
# Naive analysis!
# Estimates for how many people are moving to these neighborhoods after being evicted/displaced
# Will include the full sample estimate (sum of hhweight)
# Will also include the SE estimate from the replicate weights
# Basically Estimates by Neighborhood !
# will include estimate (sum of hhweight)
# SE estimate (using replicate weights)
evicted.displaced <- moved %>%
filter(X_6 == 'Evicted, displaced, landlord harassment')
EstbyNeighborhood.ed <- rep.wts.grp.SE(evicted.displaced, sba.name)
#create table estimate
# kable(EstbyNeighborhood.ed, format = 'html', col.names = c('SBA', 'Sample Estimate', 'Var', 'SE'))
# Let's map this so people could see where people move/stay after being evicted/displaced
# add neighborhoods to EstbyNeighborhood that are not in original df
SBA <- unique(shp.df$NAME)
SBAnoEvicted <- SBA[!SBA %in% EstbyNeighborhood.ed$sba.name]
SBAnoEvicted <- data.frame(sba.name = SBAnoEvicted, N0 = 0, Var = 0, SE = 0)
# adds those neighborhoods, where evicted/displaced people were not residing
EstbyNeighborhood.ed <- rbind(EstbyNeighborhood.ed, SBAnoEvicted)
names(EstbyNeighborhood.ed)[1] <- "NAME"
#now join these figures to shapefile
shp.df.evic <-join(shp.df, EstbyNeighborhood.ed, by = "NAME")
# lets map this
map.evic <- ggplot(shp.df.evic) +
aes(long,lat,group=group) +
geom_polygon(aes(fill=N0)) +
scale_fill_continuous(type = "viridis")+
geom_path(color="white") +
theme_bw() +
ggtitle("Neighborhoods where Evicted/Displaced/Harassed People Reside")
# How About People who Move for Greater Housing Affordability? Do people who move from displacement move to same areas
# as people seeking greater Housing Affordability?
moved %>%
select(sba.name, X_6) %>%
filter(X_6 == 'Wanted greater housing affordability') %>%
group_by(sba.name) %>%
summarise(n=n()) %>%
arrange(desc(n)) %>%
ggplot(aes(x=sba.name, y=n))+
geom_bar(stat = 'identity') +
coord_flip() +
ggtitle("Where do People that Want Housing Affordability Move?")
housing.afford <- moved %>%
filter(X_6 == 'Wanted greater housing affordability')
EstbyNeighborhood.ha <- rep.wts.grp.SE(housing.afford, sba.name)
# kable(EstbyNeighborhood.ha, format = 'html', col.names = c('SBA', 'Sample Estimate', 'Var', 'SE'))
# add neighborhoods to EstbyNeighborhood.ha that are not in original df
SBA <- unique(shp.df$NAME)
SBAnoha <- SBA[!SBA %in% EstbyNeighborhood.ha$sba.name]
SBAnoha <- data.frame(sba.name = SBAnoha, N0 = 0, Var = 0, SE = 0)
# adds those neighborhoods, where evicted/displaced people were not residing
EstbyNeighborhood.ha <- rbind(EstbyNeighborhood.ha, SBAnoha)
names(EstbyNeighborhood.ha)[1] <- "NAME"
#now join these figures to shapefile
shp.df.ha <-join(shp.df, EstbyNeighborhood.ha, by = "NAME")
# create map
map.ha<-ggplot(shp.df.ha) +
aes(long,lat,group=group) +
geom_polygon(aes(fill=N0)) +
scale_fill_continuous(type = "viridis")+
geom_path(color="white") +
theme_bw() +
ggtitle("Neighborhoods where People that Moved Seeking Housing Affordability Reside")
# Combination of maps
map.evic
map.ha
######### Go a Level Deeper and Expand on this Analysis Adding some Racial Component #####
#### EVICTIONS/ DISPLACEMENT
# Evicted/displaced/harassed in total
sum(evicted.displaced$hhweight)/100000 #13,623.21 in total were evicted in 2017
rep.wts.SE(evicted.displaced) # the SE is 1816.878
# How many people are evicted/displaced/harassed that are a minority (Full Sample Estimate)
evicted.minority <- moved %>%
filter(X_6 == 'Evicted, displaced, landlord harassment' & RaceSimplified == 'Minority')
sum(evicted.minority$hhweight)/100000 #9,692.036 minorities are displaced throughout NYC
rep.wts.SE(evicted.minority) # the SE is 1574.457!
# How many people are evicted/displaced/harassed that are white (Full Sample Estimate)
evicted.white <- moved %>%
filter(X_6 == 'Evicted, displaced, landlord harassment' & RaceSimplified == 'White')
sum(evicted.white$hhweight)/100000 #3931.175 whites are displaced throughout NYC
rep.wts.SE(evicted.white) # the SE is 1030.248!
# Not a surprise but most evictions/displaced/landlord harassments are for minorities (blacks, hispanics, asians,etc.)
# What neighborhoods are recently evicted/displaced people ending up in based on minority v. nonminority status ?
moved %>%
select(sba.name,borough,X_6, RaceSimplified, hhweight) %>%
filter(X_6 == 'Evicted, displaced, landlord harassment') %>%
group_by(sba.name, RaceSimplified) %>%
summarise(n=n(),amt=ceiling((sum(hhweight)/100000))) %>%
arrange(desc(amt)) %>%
ggplot(aes(x=sba.name, y = n, fill = RaceSimplified)) +
geom_bar(stat = 'identity', position = 'dodge') +
facet_grid(~RaceSimplified) +
coord_flip() +
ggtitle("Where do Evicted/Displaced People Reside based on Race?")
# create a table for this with Full Sample estimate, Var, and SE
EstbyNeighborhood.edRace <- rep.wts.2grps.SE(evicted.displaced, sba.name, RaceSimplified)
# kable(EstbyNeighborhood.edRace, format = 'html', col.names = c('SBA',"Status",'Sample Estimate', 'Var', 'SE'))
# let's map this
#before I join with shpfile I need to make this wide and where only interested in Full Sample estimate so that is what I'll keep
EstbyNeighborhood.edRaceWide <- spread(EstbyNeighborhood.edRace[1:3], RaceSimplified, N0)
EstbyNeighborhood.edRaceWide <- EstbyNeighborhood.edRaceWide %>% mutate_at(vars(Minority, White),
function(x) as.numeric(as.character(x)))
EstbyNeighborhood.edRaceWide$Minority[is.na(EstbyNeighborhood.edRaceWide$Minority)] <- 0
EstbyNeighborhood.edRaceWide$White[is.na(EstbyNeighborhood.edRaceWide$White)] <- 0
# add in neighborhoods not in EstbyNeighborhood.edRaceWide
SBA <- unique(shp.df$NAME)
SBAnoEvictedRaceWide <- SBA[!SBA %in% EstbyNeighborhood.edRaceWide$sba.name]
SBAnoEvictedRaceWide <- data.frame(sba.name = SBAnoEvictedRaceWide, Minority = 0, White = 0)
# adds those neighborhoods, where evicted/displaced people were not residing
EstbyNeighborhood.edRaceWide<- rbind(EstbyNeighborhood.edRaceWide, SBAnoEvictedRaceWide)
names(EstbyNeighborhood.edRaceWide)[1] <- "NAME"
# join to shapefile with (N0 estimate for all evicted/displaced already -- shp.df.evic)
shp.df.evic <-join(shp.df.evic, EstbyNeighborhood.edRaceWide, by = "NAME")
# make map
#need to find centroid
# Get polygons centroids
centroids <- as.data.frame(centroid(shp))
colnames(centroids) <- c("long_cen", "lat_cen")
centroids <- data.frame("id" = shp$bor_subb, centroids)
# Join centroids with dataframe
shp.df.evic <- plyr::join(shp.df.evic, centroids, by = "id")
# Minority Map
ggplot(shp.df.evic) +
aes(long,lat,group=group) +
geom_polygon(aes(fill=N0)) +
scale_fill_continuous(low = '#E0EEEE', high = '#0000FF')+
labs(fill='Level of Evicted/Displaced') +
geom_point(aes(x=long_cen,y=lat_cen,col = Minority), alpha = 0.9)+
scale_color_continuous(low = '#FFFFFF', high = '#CD0000')+
geom_path(color="white") +
theme_bw() +
ggtitle("Where do Evicted/Displaced Minorities Reside?")
# White Map
ggplot(shp.df.evic) +
aes(long,lat,group=group) +
geom_polygon(aes(fill=N0)) +
scale_fill_continuous(low = '#E0EEEE', high = '#0000FF')+
labs(fill='Level of Evicted/Displaced') +
geom_point(aes(x=long_cen,y=lat_cen,col = White), alpha = 0.9)+
scale_color_continuous(low = '#FFFFFF', high = '#CD0000')+
geom_path(color="white") +
theme_bw() +
ggtitle("Where do Evicted/Displaced White People Reside?")
#### WANTED GREATER HOUSING AFFORDABILITY
# Let's make the white vs/ minority affordability estimates (tables + maps)
# How many people moved for greater affordability
sum(housing.afford$hhweight)/100000 #36,101.14 moved for greater housing affordability
rep.wts.SE(housing.afford) # the SE is 3027.933
# How many people moved for greater housing affordability that are a minority (Full Sample Estimate)
ha.minority <- moved %>%
filter(X_6 == "Wanted greater housing affordability" & RaceSimplified == 'Minority')
sum(ha.minority$hhweight)/100000 #22550.74 minorities moved for greater housing affordability
rep.wts.SE(ha.minority) # the SE is 2681.442!
# How many people moved for greater housing affordability that are white (Full Sample Estimate)
ha.white <- moved %>%
filter(X_6 == "Wanted greater housing affordability" & RaceSimplified == 'White')
sum(ha.white$hhweight)/100000 #13550.4 whites moved for greater housing affordability
rep.wts.SE(ha.white) # the SE is 1842.22!
# where do whites v. minorities move when they seek greater housing affordability...
moved %>%
select(sba.name,borough,X_6, RaceSimplified, hhweight) %>%
filter(X_6 == "Wanted greater housing affordability") %>%
group_by(sba.name, RaceSimplified) %>%
summarise(n=n(),amt=ceiling((sum(hhweight)/100000))) %>%
arrange(desc(amt)) %>%
ggplot(aes(x=sba.name, y = n, fill = RaceSimplified)) +
geom_bar(stat = 'identity', position = 'dodge') +
facet_grid(~RaceSimplified) +
coord_flip() +
ggtitle("Where do People Move for Greater Housing Affordability?")
# create a table for this with Full Sample estimate, Var, and SE
EstbyNeighborhood.haRace <- rep.wts.2grps.SE(housing.afford, sba.name, RaceSimplified)
# kable(EstbyNeighborhood.haRace, format = 'html', col.names = c('SBA',"Status",'Sample Estimate', 'Var', 'SE'))
# making maps
# let's map this
#before I join with shpfile I need to make this wide and where only interested in Full Sample estimate so that is what I'll keep
EstbyNeighborhood.haRaceWide <- spread(EstbyNeighborhood.haRace[1:3], RaceSimplified, N0)
EstbyNeighborhood.haRaceWide <- EstbyNeighborhood.haRaceWide %>% mutate_at(vars(Minority, White),
function(x) as.numeric(as.character(x)))
EstbyNeighborhood.haRaceWide$Minority[is.na(EstbyNeighborhood.haRaceWide$Minority)] <- 0
EstbyNeighborhood.haRaceWide$White[is.na(EstbyNeighborhood.haRaceWide$White)] <- 0
# add in neighborhoods not in EstbyNeighborhood.edRaceWide
SBAnohaRaceWide <- SBA[!SBA %in% EstbyNeighborhood.haRaceWide$sba.name]
SBAnohaRaceWide <- data.frame(sba.name = SBAnohaRaceWide, Minority = 0, White = 0)
# adds those neighborhoods, where evicted/displaced people were not residing
EstbyNeighborhood.haRaceWide<- rbind(EstbyNeighborhood.haRaceWide, SBAnohaRaceWide)
names(EstbyNeighborhood.haRaceWide)[1] <- "NAME"
# join to shapefile with (N0 estimate for all evicted/displaced already -- shp.df.evic)
shp.df.ha <-join(shp.df.ha, EstbyNeighborhood.haRaceWide, by = "NAME")
# join centroids
shp.df.ha <- plyr::join(shp.df.ha, centroids, by = "id")
# Maps
# Minority Map
ggplot(shp.df.ha) +
aes(long,lat,group=group) +
geom_polygon(aes(fill=N0)) +
scale_fill_continuous(low = '#E0EEEE', high = '#0000FF')+
labs(fill='Level of Housing Affordability') +
geom_point(aes(x=long_cen,y=lat_cen,col = Minority), alpha = 0.9)+
scale_color_continuous(low = '#FFFFFF', high = '#CD0000')+
geom_path(color="white") +
theme_bw() +
ggtitle("Where do Minorities seeking Housing Affordability Reside?")
# White Map
ggplot(shp.df.ha) +
aes(long,lat,group=group) +
geom_polygon(aes(fill=N0)) +
scale_fill_continuous(low = '#E0EEEE', high = '#0000FF')+
labs(fill='Level of Evicted/Displaced') +
geom_point(aes(x=long_cen,y=lat_cen,col = White), alpha = 0.9)+
scale_color_continuous(low = '#FFFFFF', high = '#CD0000')+
geom_path(color="white") +
theme_bw() +
ggtitle("Where do White People seeking Housing Affordability Reside?")
######### Go another Level Deeper and Expand on this Analysis Adding Income #####
|
b9468a781002cb7dbbd9dc2178424690e543468f
|
618e4a8505b6a5dfa8e768b474bb2bc0352f39b3
|
/server.r
|
a6fcf4f6e758a22cdef41d1632dcb975cf0eec5e
|
[] |
no_license
|
eua89/buoyviz_r_shiny
|
1c660b21279b182283a40365a21f28558c7d4895
|
c1bef095c43867ff32fc81e022489de55232ea45
|
refs/heads/master
| 2021-01-21T18:24:59.099422
| 2017-05-05T14:49:37
| 2017-05-05T14:49:37
| 92,047,336
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,022
|
r
|
server.r
|
library(quantmod)
library(ggplot2)
library(scales)
#source("helper.r")
shinyServer(function(input, output) {
buoy_data=reactive({
fn = "http://localhost:5000/WAQM3.csv"
fn = paste("http://localhost:5000/",input$select1,".csv", sep="")
f = read.csv(fn,header=T, sep=",")
f$Date <- format(as.POSIXct(paste(f$X.YY, f$MM, f$DD, f$hh ,f$mm, sep = "/"), format= "%Y/%m/%d/%H/%M"))
#f$POSDATE <- as.POSIXct(f$Date)
colnames(f)[10] <- "O2"
f
# Instead of running the above function here we could load it from a helper.r file
# read_csv(input$select1)
})
output$table <- renderTable({
data <- buoy_data()
data[6:16]
})
output$plot <- renderPlot({
data <- buoy_data()
POSDATE <- as.POSIXct(data$Date)
ggplot(data, aes_string(x = "POSDATE", y = input$select2)) +
geom_line() +
scale_x_datetime(labels = date_format("%Y-%m-%d %mm"))
})
output$text <- renderText({paste(input$select2, "of buoy with ID=", input$select1 )})
})
|
2aaff223b91e46bb018147d878cbb319e0091390
|
d8ec0122f6b914db27fdcb9d657195c18b925aa9
|
/Introduction-to-R/ggplot2.R
|
9c328e9224e2bf3205ff9f99d4bba96ed9b1219a
|
[] |
no_license
|
sssridha/R
|
5c8576849a26e7beecc2325f5ec3ce782ebb63c4
|
9d061143286a96c5b89818538d7251e2997bc788
|
refs/heads/master
| 2020-07-04T02:17:24.488420
| 2016-11-19T06:05:26
| 2016-11-19T06:05:26
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,350
|
r
|
ggplot2.R
|
### ggplot2 is the most common used graphics package in R. It has great flexibility,
### allows for a wide variety of graphs, and has an easy to understand grammar.
### Additional resources:
### https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
### http://docs.ggplot2.org/current/
# Install the ggplot2 library and load it.
install.packages("ggplot2")
library(ggplot2)
# Assign the diamonds data
# View the structure of diamonds
# Create a ggplot and assign just price to the aesthetics.
# Create a histogram of price by adding a geometry.
# Assign the ggplot to p without a geometry.
# Create a density plot by adding geom_density() to p
# Create a histogram of p, set bins = 50, and add a title.
# Create a scatter plot carat and price
# Color the scatterplot by the cut variable
# Color the scatterplot by the x variable
# Color the scatterplot blue by placing the color inside geom_point()
# Add a title and customize the x and y axis
# Color the scatterplot blue by placing the color inside geom_point()
# Add a title and customize the x and y axis
## Practice:
## Assign the economics data
## View the structure of economics
## create a histogram of pce
## create a histogram of psavert
## Create a scatterplot of psavert and pce, add geom_smooth() to the plot
## Create a scatterplot of psavert and pce, add geom_smooth(method = lm) to the plot.
## Add a title, label the x-axis "Personal Savings Rate", and y-axis "Personal Consumption Expenditures".
# Line Graph
# Create the variable unemployment_rate in the economics data frame by dividing unemploy by pop.
# Create a line graph of unemployment_rate on date
# Bar Chart
str(diamonds)
# Create a barchart of the count of cut
# Use dplyr to create a barchart of mean price by cut, add the fill option
# Create a scatterplot of carat and price. Use facet_wrap on cut.
## Practice:
setwd("~/Documents/Introduction-to-R")
titanic1 = read.csv("titanic1.csv")
titanic2 = read.csv("titanic2.csv")
titanic = merge(titanic1, titanic2, by = "Id")
str(titanic)
## Create a bar plot of the survival rates on pclass.
## Add a title and custom axis labels.
## Create a bar plot of the survival rates of the pclass.
## Use facet_wrap with the sex variable.
## Add a title and custom axis labels.
|
52295cfed1c55dd0b7aca5ee59cf0302f82662ab
|
2bec5a52ce1fb3266e72f8fbeb5226b025584a16
|
/BigVAR/man/show-methods.Rd
|
af3ed58a261ebdbbbe1ee74e14a9a1612c2fa2ef
|
[] |
no_license
|
akhikolla/InformationHouse
|
4e45b11df18dee47519e917fcf0a869a77661fce
|
c0daab1e3f2827fd08aa5c31127fadae3f001948
|
refs/heads/master
| 2023-02-12T19:00:20.752555
| 2020-12-31T20:59:23
| 2020-12-31T20:59:23
| 325,589,503
| 9
| 2
| null | null | null | null |
UTF-8
|
R
| false
| true
| 780
|
rd
|
show-methods.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BigVARObjectClass.R
\docType{methods}
\name{show.BigVAR}
\alias{show.BigVAR}
\alias{show,BigVAR-method}
\title{Default show method for an object of class BigVAR}
\usage{
\S4method{show}{BigVAR}(object)
}
\arguments{
\item{object}{\code{BigVAR} object created from \code{ConstructModel}}
}
\value{
Displays the following information about the BigVAR object:
\itemize{
\item{Prints the first 5 rows of \code{Y}}
\item{ Penalty Structure}
\item{ Relaxed Least Squares Indicator}
\item{Maximum lag order}
\item{ VARX Specifications (if applicable)}
\item{Start, end of cross validation period}
}
}
\description{
Default show method for an object of class BigVAR
}
\seealso{
\code{\link{constructModel}}
}
|
6907bccec3416bbb22bd2836a35a12e38583d3fa
|
932b7a024ad453706c4a03a9e9791c5a5512c892
|
/man/getBinaryConfusionMatrix.Rd
|
e23775f86b43dca4ff29d0144a9022cd75610e5a
|
[] |
no_license
|
dputler/AlteryxPredictive
|
428eb2f836999cd44312e7a8548c5c95c5a29fb1
|
e35a0795c86e19cad7b022a3044a10158dbb5511
|
refs/heads/master
| 2020-03-06T19:20:54.612405
| 2018-04-02T18:24:30
| 2018-04-02T18:24:30
| 127,025,821
| 0
| 0
| null | 2018-03-27T17:56:39
| 2018-03-27T17:56:38
| null |
UTF-8
|
R
| false
| true
| 526
|
rd
|
getBinaryConfusionMatrix.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interactiveutils.R
\name{getBinaryConfusionMatrix}
\alias{getBinaryConfusionMatrix}
\title{Helpful wrapper around fitted and actual values for generating confusion matrix}
\usage{
getBinaryConfusionMatrix(fitted_values, actual_values)
}
\arguments{
\item{fitted_values}{fitted values}
\item{actual_values}{actual values}
}
\value{
confusion matrix
}
\description{
Helpful wrapper around fitted and actual values for generating confusion matrix
}
|
af822deeb9f06017f60a81753c17ddffc534c7fd
|
36c8308493104441ae3aa3606ee63e2f555f86b1
|
/man/convert.stress.level.Rd
|
028a3b20ee4ad91c123eabaadb02c1c74ad7d6ea
|
[] |
no_license
|
cran/ALTopt
|
26e07bd47875d27311aa89bf1e43b91c9bea8481
|
325843e8028f0c6e2f086c0ec04a73e05d76e9ce
|
refs/heads/master
| 2020-04-29T09:34:57.600803
| 2019-12-12T22:10:02
| 2019-12-12T22:10:02
| 29,384,214
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,560
|
rd
|
convert.stress.level.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ALTopt.R
\name{convert.stress.level}
\alias{convert.stress.level}
\title{Coding and decoding stress level}
\usage{
convert.stress.level(lowStLv, highStLv, actual = NULL, stand = NULL)
}
\arguments{
\item{lowStLv}{a numeric vector containing the actual lowest stress level
of each stress variable in design region.}
\item{highStLv}{a numeric vector containing the actual highest stress level
of each stress variable in design region.}
\item{actual}{a data frame or numeric vector containing the design points
in actual units.}
\item{stand}{a data frame or numeric vector containing the design points
in standardized units.}
}
\value{
When \code{actual} is provided, the function converts it to the
standardized units and when \code{stand} is provided, the function converts
it to the actual units.
}
\description{
Convert the stress levels from the actual levels to standardized levels,
and vice versa.
}
\examples{
\dontrun{
# Generating D optimal design in coded unit.
Design <- altopt.rc(optType = "D", N = 100, tc = 100, nf = 2, alpha = 1,
formula = ~x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01))
# Transform the coded unit to actual stress variable's level.
convert.stress.level(lowStLv = c(34.834, 4.094), highStLv = c(30.288, 4.5),
stand = Design$opt.design.rounded)
# Transform the actual stress level to coded units.
use <- c(38.281, 3.219)
convert.stress.level(lowStLv = c(34.834, 4.094), highStLv = c(30.288, 4.5),
actual = use)
}
}
|
c02a0fd78a6acc1d095ad4d473efb30ca3b01d4c
|
5a1bf799691965bfba48f1733b2fdbbfe2050c58
|
/R/database_functions.R
|
fcc6c3fbf8c99c384fa49b579c45ace4ff29adb1
|
[] |
no_license
|
vedhav/shipdashboard
|
33e2cc367def8db6700b22ead79fe1983f889fc9
|
e41d3f8045bc2ce48e3921fe8f341129d7c07e04
|
refs/heads/main
| 2023-01-24T11:10:17.906535
| 2020-11-21T19:16:53
| 2020-11-21T19:16:53
| 313,908,951
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,258
|
r
|
database_functions.R
|
# Kills all the unclosed db connections that occur due to exceptions or timeouts
kill_dbx_connections <- function () {
all_cons <- dbListConnections(RMySQL::MySQL())
for(con in all_cons)
dbx::dbxDisconnect(con)
print(paste(length(all_cons), " connections killed."))
print("If you have not set up the MySQL database please swith to local_file branch of the repo { git checkout local_file } so you can use this app without it")
}
#' To insert new values inside the MySQL table
#'
#' @param table_name A string which contains the MySQL table name where the data has to be inserted
#' @param values A tbl_df/tbl/data.frame which is the same structure as the MySQL table
#' @param db_name A string which contains the name of the database
#' @param host_name A string which contains the username to access the database
#' @param host_password A string which contains the password to access the database
#' @param host_ip A string which contains the host address of the database
#'
#' @return Returns the result obtained after the insert, NULL if the data was inserted successfully
insert_into_db <- function(table_name, values, db_name = default_db_name, host_name = default_host_name,
host_password = default_host_password, host_ip = default_host_ip) {
#adding support for '\' - extending dbx
for(field in 1:length(values)) {
if(typeof(values[[field]]) == 'character') {
values[[field]] <- gsub("[\\]", "\\\\\\\\", values[[field]])
}
}
conn <- tryCatch({
dbx::dbxConnect(
adapter = "mysql",
user = host_name,
password = host_password,
host = host_ip,
port = default_db_port,
dbname = db_name
)
}, error = function(err) {
kill_dbx_connections()
dbx::dbxConnect(
adapter = "mysql",
user = host_name,
password = host_password,
host = host_ip,
port = default_db_port,
dbname = db_name
)
})
result <- suppressWarnings(dbx::dbxInsert(conn, table_name, values, batch_size = 1000))
on.exit(dbx::dbxDisconnect(conn))
return(result)
}
#' To fetch data from database using select query
#'
#' @param query A string with the select query
#' @param params A list of optional parameters that fill the ? in the query
#' @param db_name A string which contains the name of the database
#' @param host_name A string which contains the username to access the database
#' @param host_password A string which contains the password to access the database
#' @param host_ip A string which contains the host address of the database
#'
#' @return A tibble which is the result of the query
get_data_from_db <- function(query, params = NULL, db_name = default_db_name, host_name = default_host_name,
host_password = default_host_password, host_ip = default_host_ip) {
if(query == "") {
stop("The query string cannot be empty")
}
conn <- tryCatch({
dbx::dbxConnect(
adapter = "mysql",
user = host_name,
password = host_password,
host = host_ip,
port = default_db_port,
dbname = db_name
)
}, error = function(err) {
kill_dbx_connections()
dbx::dbxConnect(
adapter = "mysql",
user = host_name,
password = host_password,
host = host_ip,
port = default_db_port,
dbname = db_name
)
})
result <- NULL
if(!is.null(params)) {
#adding support for '\' - extending dbx
for(field in 1:length(params))
{
if(typeof(params[[field]]) == 'character')
{
params[[field]] <- gsub("[\\]", "\\\\\\\\", params[[field]])
}
}
result <- suppressWarnings(dbx::dbxSelect(conn, query, params))
} else {
result <- suppressWarnings(dbx::dbxSelect(conn, query))
}
#To remove the unnecessary results that we get from executing a stored procedure
while(RMySQL::dbMoreResults(conn)) {
try(RMySQL::dbNextResult(conn))
}
on.exit(dbx::dbxDisconnect(conn))
return(tibble::as_tibble(result))
}
|
3da4e0b0bb2e30c59a72ca86a7983c9efe756099
|
0f2d3dcaf08ef634b59e3d7d2a88ee41fea94596
|
/ycombinator-plot/code.R
|
a85c7aa2b36953b454ca12427350d59bfbe69442
|
[
"MIT"
] |
permissive
|
kaustavSen/dataviz-makeover
|
80b944dcd7fd1aa4f88e44913b8e66c202dac95a
|
75eb709fee36945deeee686a193fb496d34f5061
|
refs/heads/main
| 2023-08-29T04:34:30.897398
| 2021-10-30T13:09:53
| 2021-10-30T13:09:53
| 367,793,854
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,210
|
r
|
code.R
|
library(tidyverse)
library(ggthemes)
library(ggtext)
library(scales)
data <- read_csv("ycombinator-plot/data.csv")
subtitle <- "% of companies funded each year which have their main focus in the emerging markets of Africa, Latin America & Middle East"
data %>%
mutate(percent = parse_number(percent) / 100) %>%
ggplot(aes(year, percent)) +
geom_step(color = "#B40F20", size = 1.05) +
scale_x_continuous(labels = number_format(accuracy = 1, big.mark = "")) +
scale_y_continuous(labels = percent_format(accuracy = 1), limits = c(0, 0.3),
expand = expansion(mult = 0), breaks = seq(0, 0.3, 0.1)) +
coord_cartesian(clip = "off") +
labs(
title = "Y Combinator's <span style='color: #B40F20'>emerging</span> markets focus",
subtitle = str_wrap(subtitle)
) +
theme_fivethirtyeight(base_family = "Inter") +
theme(
plot.title.position = "plot",
plot.title = element_markdown(family = "Poppins", hjust = 0.5, margin = margin(b = 5)),
plot.subtitle = element_text(hjust = 0.5, lineheight = 1.2, color = "grey60",
size = rel(0.9), margin = margin(b = 10))
)
ggsave("ycombinator-plot/plot.png", width = 8, height = 6)
|
754bd186f8b1090c0455dcd04ac2358d1eecf36c
|
0c3fa446b7aa8e5f48201e139fd7e19c351ae54d
|
/src/prepare_ecdc_data/R/corrections_02August.R
|
125c062335b564784bd1f219a987253b1caa9962
|
[] |
no_license
|
mrc-ide/covid19-forecasts-orderly
|
477b621129ebc93dbd4e75ae4d54fbda0ad1669c
|
2210ea70dc7b38d91fc1678ab0c2bb9b83c5ddcb
|
refs/heads/main
| 2023-02-06T17:58:18.937033
| 2022-08-02T16:39:50
| 2022-08-02T16:39:50
| 254,094,782
| 33
| 12
| null | 2022-08-02T16:39:51
| 2020-04-08T13:25:08
|
R
|
UTF-8
|
R
| false
| false
| 2,272
|
r
|
corrections_02August.R
|
#####################################################################
######################################################################
######################################################################
######################################################################
########### Corrections 02nd August ##################################
######################################################################
######################################################################
######################################################################
last_week <- seq(from = as.Date("2020-07-26"), to = as.Date("2020-08-02"), by = "1 day")
who_Guatemala <- who[who$country == "Guatemala" & who$date_reported %in% last_week, ]
ecdc_Guatemala <- raw_data[raw_data$`Countries.and.territories` == "Guatemala", ]
ecdc_Guatemala <- ecdc_Guatemala[ecdc_Guatemala$DateRep %in% last_week, ]
df <- dplyr::left_join(who_Guatemala, ecdc_Guatemala, by = c("date_reported" = "DateRep"))
df <- dplyr::arrange(df, desc(date_reported))
raw_data$Cases[raw_data$`Countries.and.territories` == "Guatemala" & raw_data$DateRep %in% df$date_reported] <- df$new_cases
raw_data$Deaths[raw_data$`Countries.and.territories` == "Guatemala" & raw_data$DateRep %in% df$date_reported] <- df$new_deaths
raw_data$Deaths[raw_data$`Countries.and.territories` == "Kosovo" & raw_data$DateRep == "2020-08-01"] <- 15
raw_data$Deaths[raw_data$`Countries.and.territories` == "Kosovo" & raw_data$DateRep == "2020-08-02"] <- 10
raw_data$Deaths[raw_data$`Countries.and.territories` == "Saudi_Arabia" & raw_data$DateRep == "2020-07-31"] <- 26
raw_data$Deaths[raw_data$`Countries.and.territories` == "Saudi_Arabia" & raw_data$DateRep == "2020-08-01"] <- 24
raw_data$Deaths[raw_data$`Countries.and.territories` == "Saudi_Arabia" & raw_data$DateRep == "2020-08-02"] <- 21
raw_data$Deaths[raw_data$`Countries.and.territories` == "Sudan" & raw_data$DateRep == "2020-08-02"] <- 6
raw_data$Cases[raw_data$`Countries.and.territories` == "Sudan" & raw_data$DateRep == "2020-08-02"] <- 94
raw_data$Deaths[raw_data$`Countries.and.territories` == "Venezuela" & raw_data$DateRep == "2020-07-31"] <- 4
raw_data$Cases[raw_data$`Countries.and.territories` == "Venezuela" & raw_data$DateRep == "2020-07-30"] <- 3
|
17c805cb769d1a49900c584cb563f7faded7ff79
|
49e30fa0003f379dc1aa21396f5678927296e629
|
/Basket/code/model/add_none.R
|
4a87d5d577d87f97c71aba97329cbafee66a24cc
|
[] |
no_license
|
xiaofeifei1800/Kaggle
|
8ce6e74b4bf7e379ae4c08afa984dfb49311148c
|
ece120bdc8fb37ac6f409548abb04c1d503332e5
|
refs/heads/master
| 2021-09-10T17:02:19.107623
| 2018-03-29T20:40:41
| 2018-03-29T20:40:41
| 63,451,543
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 705
|
r
|
add_none.R
|
test = fread("/Users/xiaofeifei/I/Kaggle/Basket/result1.csv")
none = fread("/Users/xiaofeifei/I/Kaggle/Basket/None1.csv")
none = none[-1,-3]
none = none[none$V2 == "None"]
colnames(none) = c("order_id","product_id")
none$reordered = 1
test$reordered <- (test$reordered > 0.21) * 1
test = rbind(test, none)
submission <- test %>%
filter(reordered == 1) %>%
group_by(order_id) %>%
summarise(
products = paste(product_id, collapse = " ")
)
missing <- data.frame(
order_id = unique(test$order_id[!test$order_id %in% submission$order_id]),
products = "None"
)
submission <- submission %>% bind_rows(missing) %>% arrange(order_id)
fwrite(submission, file = "none_test.csv", row.names = F)
|
bc25203b162a7f524fa66d7269219dad188c2943
|
c96d12a58c06ae3d19cd48ef60fc2051ba73d7d8
|
/R/9_edge_bundling_alternative.R
|
61242fe7bc83ef0f2624a8586e6f275b6701fca5
|
[] |
no_license
|
rafapereirabr/stockholm_access_travelbehavior
|
c5757398d532da66d8066caaedbe64e6ceeccc8f
|
a49c3d67dbbd2c728d34c422de91ec35d837fe50
|
refs/heads/main
| 2023-08-08T13:15:03.762770
| 2023-07-26T20:18:08
| 2023-07-26T20:18:08
| 380,514,032
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,266
|
r
|
9_edge_bundling_alternative.R
|
setDT(df_plot_income)
df_plot_income[, count := 1]
df_plot_income <- df_plot_income[order(group, period, income)]
ddd <- df_plot_income[, .(count = sum(count)), by=.(group,x, y)]
unique(ddd)
df_line <- sfheaders::sf_linestring(obj = ddd,
x = 'x',
y= 'y',
linestring_id = 'group',
keep = T)
head(df_line)
library(stplanr)
library(sf)
m <- subset(df_line, income == 'Low')
m <- stplanr::overline(sl = m, attrib = "count", fun = sum, ncores = 4, simplify = F)
head(m)
t <- ggplot(data = m, aes(color=count)) +
geom_sf( alpha=.1) +
coord_sf()
#
# ggplot(mm, aes(factor(base_area_orig), factor(base_area_dest), fill= count)) +
# geom_tile() +
# st_coordinates()
#
# scale_fill_viridis_c(option = 'E', direction = ) +
# facet_wrap(.~income, nrow=1 ) +
# theme_classic() +
# theme(axis.line=element_blank(),
# axis.text=element_blank(),
# axis.ticks=element_blank(),
# axis.title=element_blank(),
# strip.background=element_rect(colour=NA, fill="gray95") )
ggsave(t, file= './figures/test_count_overLOW.png', dpi=300,
width = 16, height = 8, units = 'cm')
|
19bf498b4f238572919d5d8e257517e347e6e6cb
|
885b9041aa386150e9b4afa077ba8c6c157858c5
|
/ui.R
|
beb3aee444eb8440f9f12c580cf1f169aba78739
|
[] |
no_license
|
liujiashen9307/Hoitalent-small-project
|
2a5fff22bf030e2ef0ad98c1ea5299e782c34d23
|
8a204f991d842343088d6146de6dff5e5cc5ee16
|
refs/heads/master
| 2020-09-15T22:37:35.509443
| 2016-12-04T19:04:26
| 2016-12-04T19:04:26
| 73,515,169
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,310
|
r
|
ui.R
|
library(shiny)
library(shinythemes)
library(plotly)
library(wordcloud)
dt <- read.csv('HT.csv')
bg <- read.csv('Background.csv')[c(2:16,19),1]
bg<- as.character(bg)
shinyUI(fluidPage(theme = shinytheme("sandstone"),
titlePanel(h3(strong('Find the position you may be interested in'))),
sidebarPanel(
conditionalPanel('input.tab=="Dashboard"',
h4("Description"),
p(h5('By selecting two of your most matched backgrounds, you can check:')),
p(h5('a. What extra backgrounds do positions you can apply need')),
p(h5('b. Which company may be your potential employer?')),
p(h5('c. Which language skills you better have for being more competitive ')),
p(h5('d. How many years of experience and degree you better have')),
p(h5('e. What are the key words in the job description?')),
br(),
h4('Choose two of your background'),
selectInput('bg1','Background 1',choices = bg,selectize = TRUE),
selectInput('bg2','Background 2',choices = bg,selected = bg[7],selectize = TRUE)
),
conditionalPanel('input.tab=="Position Recommendation"',
h4('Description'),
p(h5('This part can be used as a simple search engine based on the content in job description')),
br(),
p('However, it is also a prototype of personalized recommender. If one gets the full data set of the browsing history of all users, he or she can use the similar model to make a recommendation to a specific user based on his or her background, education, language, experience and browsing history.'),
br(),
p('Type in the words and click "Find the Position"'),
br(),
h4('Key words of your dreamed job'),
textInput('text',label = 'Use space to seperate different words. More words you provide, more accurate the algorithm will be. Default keywords are provided below already.',value ='Data Python Machine Learning',placeholder = 'Please type in the key words of your dream job, and we will match the most suitable job for you.',width = '600px'),
actionButton("go", "Find the Position")
),
conditionalPanel('input.tab=="About"',
h4('Contact Author'),
p(h5('Jiashen Liu')),
p(h5('Graduate of Rotterdam School of Management, Erasmus Univerisity')),
p(
a(h5("LinkedIn"),href="https://nl.linkedin.com/in/jiashen-liu-4658aa112",target="_blank"),
a(h5("Github"),href="https://github.com/liujiashen9307/",target="_blank"),
a(h5("Kaggle"),href="https://www.kaggle.com/jiashenliu",target="_blank")
)
)
),
mainPanel(
tabsetPanel(
id='tab',
tabPanel('Dashboard',
h4('A: Explore the extra background and required experience'),
splitLayout(cellwidths=c("50%","50%"),plotlyOutput("plot1"),plotlyOutput("plot2")),
h4('B: Explore the potential employer and location of company'),
splitLayout(cellwidths=c("50%","50%"),plotlyOutput("plot3"),plotlyOutput("plot4")),
h4('C: Explore required languages and degree requirement'),
splitLayout(cellwidths=c("50%","50%"),plotlyOutput("plot5"),plotlyOutput("plot6")),
h4("D: Briefly explore what terms are in Job Description"),
plotOutput('plot7')
),
tabPanel('Position Recommendation',
h4('TOP 5 Positions picked by the words you typed in'),
dataTableOutput('dt')
),
tabPanel('About',
h4("Overall"),
p('Two parts of contents are included in this small project. The first one serves as the visualization of data set while the second part, which implements a KNN model, builds a small recommendation system based on the key words you type in',align='Justify'),
h4('Data Source'),
p("All data are from Hoitalent.com , the largest job portal for English Jobs in the Netherlands",align='Justify'),
h4('Packages Used'),
p("Python Packages: bs4, requests, pandas."),
p("R Packages: Shiny, Shinythemes, plotly, wordcloud, FNN,tm, snowballC,sqldf,ggplot2, and RColorBrewer",align='Justify'),
h4("Link of Code"),
p('If you are interested, you can find both the web scraping script and Shiny Scripts in the below github link.',align='Justify'),
a(h5("Click me for the Code"),href="https://github.com/liujiashen9307/Hoitalent-small-project",target="_blank")
)
)
)
))
|
93bfd9fbd7cf27fcab23076400815ff0c1a08242
|
117936196834fbda370de297d6f5a77846bf45e9
|
/manuscript_functions/metacom_sim_fx.R
|
5939fcb6ed63b33c5aebef29a08c201e580ed61d
|
[] |
no_license
|
javirudolph/testingHMSC
|
a79dc2ffcdec967ed45d23e46151044d1365ab51
|
61c3e1b035b8095c45755833d2ab0ebc1179a6fb
|
refs/heads/master
| 2021-06-16T04:27:22.878177
| 2021-03-11T18:46:51
| 2021-03-11T18:46:51
| 170,368,566
| 4
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,355
|
r
|
metacom_sim_fx.R
|
# These are the functions needed for the processes regarding the metacommunity simulations:
# Each individual function is explained in the Supplementary information
#
# Each of these functions describes the processes going on for the metacommunity. We use these functions later on in the main_sim_fx.R script to generate an occupancy matrix for species in patches
###################################################################################################
# Colonization
###################################################################################################
# Immigration -------------------------------------------------------------
#' @title Metacommunity process: immigration
#' @description This function will calculate the propagule pressure. The effects of immigration are given as a weighted average of the occurrence probability of species i in neighborhood of z.
#' @param Y matrix of species occurrence
#' @param K connectivity matrix
#' @param m independent constant to account for immigration from outside the simulated metacommunity
#' @keywords immigration metacommunity
#'
I_f <- function(Y = "Species occurrence matrix",
K = "Patch connectivity matrix",
m = "outside immigration"){
N <- nrow(Y)
R <- ncol(Y)
I <- (1-m) * (K %*% Y) / (K %*% matrix(1, nrow = N, ncol = R)) + m
return(I)
}
# Patch Connectivity ------------------------------------------------------
#' @title Metacommunity process: patch connectivity
#' @description This function will calculate the connectivity matrix for a given set of patches and a dispersal parameter
#' @param XY corresponds to the patch coordinates
#' @param alpha dispersal parameter associated to an exponential distribution used for dispersal
#' @keywords patch
get_K <- function(XY, alpha){
N <- nrow(XY)
distMat <- as.matrix(dist(XY, method = "euclidean", upper = T, diag = T))
ConMat <- exp((-1/alpha)*distMat)
diag(ConMat) <- 0
return(ConMat)
}
# Environment -------------------------------------------------------------
# The effect of the environment on the local performance and colonization
#' @title Metacommunity process: Environmental filtering with gaussian response
#' @description The effect of the environment on the local performance and colonization. Probability of establishing a viable local population given environmental conditions in the patch.
#' @param E matrix of environmental covariates for each patch
#' @param u_c considered as the niche optima for each species, for each environmental variable.
#' @param s_c understood as niche breadth for each species for each environmental variable.
#' @keywords environmental filtering gaussian response
S_f_gaussian <- function(E, u_c, s_c) {
R <- ncol(u_c)
N <- nrow(E)
D <- ncol(E)
S <- matrix(1, nr = N, nc = R)
for(i in 1:D){
optima <- matrix(u_c[i,],nrow = N,ncol = R,byrow = TRUE)
breadth <- matrix(s_c[i,],nrow = N,ncol = R,byrow = TRUE)^2
S <- S*exp(-(E[,i]-optima)^2 / breadth)
}
return(S)
}
# This function is used for a quadratic response to the environment
#' @title Metacommunity process: Environmental filtering with quadratic response
#' @description The effect of the environment on the local performance and colonization. Probability of establishing a viable local population given environmental conditions in the patch.
#' @param E matrix of environmental covariates for each patch
#' @param u_c considered as the niche optima for each species, for each environmental variable.
#' @param s_c understood as niche breadth for each species for each environmental variable.
#' @keywords environmental filtering quadratic response
#'
S_f_quadratic <- function(E, u_c, s_c) {
R <- ncol(u_c)
N <- nrow(E)
D <- ncol(E)
S <- matrix(1, nr = N, nc = R)
for(i in 1:D){
optima <- matrix(u_c[i,],nrow = N,ncol = R,byrow = TRUE)
breadth <- matrix(s_c[i,],nrow = N,ncol = R,byrow = TRUE)
S <- S * ((-1 / (breadth/2)^2) * (E[,i] - optima)^2 + 1)
S <- ifelse(S < 0, 0 , S)
}
return(S)
}
# Interactions ------------------------------------------------------------
#' @title Metacommunity process: ecological interactions
#' @description Sum of ecological interactions. Computes the sum of ecological interactions for every location and every species
#' @param A species interaction matrix
#' @param Y species occurrence matrix
#' @keywords ecological interactions
#'
sum_interactions <- function(A = "Species interactions matrix",
Y = "Species occurrence matrix"){
t(A %*% t(Y))
}
# Then the effect of interactions on colonization
#' @title Metacommunity process: colonization
#' @description The total effect of ecological interactions on the colonization probabilty
#' @param v sum of interactions
#' @param d_c sensitivity to interactions
#' @param c_0 colonization at zero interactions
#' @param c_max colonization at max interactions
#'
C_f <- function(v = "Sum interactions",
d_c = "Sensitivity to interactions",
c_0 = "Colonization at zero interactions",
c_max = "Colonization at max interactions"){
c_max * (1 + ((1 / c_0) - 1) * exp(-v * d_c))^-1
# Is this the same as:
# c_max / (1 + ((1 / c_0) - 1) * exp(-v * d_c))
}
###################################################################################################
# Extinction
###################################################################################################
# Environment Extinction --------------------------------------------------
#' @title Metacomminbut process: extinction
#' @description Responses of the extinction probability to the local environment. Extinction rate should be minimal at environmental optimum
#' @param E Matrix of environmental covariates at each patch
#' @param u_e extinction rate at environmental optimum
#' @param s_e extintion rate at minimum
#'
M_f <- function(E = "Environmental variables",
u_e = "extinction rate at optimum?",
s_e = "extinction rate at optimum?") {
R <- ncol(u_e)
N <- nrow(E)
D <- ncol(E)
M <- matrix(1, nrow = N, ncol = R)
for(i in 1:D){
optima <- matrix(u_e[i,],nrow = N,ncol = R,byrow = TRUE)
minima <- matrix(s_e[i,],nrow = N,ncol = R,byrow = TRUE)^2
M <- M*(1 - exp(-(E[,i] - optima)^2 / minima))
}
return(M)
}
# Interaction Extinction --------------------------------------------------
#' @title Metacommunity process: interactions and extinction
#' @description Effect of ecological interactions on species extinction at the patch level. In this case, extinction probability must be larger when interactions are negative and smaller when the interactions are positive.
#' @param v sum of ecological interactions
#' @param d_e sensitivity to interactions
#' @param e_0 extinction at zero interactions
#' @param e_min extinction at max interactions
#'
E_f <- function(v = "Sum of interactions",
d_e = "Sensitivity to interactions",
e_0 = "Extinction at zero interactions",
e_min = "Extinction at max intreractions?") {
N <- nrow(v)
R <- ncol(v)
e_min_mat <- matrix(e_min, nrow = N, ncol = R, byrow=TRUE)
e_min_mat + (1 / (1-e_min_mat) + (1/(e_0-e_min_mat) - 1 / (1 - e_min_mat)) * exp(d_e * v))^-1
}
|
663346b03d4a4a618faeadbb82fda2d9e30844f5
|
4b4294254cab67348af2f98f73cc6f5341a2b3bb
|
/Milestone_Report/milestone_report.R
|
ca6f4931c9a905ae0419b5323fb9213af22a2390
|
[] |
no_license
|
jtzingsheim1/Text-Prediction-Model-and-App
|
f9bddd406353379be7dc12ae4dbe0a55cf8a67ef
|
b70a14758c6777471acd68c12fd01b5a349c1770
|
refs/heads/master
| 2020-06-14T07:19:08.277047
| 2019-08-01T22:50:14
| 2019-08-01T22:50:14
| 194,945,608
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,075
|
r
|
milestone_report.R
|
# Coursera Data Science Specialization Capstone Project 1 Script----------------
# Milestone report for predictive text project
# The purpose of this script is to complete the basic requirements behind the
# first capstone project from Johns Hopkins University within the Data Science
# Specialization on Coursera.
#
# The review criteria for this part of the project are:
# - Does the link lead to an HTML page describing the exploratory analysis of
# the training data set?
# - Has the data scientist done basic summaries of the three files? Word counts,
# line counts and basic data tables?
# - Has the data scientist made basic plots, such as histograms to illustrate
# features of the data?
# - Was the report written in a brief, concise style, in a way that a non-data
# scientist manager could appreciate?
#
# This project will begin to meet the objectives by building a script to work
# with the data. This script will serve as the basis for an R Markdown file
# which will create the actual deliverable of the html report.
library(tidyverse)
library(quanteda)
# Part 0) Function definitions--------------------------------------------------
DLAndUnzipData <- function(data.filename = "Coursera-SwiftKey.zip") {
# Downloads and unzips the capstone dataset if needed, returns folder name
#
# Args:
# data.filename: An optional name for the zip file, to replace the default
#
# Returns:
# A chacacter value of the name of the folder containing the data
# Check if the file already exists, download if it does not
if (!file.exists(data.filename)) {
message("Downloading Data File")
url <- paste0("https://d396qusza40orc.cloudfront.net/dsscapstone/dataset/",
"Coursera-SwiftKey.zip")
download.file(url, data.filename)
}
# Check if the file is already unzipped, unzip if needed
data.folder <- "final"
if (!file.exists(data.folder)) {
message("Unzipping Data File")
unzip(data.filename)
}
return(data.folder) # Return directory of unzipped file contents as character
}
AssembleCorpus <- function(n.lines,
file.selection = c("blogs", "news", "twitter"),
sub.dir = c("en_US", "de_DE", "fi_FI", "ru_RU")) {
# Reads in specified number of lines from the specified file, assembles corpus
#
# Args:
# n.lines: The number of lines to read in from the text with readLines()
# file: Select which file to read from, one of: blogs, news, or twitter
# sub.dir: The subdirectory to read in files from, "en_US" by default
#
# Returns:
# A corpus of the text from the selected file, one "text" per line
# Check and set arguments
file.selection <- match.arg(file.selection)
sub.dir <- match.arg(sub.dir)
# Download and unzip the data, store folder name and file path
filename <- paste(sub.dir, file.selection, "txt", sep = ".") # Build file name
filepath <- file.path(DLAndUnzipData(), sub.dir, filename) # Build file path
file.corpus <- filepath %>%
readLines(n = n.lines) %>% # Read in text
corpus() # Convert to corpus
# Set metadata for the corpus
docnames(file.corpus) <- paste0(file.selection, 1:ndoc(file.corpus))
file.corpus$metadata$source <- filename
file.corpus$metadata$file.size <- file.info(filepath)$size
file.corpus$metadata$rows.read <- ndoc(file.corpus)
# Return the corpus
return(file.corpus)
}
AssembleSummary <- function(corpus.object) {
# Assembles a data frame from the metadata of a corpus
#
# Args:
# corpus.object: The corpus from which to extract the metadata
#
# Returns:
# A data frame of the metadata
# Extract metadata from corpus and convert to data frame
corpus.metadata <- corpus.object %>%
metacorpus() %>%
as.data.frame(stringsAsFactors = FALSE)
# Return the metadata as a data frame
return(corpus.metadata)
}
# Part 1) Load the data, create tokens, and build summary table-----------------
# Set how many lines to read in from the text files
chunk.size <- 1000
# Read in text data and assemble into corpora
blogs.corp <- AssembleCorpus(n.lines = chunk.size, file.selection = "blogs")
news.corp <- AssembleCorpus(n.lines = chunk.size, file.selection = "news")
twitter.corp <- AssembleCorpus(n.lines = chunk.size, file.selection = "twitter")
# Tokenize and clean text
# The predictive model will not attempt to predict: numbers, punctuation,
# symbols, twitter handles, hyphens, or urls, so these are all removed
blogs.tkn <- tokens(blogs.corp, remove_numbers = T, remove_punct = T,
remove_symbols = T, remove_twitter = T, remove_hyphens = T,
remove_url = T)
news.tkn <- tokens(news.corp, remove_numbers = T, remove_punct = T,
remove_symbols = T, remove_twitter = T, remove_hyphens = T,
remove_url = T)
twitter.tkn <- tokens(twitter.corp, remove_numbers = T, remove_punct = T,
remove_symbols = T, remove_twitter = T, remove_hyphens = T,
remove_url = T)
# Count the number of words in each token object and add to the corpus metadata
blogs.corp$metadata$word.count <- sum(ntoken(blogs.tkn))
news.corp$metadata$word.count <- sum(ntoken(news.tkn))
twitter.corp$metadata$word.count <- sum(ntoken(twitter.tkn))
# Construct a table that summarizes the corpora
summary.table <- bind_rows(AssembleSummary(blogs.corp),
AssembleSummary(news.corp),
AssembleSummary(twitter.corp))
summary.table <- summary.table %>%
select(-created) %>%
mutate(file.size = round(file.size / (1024 ^ 2), 1))
# Part 2) Combine texts, explore unigram----------------------------------------
# Remove unneeded objects to free up memory
rm(DLAndUnzipData, AssembleCorpus, AssembleSummary, chunk.size, blogs.corp,
news.corp, twitter.corp, summary.table)
# Combine tokens into a single object
all.tkn <- blogs.tkn + news.tkn + twitter.tkn
rm(blogs.tkn, news.tkn, twitter.tkn)
# Build dfm of unigrams and convert to dataframe
unigram <- all.tkn %>%
dfm() %>%
textstat_frequency()
# Plot unigram frequency by index
plot(x = 1:nrow(unigram), y = unigram$frequency,
xlab = "Word Index (sorted)", ylab = "Word Frequency [count]")
# Display the top ten unigrams and frequencies
unigram %>%
select(feature:docfreq) %>%
slice(1:10) %>%
print()
# Check how many words are needed to cover 50 and 90 percent of occurances
occurances <- sum(unigram$frequency) # Total count of word occurances
# Create a table that includes the cumulative frequencies and fraction
frequencies <- unigram %>%
mutate(cum.freq = cumsum(frequency), cum.frac = cum.freq / occurances) %>%
select(cum.frac)
# Find index of 50th percentile
frequencies %>%
filter(cum.frac <= 0.5) %>%
nrow() %>%
print()
# Find index of 90th percentile
frequencies %>%
filter(cum.frac <= 0.9) %>%
nrow() %>%
print()
# Plot cumulative occurance fraction by word index
plot(x = 1:nrow(frequencies), y = frequencies$cum.frac,
xlab = "Word Index (sorted)", ylab = "Cumulative Occurance Fraction")
rm(occurances, frequencies)
rm(unigram)
# Part 3) Explore bigram and trigram--------------------------------------------
# Convert the 1-gram tokens to 2-gram tokens
all.tkn.bi <- tokens_ngrams(all.tkn, n = 2)
# Build dfm of bigrams and convert to dataframe
bigram <- all.tkn.bi %>%
dfm() %>%
textstat_frequency()
rm(all.tkn.bi)
# Check total number of bigrams
bigram %>%
nrow() %>%
print()
# Display the top ten bigrams and frequencies
bigram %>%
select(feature:docfreq) %>%
slice(1:10) %>%
print()
rm(bigram)
# Convert the 1-gram tokens to 3-gram tokens
all.tkn.tri <- tokens_ngrams(all.tkn, n = 3)
rm(all.tkn)
# Build dfm of trigrams and convert to dataframe
trigram <- all.tkn.tri %>%
dfm() %>%
textstat_frequency()
rm(all.tkn.tri)
# Check total number of trigrams
trigram %>%
nrow() %>%
print()
# Display the top ten trigrams and frequencies
trigram %>%
select(feature:docfreq) %>%
slice(1:10) %>%
print()
rm(trigram)
|
b62edbe782fd3147cd7124749f960f02ffbe802a
|
bd1609c4c212dfe7169ed071dab297d6342e1dcf
|
/examples/createAndUpdateCoordinates.R
|
d4e9ab366ff22cb988ff25f7d78fd537c852f9ec
|
[] |
no_license
|
restevesd/RtweetsDb
|
86a5871c13683abf7ec5a4bc0dc3053f7c94b658
|
efe25474a4cdb0c020fa03ae5c956a576ec74498
|
refs/heads/master
| 2021-01-26T07:20:38.778298
| 2015-07-06T12:09:54
| 2015-07-06T12:09:54
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 451
|
r
|
createAndUpdateCoordinates.R
|
## Working directory for this script should be the root of the project
## If this file is in working directory, you can run command
## setwd('..')
source('RtweetsDb.R')
createCoordinatesModel()
lookupAndAddCoordinates(c("Barcelona", "Zaragoza"))
lookupAndAddCoordinates(c("Barcelona", "Antofagasta"))
lookupAndAddCoordinates(c("Barcelona", "Antofagasta", "Warszawa", "Denton", "Coventry"))
lookupAndAddCoordinates(c("Barcelona", "Bialystok"))
|
eb6b08d6e947e347c0c09252724fd0e31820a660
|
0f13169c31b56ae9769850f9d909eed4909b6874
|
/tests/testthat/test-components.R
|
4c452865e7bb93ad8f28d441f7c0f3d4ec1ae5c3
|
[] |
no_license
|
hathawayj/osfr
|
d2a5f962839954ca86618f9ae5ee040f74ae3492
|
e01f61c90ecc3a44cfb2f3feb2e47a07f87fd082
|
refs/heads/master
| 2020-03-06T19:23:14.243628
| 2018-05-03T16:04:05
| 2018-05-03T16:04:05
| 127,026,594
| 0
| 1
| null | 2018-03-27T18:03:41
| 2018-03-27T18:03:34
|
R
|
UTF-8
|
R
| false
| false
| 671
|
r
|
test-components.R
|
context("component operations")
test_that("create, update, delete", {
x <- create_project('tmp')
expect_error(create_component())
expect_error(create_component(x))
expect_is(create_component(x, title = 'Test'), 'character')
expect_is(create_component(x, title = 'Test', description = 'Text'), 'character')
expect_is(create_component(x, title = 'Test', description = 'Text', private = FALSE), 'character')
y <- create_component(x, title = 'Test')
expect_error(update_component())
expect_true(update_component(y, private = TRUE))
expect_true(update_component(y, private = FALSE))
expect_error(delete_component())
expect_true(delete_component(y))
})
|
cc9f6fb9f10604e919f01aaf0f6f24f7f7dc4c44
|
1f0fac7707f85b34bea60416d27d84cfbfad9169
|
/man/ruch_gracz_anty_yolo_zakup_malego_psa.Rd
|
9924d5213df3164a9ae91c11424f73e0fc1cec40
|
[] |
no_license
|
nkneblewska/SuperFarmer
|
65dd1e44e15d6e38c2407c790dc62fdf2b3c7619
|
3b65fa7a6fa457c6cd4f288b53ea4fe5f5d2e959
|
refs/heads/master
| 2020-06-19T15:50:57.439890
| 2016-12-07T07:49:34
| 2016-12-07T07:49:34
| 74,902,062
| 0
| 0
| null | null | null | null |
WINDOWS-1250
|
R
| false
| true
| 473
|
rd
|
ruch_gracz_anty_yolo_zakup_malego_psa.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ruch_gracz_anty_yolo_zakup_malego_psa.R
\name{ruch_gracz_anty_yolo_zakup_malego_psa}
\alias{ruch_gracz_anty_yolo_zakup_malego_psa}
\title{Zakup małego psa}
\usage{
ruch_gracz_anty_yolo_zakup_malego_psa(stan_gracza)
}
\arguments{
\item{stan_gracza}{Wektor będący stanem stada gracza}
}
\description{
Funkcja pomocnicza strategii ANTY-YOLO, kupująca małego psa obronnego, jeśli nas stać
}
|
7532a656e60292fdb89dbeea519188d6a48d9f39
|
20ba172691d30cb84d60c374053d5d4a546d48d5
|
/man/emp_vario.Rd
|
3d5a97d4b7720b63a0cff3e4fb01a9d0a07237f8
|
[] |
no_license
|
sebastian-engelke/graphicalExtremes
|
b597b20c8a72a09e8cccd9876eb1e2b0cba3d851
|
4ad26c9f64e0f8ff8e4a96830c36be41c0330574
|
refs/heads/master
| 2023-09-01T15:52:11.310214
| 2023-04-03T10:05:06
| 2023-04-03T10:05:06
| 219,372,705
| 15
| 6
| null | 2022-09-16T14:03:03
| 2019-11-03T22:13:08
|
R
|
UTF-8
|
R
| false
| true
| 1,392
|
rd
|
emp_vario.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estimation_param.R
\name{emp_vario}
\alias{emp_vario}
\alias{emp_vario_pairwise}
\title{Estimation of the variogram matrix \eGamma of a Huesler--Reiss distribution}
\usage{
emp_vario(data, k = NULL, p = NULL)
emp_vario_pairwise(data, k = NULL, p = NULL, verbose = FALSE)
}
\arguments{
\item{data}{Numeric \nxd matrix, where \code{n} is the
number of observations and \code{d} is the dimension.}
\item{k}{Integer between 1 and \code{d}. Component of the multivariate
observations that is conditioned to be larger than the threshold \code{p}.
If \code{NULL} (default), then an average over all \code{k} is returned.}
\item{p}{Numeric between 0 and 1 or \code{NULL}. If \code{NULL} (default),
it is assumed that the \code{data} are already on multivariate Pareto scale. Else,
\code{p} is used as the probability in the function \code{\link[=data2mpareto]{data2mpareto()}}
to standardize the \code{data}.}
\item{verbose}{Print verbose progress information}
}
\value{
Numeric \dxd matrix. The estimated variogram of the Huesler--Reiss distribution.
}
\description{
Estimates the variogram of the Huesler--Reiss distribution empirically.
}
\details{
\code{emp_vario_pairwise} calls \code{emp_vario} for each pair of observations.
This is more robust if the data contains many \code{NA}s, but can take rather long.
}
|
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