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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c704f96cbd2c4958c8e230be66448832cc0dcdf7
|
f4ea90de095579e845aa7cd8e631583c77755beb
|
/R/Class-hier-fn.R
|
1c1746cf1ab4adbb81d7cabea022a2b6af656327
|
[] |
no_license
|
cran/classGraph
|
c35e320f389f372eba1f93fe2c6ed677934323a5
|
1666954776a5334a49c621fd733e1aadebd953ec
|
refs/heads/master
| 2023-08-31T07:13:05.501094
| 2023-08-21T19:50:02
| 2023-08-21T20:30:50
| 17,695,085
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,202
|
r
|
Class-hier-fn.R
|
if((Rv <- getRversion()) < "3.2.1") {
lengths <- function(x, use.names = TRUE)
vapply(x, length, 1L, USE.NAMES = use.names)
}## R < 3.2.1
subClasses <- function(Cl, directOnly = TRUE, complete = TRUE, ...)
{
## utility for classTree():
if (isClassDef(Cl)) {
cDef <- Cl
Cl <- cDef@className
} else { ## need getClass() can give error because sub classes can
## be "not defined" (?!) -- e.g. "iMatrix"
cDef <- if (complete) getClass(Cl) else getClassDef(Cl)
}
subs <- showExtends(cDef@subclasses, printTo = FALSE)
if(directOnly) subs$what[subs$how == "directly"] else subs$what
}
numOutEdges <- function(g)
{
## Purpose: returns a named integer vector giving for each node in g,
## the number of edges *from* the node
## ----------------------------------------------------------------------
## Arguments: g: graph
## ----------------------------------------------------------------------
## Author: Martin Maechler, Date: 8 Feb 2007, 22:59
lengths( sapply(edgeL(g), `[[`, "edges") )
}
is.leaf <- function(g) numOutEdges(g) == 0
## The graph package now defines a leaves() generic {w/ degree.dir}
## leaves <- function(g) nodes(g)[is.leaf(g)]
bGraph <- function(n, root = "Mom",
leaves = paste(l.prefix, seq(length=n), sep=""),
l.prefix = "D", # for 'D'aughter
weights = NULL,
mode = c("undirected", "directed"))
{
## Purpose: Create a "branch graph", a simple tree with root and
## n branches / leaves
## ----------------------------------------------------------------------
## Author: Martin Maechler, Date: Aug 2005
if(!missing(leaves)) {
stopifnot(is.character(leaves))
n <- length(leaves)
} else stopifnot(is.numeric(n), length(n) == 1, n >= 0)
mode <- match.arg(mode)
ftM2graphNEL(cbind(root, leaves), W = weights, edgemode = mode)
}
## agopen() has
## layoutType = c("dot","neato","twopi", "circo", "fdp")
abbrMatrixcl <- function(clnames, level = 1) {
### Do "Matrixclass" name abbrevation
doSub <- clnames != "Matrix"
clnames[doSub] <- sub("Matrix$", "*", clnames[doSub])
## sparse
iSp <- grep("sparse", clnames)
if(level >= 2)
clnames[iSp] <- sub("sparse\\*", ".sp.", clnames[iSp])
## dense
iD <- grep("dense", clnames)
if(level >= 2)
clnames[iD] <- sub("dense\\*", ".D.", clnames[iD])
list(clnames = clnames, iSparse = iSp, iDense = iD)
}
mRagraph <- function(gr, lType,
fixedsize = FALSE, ## <---- this is it !
fill = c("lightblue", "gray90"),
color =c("blue3", "gray60"),
labcol = c("blue3","green4","purple"))
{
## Produce a layed out graph, an "Ragraph" -- to be plotted
if (!validGraph(gr))
stop("The graph to be plotted is not a valid graph structure")
if (missing(lType))
lType <- "dot"
ng <- nodes(gr)
## leaves <- function(g) nodes(g)[is.leaf(g)]
## nonVirtual <- leaves(gr) ## the leaves are *non*-virtual classes
nonVirtual <- ng[is.leaf(gr)] ## the leaves are *non*-virtual classes
r <- abbrMatrixcl(ng)
nAtt <- makeNodeAttrs(gr, label = r$clnames, shape = "ellipse",
fixedsize = fixedsize,
fillcolor = fill[1], color = color[1],
fontcolor = labcol[1])
nAtt$fontcolor[r$iSparse] <- labcol[2]
nAtt$fontcolor[r$iDense] <- labcol[3]
nAtt$fillcolor[nonVirtual] <- fill[2]
nAtt $ color[nonVirtual] <- color[2]
## but make one exception (for visualization):
nAtt$fillcolor["pMatrix"] <- "thistle"
nAtt $ color["pMatrix"] <- "turquoise"
if(getOption("verbose")) { cat("mRagraph(): nodeAttrs: "); str(nAtt) }
### Returns the "layouted graph"; is +- == method("plot", "graph"):
agopen(gr, name = "", layout = TRUE, layoutType = lType,
attrs = list(), nodeAttrs = nAtt, edgeAttrs = list(),
subGList = list(), recipEdges = "combined")
}
## plotRag() : a bit more than selectMethod("plot", "Ragraph")
## -- but building on that
.optRagargs <- function(side = 1, adj = 0.05, cex = 0.75, line = 3)
list(side = side, adj = adj, cex = cex, line = line)
plotRag <- function(ragr, sub, subArgs = .optRagargs(), ...)
{
stopifnot(is(ragr, "Ragraph"))
if(missing(sub)) {
## nEdges <- length(unlist(edgeL(gr), use.names=FALSE))
sub <- paste(length(ragr@AgNode), "nodes with",
length(ragr@AgEdge), "edges")
}
### BUG in Rgraphviz ----> FIXME: bug report, ...
### plot(ragr, sub = sub, ...)
### workaround {{but more flexible anyway:
plot(ragr, ...)
op <- par(xpd = NA) ; on.exit(par(op))
do.call(mtext, c(list(text = sub), subArgs))
}
## Now do this recusively
classTree <- function(Cl, all = FALSE, ...)
{
## First a check
if (isClassDef(Cl)) {
cDef <- Cl
Cl <- cDef@className
} else cDef <- getClass(Cl)
pkg <- cDef@package
where <- if(pkg == ".GlobalEnv") .GlobalEnv else asNamespace(pkg)
## Now define a recursive function that computes the extended subtree
## for one class, and uses this for all sub-classes of Cl
subtree <- function(cl, all) {
stopifnot(isClassDef(cl))
clN <- cl@className
if(getOption('verbose')) cat(" ST",clN,":")
sc <- subClasses(cl, directOnly = !all)
if(length(sc) == 0) {
if(getOption('verbose')) cat(" is leaf\n")
## one node named 'cl':
g <- new("graphNEL", nodes = clN, edgemode = "dir")
}
else {
if(getOption('verbose')) cat(" has leaves:\n\t")
g <- bGraph(root = clN, leaves = sc, mode = "dir")
for(cc in sc) {
if(getOption('verbose')) cat(":: ",clN,"-",cc,sep="")
st <- subtree(getClass(cc, where = where), all = all)
## -------## recursive
if(numNodes(st) > 1)
g <- join(g, st)
}
}
g
}
subtree(cDef, all = all)
}
|
d0d7b210a89bff73d1a8b9b23796ae874dc8b0ec
|
c20629bc224ad88e47849943e99fe8bc6ccb1f17
|
/Data processing functions/lost.hrs_function.R
|
f09fec2bc29b5a05bf73b429ea5754bd7053dfab
|
[] |
no_license
|
nayefahmad/R-vocab-and-experiments
|
71a99e4d3ff0414d1306a5c7cabfd79b49df17f9
|
4384d3d473b0a9d28d86c3d36b0b06a1f91b862e
|
refs/heads/master
| 2022-12-13T01:12:11.134952
| 2020-08-24T04:59:52
| 2020-08-24T04:59:52
| 103,333,690
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,458
|
r
|
lost.hrs_function.R
|
#*******************************************
# FUNCTION TO FIND LOST HOURS IN BOOKING DATA:
#*******************************************
# rm(list = ls())
# source("2017-12-13_vgh_mdc-explore-booking-data.R")
# TODO: ---------------
# fn defn:
lost.hrs <- function(df) {
# dataframe is booking data split on date and bay
# output: dataframe with added col showing lost hours
# note: this doesn't count gaps where two appointments are not
# scheduled back-to-bacck. Only looks at gaps caused by no-shows,
# cancelled appointments, etc.
nrow = nrow(df) # %>% print
if (nrow > 1) {
lag.end <- df$endtime[1:(nrow-1)] # %>% print
lag.start <- df$starttime[1:(nrow-1)] # %>% print
} else {
if (df$status[1] == "IN") {
result.df <- data.frame(date = df$date,
bay = df$bay,
losses = 0,
losses.made.up = 0)
return(result.df)
} else {
result.df <- data.frame(date = df$date,
bay = df$bay,
losses = df$endtime[1] - df$starttime[1],
losses.made.up = 0)
return(result.df)
}
}
# first we ensure that df is arranged right:
df %<>% arrange(starttime, status) # %>% print
# check first row for losses:
first.row.loss <- ifelse(!(df$status[1] %in% "IN"),
df$endtime[1] - df$starttime[1],
0) # %>% print
# now we have to remove first row to add lagged vars:
df <- df[2:nrow, ]
# df$starttime %>% print
# print(df$starttime - lag.end)
df %<>%
select(id, date, bay, status, starttime, endtime) %>%
mutate(# add lag vars:
lag.starttime = lag.start,
lag.endtime = lag.end,
# durations:
dur = endtime - starttime,
lag.dur = lag.endtime - lag.starttime,
# anything other than "IN" is "lost" time:
loss.hrs = ifelse(!(status %in% "IN"),
endtime - starttime,
NA),
is.overlap = ifelse(status %in% "IN",
ifelse(starttime < lag.endtime,
1,
0),
NA),
loss.makeup = ifelse(is.overlap == 1,
ifelse(dur >= lag.dur,
# branch 1:
ifelse(starttime == lag.starttime,
lag.dur,
(lag.dur - (starttime - lag.starttime))),
# branch 2:
ifelse(starttime == lag.starttime,
lag.dur,
(lag.dur - (starttime - lag.starttime)))),
NA))
# print(df)
# aggregate all losses:
losses <- c(first.row.loss, df$loss.hrs)
losses[is.na(losses)] <- 0
losses.made.up <- c(df$loss.makeup, NA)
losses.made.up[is.na(losses.made.up)] <- 0
result.df = data.frame(date = rep(df$date[1], length(losses)),
bay = rep(df$bay[1], length(losses)),
losses = losses,
losses.made.up = losses.made.up)
return(result.df)
}
# test the fn:
lost.hrs(list1.split[[2]])
|
339dc835e6ccac3b9f14b29ddc0ac1d79fe31e35
|
a1e2e1e9c4b5eb2b2eb56f64ec6df2f83d65a2ce
|
/common/general_dataset_stats.R
|
97c8c44a9a29a0f18a2c3abf65551d1de3bfad6e
|
[] |
no_license
|
posfaig/wow
|
e6b2747d17e7eda6869a01a1ced50b6d0a1cad67
|
1d8b7694886706d6152d058a9465c1c34acbb8ee
|
refs/heads/master
| 2021-01-11T19:43:07.244518
| 2017-01-13T01:27:40
| 2017-01-13T01:27:40
| 69,188,269
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,885
|
r
|
general_dataset_stats.R
|
######################################################################
###
### Get the general statistics of the datasets
###
######################################################################
library(dplyr)
compute_dataset <- function(data, pred_date, testset_end_date, ...){
print("compute_dataset")
pred_date <- as.Date(pred_date)
testset_end_date <- as.Date(testset_end_date)
##### Compute labels for avatars
#
# true: left the guild in the given period
# false: not left the guild in the given period
#
test_data <- data %>% filter(current_date >= pred_date & current_date < testset_end_date)
labels <- test_data %>%
group_by(avatar) %>%
summarise(label = (sum(event == "Guild Left" | event == "Guild Changed") > 0))
#### Records
train_data <- data %>% filter(current_date < pred_date)
# get the avatars that are member of a guild at prediction date
features <- train_data %>%
group_by(avatar) %>%
dplyr::slice(n()) %>%
group_by() %>%
filter(guild != -1) %>%
select(avatar, guild)
##### Joining labels
print("Joining features and labels")
features_and_labels <- left_join(features, labels, by = "avatar")
features_and_labels <- features_and_labels %>% mutate(label = ifelse(is.na(label), FALSE, label)) # label is FALSE for avatars that did not appear in the test period
features_and_labels$pred_date <- pred_date
features_and_labels$testset_end_date <- testset_end_date
features_and_labels
}
get_dataset_stats <- function(dataset){
result <- list()
result[["records"]] <- nrow(dataset)
result[["pred_dates"]] <- length(unique(dataset[["pred_date"]]))
result[["guilds"]] <- length(unique(dataset[["guild"]]))
result[["avatars"]] <- length(unique(dataset[["avatar"]]))
result[["labels_true"]] <- sum(as.numeric(dataset[["label"]]) == 1)
result[["labels_false"]] <- sum(as.numeric(dataset[["label"]]) == 0)
result
}
source("common/init.R")
source("common/streamline_functions_for_modeling.R")
training_data <- get_features_for_pred_dates(
prediction_dates_train,
function(...){NULL},
compute_dataset)
test_data <- get_features_for_pred_dates(
prediction_dates_test,
function(...){NULL},
compute_dataset)
stats_train <- get_dataset_stats(training_data)
stats_test <- get_dataset_stats(test_data)
# Write stats to file
dir.create(file.path("generated/dataset_stats/"), showWarnings = FALSE, recursive = TRUE)
lines_train <- sapply(names(stats_train), function(x) {
paste(x, ",", stats_train[[x]], sep = "")
})
lines_test <- sapply(names(stats_test), function(x) {
paste(x, ",", stats_test[[x]], sep = "")
})
fileConn <- file("generated/dataset_stats/stats.txt")
writeLines(c("TRAINING DATASET", lines_train, "TEST DATASET", lines_test), fileConn)
close(fileConn)
|
014c9ef0102674ce88f2c8a62a5b9a8ca25ecf3a
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/lmreg/examples/stars2.Rd.R
|
69096a8b13316dde0da7b314ecca7d69a2350b96
|
[] |
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
| 148
|
r
|
stars2.Rd.R
|
library(lmreg)
### Name: stars2
### Title: Stars data 2
### Aliases: stars2
### Keywords: datasets
### ** Examples
data(stars2)
head(stars2)
|
c0a6cc76bc0a6c4e87be57d17a53ba66b97fe78b
|
561beed458dfcf06de55c8b9145613adf3e3dad6
|
/old_bic.r
|
b28969541f1ef0fefa89eb54035edadbb60807d3
|
[] |
no_license
|
deniseduma/respls
|
e75dafd68ecd0fa5905667b78f2a36f44d8947da
|
f326cf1a84ab4734a67a156917f8a2752597be68
|
refs/heads/master
| 2016-09-14T05:36:51.454059
| 2016-05-23T22:30:59
| 2016-05-23T22:30:59
| 59,517,165
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,495
|
r
|
old_bic.r
|
## Select best w using BIC
cat("\n"); print("BIC")
betas = list(); prs = list();
idx_bic = 1; min_bic = 1e6; all_zero = 1
for (j in 1:nlam) {
w = ws[ ,j]; idx_w = which(w!=0); nnz_w = length(idx_w)
if (nnz_w==0)
next
all_zero = 0
## OLS re-estimation of w
#y = ys[ ,j]; sX = X[ ,idx_w,drop=FALSE]
#sw = solve ( crossprod(sX) + 0.001*diag(nnz_w), t(sX)%*%y); w2[idx_w] = sw
#err = (norm(y - X%*%w2)^2) / (q)
## PLS re-estimation of w
idx = union( idx_W, idx_w )
sX0 = X0[,idx,drop=FALSE]
plsfit = pls::plsr( Y0~sX0, ncomp=min(k,length(idx)), method="simpls", scale=F)
beta = matrix( 0, p, q ); beta[idx,] = matrix( coef(plsfit), length(idx), q )
betas[[j]] = beta; prs[[j]] = plsfit$projection
err = (norm(Y0-X0%*%beta)^2) / (n*q)
## df estimation
## Method 1
dfterm = (log(n*q) / (n*q)) * nnz_w
## Method 2
#sX = X[,idx_w,drop=FALSE]
#lam = lambdas[j]; lam2 = alpha2*lam
#DF = sX %*% solve( t(sX)%*%sX + lam2*L[idx_w, idx_w] + lam2*diag(nnz_w), t(sX))
#df = sum(diag(DF)); dfterm = (log(n*q) / (n*q))*df
#plty = 2*0.5*log(choose(p, nnz_w))
bic = log(err) + dfterm # + plty
print(paste("j=",j,", nnz=",nnz_w,", err=",err,", log(err)=",log(err),", dfterm=",dfterm, ", bic=",bic,sep=""))
if (bic<min_bic) {
idx_bic = j; min_bic = bic
}
}
if (all_zero) {
print(paste("[opt]No non-zero found at PC ",k,sep=""))
break
}
w = ws[ ,idx_bic]
v = vs[ ,idx_bic]
|
e22aa30cf69877285ef788722f94005563d17afa
|
39b1d1b3f6e933d7fc7c30579f76d5027620b3b0
|
/R/parse_crossovers.R
|
54ad6cc9aec9ee95478e6b8c7324a19fd014e285
|
[
"LicenseRef-scancode-warranty-disclaimer"
] |
no_license
|
susjoh/crimaptools
|
bb46fdf82a889fd1b7e203fe2872c348f71686f0
|
70a6ec563a8544fb533a0b9a653189501bedd5e2
|
refs/heads/master
| 2021-01-09T20:53:58.204711
| 2020-10-12T22:23:26
| 2020-10-12T22:23:26
| 58,579,445
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,829
|
r
|
parse_crossovers.R
|
#' parse_crossovers: Parse crossover information from CriMAP chrompic output
#' @param chrompicfile File with output from chrompic
#' @param familyPedigree data.frame containing columns ANIMAL, FATHER, MOTHER
#' and FAMILY. FAMILY defines family groups for crimap. IDs in ANIMAL can be
#' repeated within and between families if necessary. Should be that used in
#' create_crimap_input.
#' @param remove.zero.inf.loci Logical, default = TRUE. Remove IDs with no
#' informative loci.
#' @import plyr
#' @export
parse_crossovers <- function(chrompicfile, familyPedigree, remove.zero.inf.loci = TRUE){
#~~ read lines from the chrompic file
x <- readLines(chrompicfile)
nmarkers <- nrow(parse_map_chrompic(chrompicfile))
x <- x[grep("Family", x)[1]:(grep("Overall recombinations", x)-1)]
x <- x[-grep("^Recombinations ", x)]
x <- x[-grep("^Non-recombinations ", x)]
x <- x[-grep("^Chromosomes ", x)]
x <- x[-which(x == "")]
if (length(grep(" ", x) > 0)) x <- x[-grep(" ", x)]
#~~ create a table with family information to add to the full table later
famtab <- data.frame(FamilyOrder = 1:length(grep("Family", x)),
FamilyID = grep("Family", x, value = T),
LineNo = grep("Family", x),
stringsAsFactors = F)
famtab$startline <- famtab$LineNo - (famtab$FamilyOrder - 1)
famtab$FamilyShortID <- NA
famtab$FamilyShortID <- unlist(lapply(famtab$FamilyID, function (x) strsplit(x, split = " ")[[1]][2]))
x <- x[-grep("Family", x)]
famtab$Count <- diff(c(famtab$startline, (length(x) + 1)))
table(famtab$Count)
famvec <- rep(famtab$FamilyShortID, famtab$Count)
#~~ create a data frame to put all information in
recombframe <- data.frame(data = x,
ANIMAL = NA,
RecombCount = NA,
parent = NA,
Family = famvec,
stringsAsFactors=F)
#~~ Get the IDs
recombframe$ANIMAL <- unlist(lapply(recombframe$data, function(foo){
z <- strsplit(foo, split = "\\s+")[[1]]
if(length(z) == ceiling((nmarkers/10)+2)){
return(z[1])
} else {
return(z[2])
}
}))
recombframe$parent[which(recombframe$ANIMAL != "")] <- "MOTHER"
recombframe$ANIMAL[which(recombframe$ANIMAL == "")] <- recombframe$ANIMAL[which(recombframe$ANIMAL != "")]
recombframe$parent[which(is.na(recombframe$parent))] <- "FATHER"
#~~ get the number of recombinations for each individual
recExtract <- function(CMPstring){
x <- strsplit(CMPstring, split = " ")[[1]][length(strsplit(CMPstring, split = " ")[[1]])]
}
recombframe$RecombCount <- sapply(recombframe$data, recExtract, simplify = TRUE)
#~~ format the data column so that we can get out the number of informative loci
removeID <- function(CMPstring){
z <- strsplit(CMPstring, split = "\\s+")[[1]]
if(length(z) == ceiling((nmarkers/10)+2)){
return(paste0(z[2:(length(z)-1)], collapse = ""))
} else {
return(paste0(z[3:(length(z)-1)], collapse = ""))
}
}
system.time(recombframe$data <- unlist(lapply(recombframe$data, removeID)))
recombframe$data2 <- gsub("-" , "", recombframe$data)
recombframe$data2 <- gsub("c" , "", recombframe$data2)
recombframe$data2 <- gsub(":" , "", recombframe$data2)
recombframe$No.Inf.Loci <- nchar(recombframe$data2)
recombframe <- subset(recombframe, select = -data2)
recombframe$RecombCount <- as.numeric(recombframe$RecombCount)
if(remove.zero.inf.loci == TRUE) recombframe <- subset(recombframe, No.Inf.Loci != 0)
#~~ Get first and list informative positions
recombframe$First.Inf.Order <- unlist(lapply(recombframe$data, InfLengthFunc))
recombframe$Last.Inf.Order <- unlist(lapply(recombframe$data, function(x) InfLengthFunc(x, position = "Last")))
#~~ add the RRID information - the individual in which the recombination took place
suppressMessages(recombframe <- join(recombframe, familyPedigree))
recombframe$RRID <- NA
recombframe$RRID[which(recombframe$parent == "MOTHER")] <- recombframe$MOTHER[which(recombframe$parent == "MOTHER")]
recombframe$RRID[which(recombframe$parent == "FATHER")] <- recombframe$FATHER[which(recombframe$parent == "FATHER")]
#~~ add analysisID
analysisID.val <- gsub("\\\\", "/", chrompicfile)
analysisID.val <- strsplit(analysisID.val, split = "/")[[1]]
analysisID.val <- analysisID.val[length(analysisID.val)]
analysisID.val <- gsub("chr", "", analysisID.val)
analysisID.val <- gsub(".cmp", "", analysisID.val, fixed = T)
recombframe$analysisID <- analysisID.val
recombframe$UniqueID <- paste(recombframe$analysisID, recombframe$Family, recombframe$RRID, recombframe$ANIMAL, recombframe$parent, sep = "_")
recombframe
}
|
4c27a21156f045fc1e332f953dfb5aff838aab91
|
0ff7d2d3b7a141ca374a1d73a47289871d5d2fda
|
/R/get_alphabet_freq.R
|
4095237cf3c35dfebfa7bde2e07dd08a12b4d1a3
|
[] |
no_license
|
tinyheero/shmutils
|
a13f44111d8323fef68810f7a0f88aad7c09a2ec
|
a26d06eeb21dca16f70ddb422604ae60d5ec25f6
|
refs/heads/master
| 2020-12-25T18:23:16.564227
| 2015-07-16T21:55:32
| 2015-07-16T21:55:32
| 39,107,770
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,286
|
r
|
get_alphabet_freq.R
|
#' Return the Alphabet Distribution Over a GRanges Object
#'
#' Calculates the number of different bases there are in a particlar GRanges
#' object.
#'
#' @param gr GRanges object. There needs to be a id column in the metadata.
#' So that the number of motifs can be associated with the original
#' GenomicRange.
#' @param BS.genome This a Biostring-based genome object. (BSgenome from
#' Bioconductor). For instance, library("BSgenome.Hsapiens.UCSC.hg19")
#' can be used.
#' @return A matrix containing with letters as columns and each GRange as
#' a row. Each value indicates the prevalence of that letter in the
#' GRange
#' @export
get_alphabet_freq <- function(gr, bs.genome) {
if (!"id" %in% colnames(S4Vectors::mcols(gr))) {
stop("No id column in the metadata columns of gr. This column is need to
map the original GenomicRanges to the motifs")
}
message("Retrieving Sequences of the GRanges Object")
gr.window.seq <- Biostrings::getSeq(bs.genome, gr)
# assign names so that we can map the matches back to GRanges object
names(gr.window.seq) <- S4Vectors::mcols(gr)[, "id"]
message("Calculating the alphabet frequency")
alpha.freq <- Biostrings::alphabetFrequency(gr.window.seq)
alpha.freq
}
|
73d8bb5931616fee34f7632ccf277db2a833edfd
|
fdae59c4711c9710bbbb78e98a8dd1a6e7026eeb
|
/rserv/suncorp_rserve/qa/duplicates_Scenario.R
|
61ad63a58b5e72f8b925c14f6709b316d6ff2e17
|
[] |
no_license
|
saurabh-singh-17/secgov
|
ac61459751b0e8cfa6f5ec828a87054819329101
|
b19b9a838cd0c43562dc8fa771075ca3f2635905
|
refs/heads/master
| 2020-12-14T01:01:49.172334
| 2017-01-15T07:58:03
| 2017-01-15T07:58:03
| 55,128,035
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,922
|
r
|
duplicates_Scenario.R
|
# Written by: Tushar Gupta
# Time : Sep 2014
#=================================================================================
# inputPath<-
# outputPath<-
# customkEY<-
# var_list <-c("Store_Format")
# scenarioName <- 'abcd'
# keepOption <-
#===============================for verify and save button========================
options(digits=10)
load(paste(inputPath,"/dataworking.RData",sep=""))
var_name<- paste("murx_",scenarioName,sep="")
if (length(var_list) != 1){
dataworking[,"var"] <-apply(dataworking[,var_list],1,function(x){paste(x,collapse="_")})
}else{
dataworking[,"var"] <- dataworking[,var_list]
}
number_records <- nrow(dataworking)
duplicates<- duplicated(dataworking[,"var"])
duplicates_detected <- length(which(duplicates=="TRUE"))
Statistics <- c("Number of Observations",
"Number of Duplicates detected","Number of Duplicates Removed","Keep Option")
#=================================================================================
#for keeping first occurence (both as option and for auto ticking in view of first
if (keepOption == "FIRST"){
logical_vector_F <- duplicated(dataworking[,"var"])
dataworking[!logical_vector_F,var_name] <- 1
# data_final_first <- data[!logical_vector_F,]
number_deleted <- duplicates_detected
}
#=================================================================================
#=================================================================================
# Keep the last occurence
#=================================================================================
if (keepOption == "LAST"){
logical_vector_L <- duplicated(dataworking[,"var"],fromLast=T)
dataworking[!logical_vector_L,var_name] <- 1
# data_final_last <-data[!logical_vector_L,]
number_deleted <-duplicates_detected
}
#Customized selection
if (keepOption =="CUSTOM"){
allDups <- !duplicated(dataworking[,"var"],fromLast=TRUE) & !duplicated(dataworking[,"var"])
uniquekey<-which(allDups=="TRUE")
if (length(uniquekey) != 0){
customKey<- c(customKey,uniquekey)
}
dataworking[customKey,var_name] <-1
number_deleted<-nrow(dataworking)-length(customKey)
}
#===============================================================================
# Number of Duplicate
#===============================================================================
# number of duplicates detected
dataworking$var <- NULL
duplicates_deleted <- number_deleted
records <- cbind(duplicates_detected,duplicates_deleted)
Value <- c(number_records,duplicates_detected,number_deleted,keepOption)
Observations <- cbind(Statistics,Value)
write.csv(Observations,paste(outputPath,"/","OBSERVATIONS.csv",sep=""),row.names=F,quote=F)
save(dataworking,file=paste(paste(inputPath, "/", "dataworking.RData", sep="")))
write("COMPLETED",file=paste(outputPath,"/","SCENARIO_COMPLETED.TXT",sep=""))
|
3c6258110fa5980decd7860f2dc1960b22d0d220
|
0c1885fa442778f9e027975ef82267c5b6d69075
|
/learn_hmmr.R
|
202eccfbe1d7c39233b5263664c945a63f579af1
|
[] |
no_license
|
blinsimon/HMMR
|
489a63344350f8a5d31b796ff796f6b90373f4e9
|
45dc511dc94c0a39cfc86441a02a2d5644eff5cd
|
refs/heads/master
| 2020-04-27T00:52:57.589728
| 2019-03-05T13:07:32
| 2019-03-05T13:07:32
| 173,947,996
| 0
| 0
| null | null | null | null |
IBM852
|
R
| false
| false
| 14,221
|
r
|
learn_hmmr.R
|
learn_hmmr<- function(x, y, K, p,type_variance, total_EM_tries, max_iter_EM, threshold, verbose){
# learn_hmmr learn a Regression model with a Hidden Markov Process (HMMR)
# for modeling and segmentation of a time series with regime changes.
# The learning is performed by the EM (Baum-Welch) algorithm.
#
#
# Inputs :
#
# (x,y) : a time series composed of m points : dim(y)=[m 1]
# * Each curve is observed during the interval [0,T], i.e x =[t_1,...,t_m]
#
# K : Number of polynomial regression components (regimes)
# p : degree of the polynomials
#
# Outputs :
#
# hmmr: the estimated HMMR model. a structure composed of:
#
# prior: [Kx1]: prior(k) = Pr(z_1=k), k=1...K
# trans_mat: [KxK], trans_mat(\ell,k) = Pr(z_t = k|z_{t-1}=\ell)
# reg_param: the paramters of the regressors:
# betak: regression coefficients
# sigma2k (or sigma2) : the variance(s)
# Stats:
# tau_tk: smoothing probs: [nxK], tau_tk(t,k) = Pr(z_i=k | y1...yn)
# alpha_tk: [nxK], forwards probs: Pr(y1...yt,zt=k)
# beta_tk: [nxK], backwards probs: Pr(yt+1...yn|zt=k)
# xi_tkl: [(n-1)xKxK], joint post probs : xi_tk\elll(t,k,\ell) = Pr(z_t=k, z_{t-1}=\ell | Y) t =2,..,n
# X: [nx(p+1)] regression design matrix
# nu: model complexity
# parameter_vector
# f_tk: [nxK] f(yt|zt=k)
# log_f_tk: [nxK] log(f(yt|zt=k))
# loglik: log-likelihood at convergence
# stored_loglik: stored log-likelihood values during EM
# cputime: for the best run
# cputime_total: for all the EM runs
# klas: [nx1 double]
# Zik: [nxK]
# state_probs: [nxK]
# BIC: -2.1416e+03
# AIC: -2.0355e+03
# regressors: [nxK]
# predict_prob: [nxK]: Pr(zt=k|y1...y_{t-1})
# predicted: [nx1]
# filter_prob: [nxK]: Pr(zt=k|y1...y_t)
# filtered: [nx1]
# smoothed_regressors: [nxK]
# smoothed: [nx1]
#
#
#Faicel Chamroukhi, sept 2008
#
## Please cite the following papers for this code:
#
#
# @article{Chamroukhi-FDA-2018,
# Journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
# Author = {Faicel Chamroukhi and Hien D. Nguyen},
# Note = {DOI: 10.1002/widm.1298.},
# Volume = {},
# Title = {Model-Based Clustering and Classification of Functional Data},
# Year = {2019},
# Month = {to appear},
# url = {https://chamroukhi.com/papers/MBCC-FDA.pdf}
# }
#
# @InProceedings{Chamroukhi-IJCNN-2011,
# author = {F. Chamroukhi and A. Sam\'e and P. Aknin and G. Govaert},
# title = {Model-based clustering with Hidden Markov Model regression for time series with regime changes},
# Booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
# Pages = {2814--2821},
# Adress = {San Jose, California, USA},
# year = {2011},
# month = {Jul-Aug},
# url = {https://chamroukhi.com/papers/Chamroukhi-ijcnn-2011.pdf}
# }
#
# @INPROCEEDINGS{Chamroukhi-IJCNN-2009,
# AUTHOR = {Chamroukhi, F. and Sam\'e, A. and Govaert, G. and Aknin, P.},
# TITLE = {A regression model with a hidden logistic process for feature extraction from time series},
# BOOKTITLE = {International Joint Conference on Neural Networks (IJCNN)},
# YEAR = {2009},
# month = {June},
# pages = {489--496},
# Address = {Atlanta, GA},
# url = {https://chamroukhi.com/papers/chamroukhi_ijcnn2009.pdf},
# slides = {./conf-presentations/presentation_IJCNN2009}
# }
#
# @article{chamroukhi_et_al_NN2009,
# Address = {Oxford, UK, UK},
# Author = {Chamroukhi, F. and Sam\'{e}, A. and Govaert, G. and Aknin, P.},
# Date-Added = {2014-10-22 20:08:41 +0000},
# Date-Modified = {2014-10-22 20:08:41 +0000},
# Journal = {Neural Networks},
# Number = {5-6},
# Pages = {593--602},
# Publisher = {Elsevier Science Ltd.},
# Title = {Time series modeling by a regression approach based on a latent process},
# Volume = {22},
# Year = {2009},
# url = {https://chamroukhi.users.lmno.cnrs.fr/papers/Chamroukhi_Neural_Networks_2009.pdf}
# }
#
##########################################################################################
options(warn=-1)
if (nargs()<9){verbose =0}
if (nargs()<8){threshold = 1e-6}
if (nargs()<7){max_iter_EM = 1500}
if (nargs()<6){total_EM_tries = 1}
if (nargs()<5){total_EM_tries = 1
type_variance='hetereskedastic'}
if (type_variance == 'homoskedastic'){homoskedastic =1}
else if (type_variance == 'hetereskedastic'){homoskedastic=0}
else{stop('The type of the model variance should be : "homoskedastic" or "hetereskedastic"')}
#Chargement de toutes les fonctions Ó utiliser
source("designmatrix.R")
source("init_hmmr.R")
source("forwards_backwards.R")
source("normalise.R")
source("mk_stochastic.R")
source("MAP.R")
source("hmm_process.R")
if (ncol(y)!=1){
y=t(y)
}
m = length(y)#length(y)
X = designmatrix(x,p)#design matrix
P = ncol(X)# here P is p+1
I = diag(P)# define an identity matrix, in case of a Bayesian regularization for regression
#
best_loglik = -10000000
nb_good_try=0
total_nb_try=0
cputime_total=c()
while (nb_good_try < total_EM_tries){
start_time = Sys.time()
if (total_EM_tries>1){
paste('EM try n░',(nb_good_try+1))
}
total_nb_try=total_nb_try+1
## EM Initializaiton step
## Initialization of the Markov chain params, the regression coeffs, and the variance(s)
HMMR = init_hmmr(X, y, K, type_variance, nb_good_try+1)
# calculare the initial post probs (tau_tk) and joint post probs (xi_ikl)
#f_tk = hmmr.stats.f_tk; # observation component densities: f(yt|zt=k)
prior = HMMR[[1]]
trans_mat = HMMR[[3]]
Mask = HMMR[[2]]
betak = HMMR[[4]][[1]]
if (homoskedastic==1){
sigma2 = HMMR[[4]][[2]]
}
else {
sigma2k = HMMR[[4]][[3]]
}
#
iter = 0
prev_loglik = -100000
converged = 0
top = 0
#
log_f_tk = matrix(c(0),m,K)
muk = matrix(c(0),m,K)
#
## EM
stored_loglik=c()
while ((iter <= max_iter_EM) & (converged!=1)){
## E step : calculate tge tau_tk (p(Zt=k|y1...ym;theta)) and xi t_kl (and the log-likelihood) by
# forwards backwards (computes the alpha_tk et beta_tk)
# observation likelihoods
for (k in 1:K){
mk = X%*%betak[,k]
muk[,k] = mk
# the regressors means
if (homoskedastic==1){
sk = sigma2
}
else{
sk = sigma2k[k]
}
z=((y - mk)^2)/sk
log_f_tk[,k] = -0.5*matrix(c(1),m,1)%*%(log(2*pi)+log(sk)) - 0.5*z#log(gaussienne)
}
for (k in 1:K){
for (i in 1:nrow(log_f_tk)){
log_f_tk[i,k] = min(log_f_tk[i,k],log(.Machine$double.xmax))
log_f_tk[i,k] = max(log_f_tk[i,k] ,log(.Machine$double.xmin))
}
}
f_tk = exp(log_f_tk)
#fprintf(1, 'forwards-backwards ');
#[tau_tk, xi_tkl, alpha_tk, beta_tk, loglik] =
fb=forwards_backwards(prior, trans_mat , f_tk )
tau_tk=fb[[1]]
xi_tkl=fb$xi_tkl
alpha_tk=fb[[3]]
beta_tk=fb[[4]]
loglik=fb[[5]]
## M step
# updates of the Markov chain parameters
# initial states prob: P(Z_1 = k)
prior = normalise(tau_tk[1,])[[1]]
# transition matrix: P(Zt=i|Zt-1=j) (A_{k\ell})
#print(cbind(apply(xi_tkl[,,1],2,sum),apply(xi_tkl[,,2],2,sum),apply(xi_tkl[,,3],2,sum)))
# print(xi_tkl[,,1])
trans_mat = round(mk_stochastic(cbind(apply(xi_tkl[,,1],2,sum),
apply(xi_tkl[,,2],2,sum),
apply(xi_tkl[,,3],2,sum),
apply(xi_tkl[,,4],2,sum),
apply(xi_tkl[,,5],2,sum))),4)
# for segmental HMMR: p(z_t = k| z_{t-1} = \ell) = zero if k<\ell (no back) of if k >= \ell+2 (no jumps)
trans_mat = mk_stochastic(Mask*trans_mat)
## update of the regressors (reg coefficients betak and the variance(s) sigma2k)
s = 0 # if homoskedastic
for (k in 1:K){
wieghts = tau_tk[,k]
nk = sum(wieghts)# expected cardinal nbr of state k
Xk = X*(sqrt(wieghts)%*%matrix(c(1),1,P))#[n*(p+1)]
yk=y*(sqrt(wieghts))# dimension :[(nx1).*(nx1)] = [nx1]
# reg coefficients
lambda = 1e-9 # if a bayesian prior on the beta's
bk = (solve(t(Xk)%*%Xk + lambda*diag(P))%*%t(Xk))%*%yk
betak[,k] = bk
# variance(s)
z = sqrt(wieghts)*(y-X%*%bk)
sk = t(z)%*%z
if (homoskedastic==1){
s = (s+sk)
sigma2 = s/m
}
else{
sigma2k[k] = sk/nk
}
}
## En of an EM iteration
iter = iter + 1
# test of convergence
loglik = loglik + log(lambda)
if (verbose==1){
paste('HMM_regression | EM : Iteration :', iter,' Log-likelihood : ', loglik)
}
if (prev_loglik-loglik > 1e-4){
top = top+1;
if (top==10){
stop(paste('!!!!! The loglikelihood is decreasing from',prev_loglik,' to ',loglik))
}
}
converged = (abs(loglik - prev_loglik)/abs(prev_loglik) < threshold)
stored_loglik[iter] <-loglik
print(stored_loglik)
prev_loglik = loglik
end_time=Sys.time()
cputime_total[nb_good_try+1]=c(end_time-start_time)
}
reg_param=list(betak=betak)
if (homoskedastic==1){
reg_param = append(reg_param,list(sigma2=sigma2))
}
else{
reg_param = append(reg_param,list(sigma2k=sigma2k))
}
hmmr=list(prior=prior,trans_mat=trans_mat,reg_param=reg_param)
# Estimated parameter vector (Pi,A,\theta)
if (homoskedastic==1){
parameter_vector=c(prior, trans_mat[Mask!=0],c(betak[1:P,]), sigma2)
nu = K-1 + K*(K-1) + K*(p+1) + 1#length(parameter_vector);#
}
else{
parameter_vector=c(prior, trans_mat[Mask!=0],c(betak[1:P,]), sigma2k)
nu = K-1 + K*(K-1) + K*(p+1) + K #length(parameter_vector);#
}
stats=list(nu=nu,parameter_vector=parameter_vector,tau_tk=tau_tk,alpha_tk=alpha_tk,beta_tk=beta_tk,
xi_tkl=xi_tkl,f_tk=f_tk,log_f_tk=log_f_tk,loglik=loglik,stored_loglik=stored_loglik,X=X)
if (total_EM_tries>1){
paste('loglik_max = ',loglik)
}
#
#
if (length(hmmr$reg_param$betak)!=0){
nb_good_try=nb_good_try+1
total_nb_try=0
if (loglik > best_loglik){
best_hmmr = hmmr
best_loglik = loglik
}
}
if (total_nb_try > 500){
paste('can',"'",'t obtain the requested number of classes')
hmmr=NULL
return(hmmr)
}
}#End of the EM runs
hmmr = best_hmmr
#
if (total_EM_tries>1){
paste('best_loglik: ',stats$loglik)
}
#
#
stats=append(stats,list(cputime=mean(cputime_total),cputime_total=cputime_total))
## Smoothing state sequences : argmax(smoothing probs), and corresponding binary allocations partition
stats=append(stats,MAP(stats$tau_tk)) #[hmmr.stats.klas, hmmr.stats.Zik ]
# # compute the sequence with viterbi
# #[path, ~] = viterbi_path(hmmr.prior, hmmr.trans_mat, hmmr.stats.fik');
# #hmmr.stats.viterbi_path = path;
# #hmmr.stats.klas = path;
# ###################
#
# # ## determination des temps de changements (les fonti├Ętres entres les
# # ## classes)
# # nk=sum(hmmr.stats.Zik,1);
# # for k = 1:K
# # tk(k) = sum(nk(1:k));
# # end
# # hmmr.stats.tk = [1 tk];
#
# ## sate sequence prob p(z_1,...,z_n;\pi,A)
state_probs = hmm_process(hmmr$prior, hmmr$trans_mat, m)
stats = append(stats,list(state_probs=state_probs))
### BIC, AIC, ICL
stats = append(stats,list(BIC=(stats$loglik - (stats$nu*log(m)/2))))
stats = append(stats,list(AIC=(stats$loglik - stats$nu)))
# # CL(theta) : Completed-data loglikelihood
# sum_t_log_Pz_ftk = sum(hmmr.stats.Zik.*log(state_probs.*hmmr.stats.f_tk), 2);
# comp_loglik = sum(sum_t_log_Pz_ftk(K:end));
# hmmr.stats.comp_loglik = comp_loglik;
# hmmr.stats.ICL = comp_loglik - (nu*log(m)/2);
## predicted, filtered, and smoothed time series
stats = append(stats,list(regressors = round(X%*%hmmr$reg_param$betak,4)))
# prediction probs = Pr(z_t|y_1,...,y_{t-1})
predict_prob = matrix(c(0),m,K)
predict_prob[1,] = hmmr$prior#t=1 p (z_1)
predict_prob[2:m,] = round((stats$alpha_tk[(1:(m-1)),]%*%hmmr$trans_mat)/(apply(stats$alpha_tk[(1:(m-1)),],1,sum)%*%matrix(c(1),1,K)),5)#t =2,...,n
stats = append(stats,list(predict_prob = predict_prob))
# predicted observations
stats = append(stats,list(predicted = apply(round(stats$predict_prob*stats$regressors,5),1,sum)))#pond par les probas de prediction
# filtering probs = Pr(z_t|y_1,...,y_t)
stats = append(stats,list(filter_prob = round(stats$alpha_tk/(apply(stats$alpha_tk,1,sum)%*%matrix(c(1),1,K)),5)))#normalize(alpha_tk,2);
# filetered observations
stats = append(stats,list(filtered = apply(round(stats$filter_prob*stats$regressors,5), 1,sum)))#pond par les probas de filtrage
### smoothed observations
stats = append(stats,list(smoothed_regressors = stats$tau_tk*stats$regressors))
stats =append(stats,list(smoothed = apply(stats$smoothed_regressors, 1,sum)))
hmmr=c(hmmr,stats=list(stats))
return(hmmr)
}
|
34454d1159ed4c253064e705fbafbaa46e7af6d3
|
4459eb5432916b4ad6c5c5d911b50c9d2fec1ad5
|
/man/table.Correlation.Rd
|
2d706b7bb49ee43ebf807ec5bd230c9552f088bb
|
[] |
no_license
|
braverock/PerformanceAnalytics
|
057af55b0a4ddeb4befcc02e36a85f582406b95c
|
49a93f1ed6e2e159b63bf346672575f3634ed370
|
refs/heads/master
| 2023-08-03T10:18:27.115592
| 2023-03-29T09:23:17
| 2023-03-29T09:23:17
| 58,736,268
| 209
| 113
| null | 2023-05-23T17:46:08
| 2016-05-13T12:02:42
|
R
|
UTF-8
|
R
| false
| true
| 1,341
|
rd
|
table.Correlation.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/table.Correlation.R
\name{table.Correlation}
\alias{table.Correlation}
\title{calculate correlalations of multicolumn data}
\usage{
table.Correlation(Ra, Rb, ...)
}
\arguments{
\item{Ra}{a vector of returns to test, e.g., the asset to be examined}
\item{Rb}{a matrix, data.frame, or timeSeries of benchmark(s) to test the
asset against.}
\item{\dots}{any other passthru parameters to \code{\link{cor.test}}}
}
\description{
This is a wrapper for calculating correlation and significance against each
column of the data provided.
}
\examples{
# First we load the data
data(managers)
table.Correlation(managers[,1:6],managers[,7:8])
result=table.Correlation(managers[,1:6],managers[,8])
rownames(result)=colnames(managers[,1:6])
require("Hmisc")
textplot(format.df(result, na.blank=TRUE, numeric.dollar=FALSE,
cdec=rep(3,dim(result)[2])), rmar = 0.8, cmar = 1.5,
max.cex=.9, halign = "center", valign = "top", row.valign="center"
, wrap.rownames=20, wrap.colnames=10, mar = c(0,0,3,0)+0.1)
title(main="Correlations to SP500 TR")
ctable = table.Correlation(managers[,1:6],managers[,8,drop=FALSE], conf.level=.99)
dotchart(ctable[,1],labels=rownames(ctable),xlim=c(-1,1))
}
\seealso{
\code{\link{cor.test}}
}
\author{
Peter Carl
}
|
45ecea4c881b9a7f66994ec6fee5f3dae1dfc5a4
|
fde0347e6cd6aadf0c5850de45f26fc771e67f0e
|
/Demos/Rbased/CrashCourse/03_ClimateProcessing.R
|
ccb56c068b864c8b0e5f34d11f8c00048bf14ef4
|
[] |
no_license
|
shichaochen/BotanyENMWorkshop2020
|
4b43292776c6948ae639a4d5f4d5e5d245ed9e16
|
9cb02a7a52bdf5d4a453d6b34c7334f7fd9e3d43
|
refs/heads/master
| 2022-11-24T11:34:20.051259
| 2020-08-01T17:42:12
| 2020-08-01T17:42:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,777
|
r
|
03_ClimateProcessing.R
|
# ClimateProcesssing.R
## Climate Layer Processing
## Original script by Charlotte Germain-Aubrey.
## Modified and created by ML Gaynor.
# Load Packages
library(maptools)
library(raster)
library(rgdal)
library(sp)
library(maps)
library(mapproj)
# Load bioclim layers
alt_l <- raster("data/climate_processing/Bioclim/alt.bil")
bio1_l <- raster("data/climate_processing/Bioclim/bio1.bil")
bio2_l <- raster("data/climate_processing/Bioclim/bio2.bil")
bio3_l <- raster("data/climate_processing/Bioclim/bio3.bil")
bio4_l <- raster("data/climate_processing/Bioclim/bio4.bil")
bio5_l <- raster("data/climate_processing/Bioclim/bio5.bil")
bio6_l <- raster("data/climate_processing/Bioclim/bio6.bil")
bio7_l <- raster("data/climate_processing/Bioclim/bio7.bil")
bio8_l <- raster("data/climate_processing/Bioclim/bio8.bil")
bio9_l <- raster("data/climate_processing/Bioclim/bio9.bil")
bio10_l <- raster("data/climate_processing/Bioclim/bio10.bil")
bio11_l <- raster("data/climate_processing/Bioclim/bio11.bil")
bio12_l <- raster("data/climate_processing/Bioclim/bio12.bil")
bio13_l <- raster("data/climate_processing/Bioclim/bio13.bil")
bio14_l <- raster("data/climate_processing/Bioclim/bio14.bil")
bio15_l <- raster("data/climate_processing/Bioclim/bio15.bil")
bio16_l <- raster("data/climate_processing/Bioclim/bio16.bil")
bio17_l <- raster("data/climate_processing/Bioclim/bio17.bil")
bio18_l <- raster("data/climate_processing/Bioclim/bio18.bil")
bio19_l <- raster("data/climate_processing/Bioclim/bio19.bil")
# Define desired extent
## We are going to use Florida as our desired extent
FL <- rgdal::readOGR("data/climate_processing/FL/FLstate2.shp")
# Crop bioclim layers to the desired extent
# Visualize the first layer
plot(alt_l)
## First, mask the bioclim layer with Florida.
alt <- mask(alt_l, FL)
## Visualize the masked layers
plot(alt)
## Next, crop the bioclim layers to Florida's extent.
alt <- crop(alt, extent(FL))
## Visualize the final layer
plot(alt)
## Now save the layer - note we already saved these layers for you, so you have to overwrite the file to save yours
writeRaster(alt, "data/climate_processing/PresentLayers/alt.asc", format="ascii", overwrite = TRUE)
# Repeat with all additional files
bio1 <- mask(bio1_l, FL)
bio1 <- crop(bio1, extent(FL))
#writeRaster(bio1, "data/climate_processing/PresentLayers/bio1.asc", format="ascii")
bio2 <- mask(bio2_l, FL)
bio2 <- crop(bio2, extent(FL))
#writeRaster(bio2, "data/climate_processing/PresentLayers/bio2.asc", format="ascii")
bio3 <- mask(bio3_l, FL)
bio3 <- crop(bio3, extent(FL))
#writeRaster(bio3, "data/climate_processing/PresentLayers/bio3.asc", format="ascii")
bio4 <- mask(bio4_l, FL)
bio4 <- crop(bio4, extent(FL))
#writeRaster(bio4, "data/climate_processing/PresentLayers/bio4.asc", format="ascii")
bio5 <- mask(bio5_l, FL)
bio5 <- crop(bio5, extent(FL))
#writeRaster(bio5, "data/climate_processing/PresentLayers/bio5.asc", format="ascii")
bio6 <- mask(bio6_l, FL)
bio6 <- crop(bio6, extent(FL))
#writeRaster(bio6, "data/climate_processing/PresentLayers/bio6.asc", format="ascii")
bio7 <- mask(bio7_l, FL)
bio7 <- crop(bio7, extent(FL))
#writeRaster(bio7, "data/climate_processing/PresentLayers/bio7.asc", format="ascii")
bio8 <- mask(bio8_l, FL)
bio8 <- crop(bio8, extent(FL))
#writeRaster(bio8, "data/climate_processing/PresentLayers/bio8.asc", format="ascii")
bio9 <- mask(bio9_l, FL)
bio9 <- crop(bio9, extent(FL))
#writeRaster(bio9, "data/climate_processing/PresentLayers/bio9.asc", format="ascii")
bio10 <- mask(bio10_l, FL)
bio10 <- crop(bio10, extent(FL))
#writeRaster(bio10, "data/climate_processing/PresentLayers/bio10.asc", format="ascii")
bio11 <- mask(bio11_l, FL)
bio11 <- crop(bio11, extent(FL))
#writeRaster(bio11, "data/climate_processing/PresentLayers/bio11.asc", format="ascii")
bio12 <- mask(bio12_l, FL)
bio12 <- crop(bio12, extent(FL))
#writeRaster(bio12, "data/climate_processing/PresentLayers/bio12.asc", format="ascii")
bio13 <- mask(bio13_l, FL)
bio13 <- crop(bio13, extent(FL))
#writeRaster(bio13, "data/climate_processing/PresentLayers/bio13.asc", format="ascii")
bio14 <- mask(bio14_l, FL)
bio14 <- crop(bio14, extent(FL))
#writeRaster(bio14, "data/climate_processing/PresentLayers/bio14.asc", format="ascii")
bio15 <- mask(bio15_l, FL)
bio15 <- crop(bio15, extent(FL))
#writeRaster(bio15, "data/climate_processing/PresentLayers/bio15.asc", format="ascii")
bio16 <- mask(bio16_l, FL)
bio16 <- crop(bio16, extent(FL))
#writeRaster(bio16, "data/climate_processing/PresentLayers/bio16.asc", format="ascii")
bio17 <- mask(bio17_l, FL)
bio17 <- crop(bio17, extent(FL))
#writeRaster(bio17, "data/climate_processing/PresentLayers/bio17.asc", format="ascii")
bio18 <- mask(bio18_l, FL)
bio18 <- crop(bio18, extent(FL))
#writeRaster(bio18, "data/climate_processing/PresentLayers/bio18.asc", format="ascii")
bio19 <- mask(bio19_l, FL)
bio19 <- crop(bio19, extent(FL))
#writeRaster(bio19, "data/climate_processing/PresentLayers/bio19.asc", format="ascii")
# Select layers for MaxEnt
## We only want to include layers that are not highly correlated.
## To assess which layers we will include, we want to look at the pearson correlation coefficient among layers.
### Stack all layers
stack <- stack(bio1, bio2, bio3, bio4, bio5, bio6, bio7, bio8, bio9, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17, bio18, bio19)
### Then calculate the correlation coefficient
corr <- layerStats(stack, 'pearson', na.rm=TRUE)
### Isolate only the pearson correlation coefficient
c <- corr$`pearson correlation coefficient`
### Write file and view in excel
#write.csv(c, "data/climate_processing/correlationBioclim.csv")
## Highly correlated layers (> |0.80|) can impact the statistical significance
## of the niche models and therefore must be removed.
|
ba27ed6ee0531c5ba65a216e8f08eb8363431df7
|
9f9cc8931df4007d7ae3d215b1f39b6658200e79
|
/BLP.R
|
07cf32bce36001da638d0c09b9869b28d68cb9b9
|
[] |
no_license
|
ygh929/GP_Gibbs
|
e45940deedb296efa5f21531054e04289c8ab464
|
5e7150951b9121e643eba64c9e2b799cb3ea2ee1
|
refs/heads/master
| 2021-01-20T02:15:34.887554
| 2015-08-29T04:51:42
| 2015-08-29T04:51:42
| 20,091,153
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 469
|
r
|
BLP.R
|
GetXZ<-function(cDat){
points=cDat$points
pairs=cDat$pairs
m=dim(pairs)[1]
n=dim(points)[1]
X=matrix(0,nrow=m,ncol=n)
Z=matrix(NA,nrow=m,ncol=1)
for (i in 1:m){
X[i,min(pairs[i])]=1
X[i,max(pairs[i])]=-1
Z[i,1]=2*I(pairs[i,1]>pairs[i,2])-1
}
list(X,Z)
}
#set the size of train and validate data set
n1=50
n2=50
#generate training data and calculate X,Z
Gen1=gendata(n1,p,type=1)
Dat=Gen1$Dat
cDat=convertDat(Dat)
M=getXZ(cDat)
X=M$X
Z=M$Z
Sig=getSigma()
|
b52f983bad7d19a57cfa1b85bebdc969950f4e8c
|
84e92a2e0e3f8fbdb724e7b3d70052a9059d5259
|
/test/rgtsvm-svmlight.R
|
cc05be6ed13620e3ed39e5dc4c48e6ea702335e5
|
[] |
no_license
|
minghao2016/Rgtsvm
|
bc9c3fdd25d5a89962e9c3608357041338d5e819
|
b7e7bf043b70c1d21a96406ef4e0847922495ff1
|
refs/heads/master
| 2021-01-21T11:36:24.676439
| 2016-12-12T20:40:56
| 2016-12-12T20:40:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 850
|
r
|
rgtsvm-svmlight.R
|
library(Rgtsvm);
#wget http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/glass.scale
# normal matrix
#mat <- as.matrix( load.svmlight("http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/glass.scale") );
# sparse matrix
#mat <- load.svmlight("http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/glass.scale") ;
mat <- load.svmlight("mnist")
gt.model <- svm( x=mat[,-1], y=mat[,1], gamma=0.05, tolerance=0.01, type="C-classification", scale = FALSE, fitted=TRUE);
save(gt.model, file="test2-gt.rdata");
cat("correction=", gt.model$correct, "\n");
gt.predit <- predict( gt.model, mat[,-1], score=FALSE );
save( gt.model, gt.predit, y=mat[,1], file="test2-gt.rdata");
correct = length( which(gt.predit == mat[,1]) )/nrow(mat)
cat("correct=", correct, "\n");
summary(gt.predit);
|
d7bd673b458c7887b4efc77208090cc5b7a73b18
|
6593fa55f4216c069a8aa53d2475838b9a3db1c7
|
/munge/descarga_mapa_2.R
|
0064cca0d4f72d55f074abb8957e621f982a740e
|
[] |
no_license
|
ecastillomon/ecoVIZ
|
d6eef354fd80ddcdf266c507e8a61872b9c2cce3
|
6d30494dd4f878b53bf046aff29ec67ba736451a
|
refs/heads/master
| 2020-08-21T22:00:42.174077
| 2020-07-17T00:42:46
| 2020-07-17T00:42:46
| 216,253,783
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,097
|
r
|
descarga_mapa_2.R
|
library(ggmap)
# ggmap::register_google(key = "AIzaSyB6BLEl5Bhygcrfy-Q4USZ6dMghajMNax0")
# p=ggmap(get_googlemap(center = c(long_centro,lat_centro),
# zoom = 11, scale = 2, source="stamen",
# maptype ='terrain',
# color = 'color'))
# [-99.13, -99.22, 19.35, 19.45]
# c(-99.1658746-.05 ,19.4055775-.05 ,-99.1658746+.05,19.4055775 +.05)
estaciones=read.csv("data/estaciones.csv") %>% mutate(long=location.lon,lat=location.lat) %>% filter(!is.na(long))
p = qmap(location = c(-99.22 ,19.35,-99.13,19.45 ), zoom = 15, source="osm",
extent="device", legend = "topleft",highres=TRUE,scale="auto")
x_distance=p$data$lon %>% unique()
x_distance=max(x_distance) - mean(x_distance)
y_distance=p$data$lat %>% unique()
y_distance=max(y_distance) - mean(y_distance)
prop=x_distance/y_distance
png(filename="plots/mapa_estaciones.png", width=(1280*prop) , height=1280)
# p %>%ggsave(file="plots/mapa_estaciones.png", limitsize = TRUE,width = 390,height =555,units = "cm" )
print(p+ggsn::scalebar(x.min = -99.21 ,x.max=-99.20,y.min =19.36,y.max =19.3625 , transform = TRUE,location = "bottomright",
dist = .5, dist_unit = "km",st.size=3.5,st.color = "black",st.dist = 1,
model = "WGS84", height = 0.5))
dev.off()
#
# sb = scalebar(19.13,-99.22, 0.15, 5, 10, "km" )
#
#
#
p +
scalebar(19.37,-99.20, dist_unit = "km", transform = FALSE, model = "WGS84")
# ggsn::scalebar( data=p$data ,location="topright",transform = FALSE ,dist_unit = "km",dist =4,st.size = 2, model = "WGS84")
#
#
# geom_rect(data=sb[[1]], aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, fill=z), inherit.aes=F,
# show.legend = F, color = "black", fill = sb[[1]]$fill.col) +
# geom_text(data=sb[[2]], aes(x=xlab, y=ylab, label=text), inherit.aes=F, show.legend = F)
# geom_point(data=estaciones,aes(x=long,y=lat))
#
#
#
# scalebar(p, dist=2, location="topright", st.size=2 ,transform = TRUE ,dist_unit = "km")
# scalebar(p)
# scalebar(x.min = -99.13 ,x.max=-99.11,y.min =19.25 ,y.max =19.45 , dist_unit = "km",
# + transform = TRUE, model = "WGS84")
# #
p2 = qmap(location = c(-99.289595,19.2393901,-99.015463,19.606023), zoom = 12, source="osm",
extent="device", legend = "topleft",highres=TRUE,scale="auto")
x_distance=p2$data$lon %>% unique()
x_distance=max(x_distance) - mean(x_distance)
y_distance=p2$data$lat %>% unique()
y_distance=max(y_distance) - mean(y_distance)
prop=x_distance/y_distance
# p2 %>%ggsave(file="plots/mapa_ciudad.png", limitsize = TRUE,width = 390,height =555,units = "cm" )
png(filename="plots/mapa_ciudad.png", width=(1280*prop) , height=1280)
print(p2+ggsn::scalebar(x.min = -99.2 ,x.max=-99.25,y.min =19.30,y.max =19.31 , transform = TRUE,location = "bottomright",
dist =2, dist_unit = "km",st.size=3.5,st.color = "black",st.dist = 1,
model = "WGS84", height = 0.5))
dev.off()
# p+ geom_point(data=estaciones,aes(x=location.lon,y=location.lat))
|
85a5dd00a91555b2917574bf389bb2b9838ccf62
|
29445f5eb91f1c9c083a387c140d302e5e3e5b52
|
/tests/testthat/test-stitch.R
|
eaece6c5a554332d0290f7fe5e06fd47f20f88f0
|
[
"MIT"
] |
permissive
|
adam-gruer/victor
|
d9a896c371e33f49d8bcc0e4c11f4d1fc8bb19d2
|
988ce71dbee593fa5aafbf67a86caf4f36daa0dc
|
refs/heads/master
| 2020-08-20T02:51:55.633455
| 2019-11-02T00:08:47
| 2019-11-02T00:08:47
| 215,977,085
| 29
| 2
|
MIT
| 2019-10-19T23:57:57
| 2019-10-18T08:26:50
|
R
|
UTF-8
|
R
| false
| false
| 371
|
r
|
test-stitch.R
|
test_that("binding two tile's layer with differing attributes", {
perth <- list(longitude = 115.906105,latitude = -31.842274)
au_west <- spoils(zoom = 4, longitude = perth$longitude, latitude = perth$latitude)
sydney <- list(longitude = 151.131706,latitude = -33.805967)
aus_se <- spoils(zoom = 4, longitude = sydney$longitude, latitude = sydney$latitude)
})
|
d3f59dae291a50f44bb38b988da1d2c230b73e71
|
dfea2a089f5373848d4bbfddb785f7956ba9d312
|
/man/ThreeComp_Volume_Exponent.Rd
|
4cbde6f8ae6f29c445a8af1766826beaaa830802
|
[] |
no_license
|
cran/PKconverter
|
c09d298d74948e54bbbd00a169fb4cce66b427d4
|
23d3cac38c79f9164b9710ebabf579b5d9825556
|
refs/heads/master
| 2020-04-09T07:46:01.820084
| 2020-02-06T08:00:02
| 2020-02-06T08:00:02
| 160,169,464
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,827
|
rd
|
ThreeComp_Volume_Exponent.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ThreeComp_Volume_Exponent.R
\name{ThreeComp_Volume_Exponent}
\alias{ThreeComp_Volume_Exponent}
\title{Convert pharmacokinetic parameters for three compartment model}
\usage{
ThreeComp_Volume_Exponent(V1,alpha,beta,gamma,k21,k31,
V1.sd=NA,alpha.sd=NA,beta.sd=NA,gamma.sd=NA,k21.sd=NA,k31.sd=NA,
covar=c(V1alpha=NA,V1beta=NA,V1gamma=NA,V1k21=NA,V1k31=NA,
alphabeta=NA,alphagamma=NA,alphak21=NA,alphak31=NA,
betagamma=NA,betak21=NA,betak31=NA,gammak21=NA,gammak31=NA,
k21k31=NA),...)
}
\arguments{
\item{V1}{The volume of distribution of compartment 1}
\item{alpha}{parameter in one compartment model "Aexp(-alpha)"}
\item{beta}{parameter in two compartment model "Aexp(-alpha)+Bexp(-beta)"}
\item{gamma}{parameter in three compartment model
"Aexp(-alpha)+Bexp(-beta)+Cexp(-gamma)"}
\item{k21}{transfer rate constants from compartment 2 to compartment 1}
\item{k31}{transfer rate constants from compartment 3 to compartment 1}
\item{V1.sd}{standard error of V1}
\item{alpha.sd}{standard error of alpha}
\item{beta.sd}{standard error of beta}
\item{gamma.sd}{standard error of gamma}
\item{k21.sd}{standard error of k21}
\item{k31.sd}{standard error of k31}
\item{covar}{covariances among parameters}
\item{...}{arguments to be passed to methods}
}
\description{
Calculate pharmacokinetic parameters with volume of distribution(V1),
transfer rate constant (k12 and k31), and parameters
(alpha, beta and gamma) in the model "Aexp(-alpha)+Bexp(-beta)+Cexp(-gamma)"
}
\examples{
ThreeComp_Volume_Exponent(V1=10,alpha=0.6, beta=0.013, gamma=0.00074,
k21=0.02, k31=0.001, V1.sd=0.01,alpha.sd=0.01,beta.sd=0.00005,
gamma.sd=0.000002, k21.sd=0.0006,k31.sd=0.0000005)
}
\references{
\url{http://www.nonmemcourse.com/convert.xls}
}
|
652274b79ef8cbcfc3351d42367ab07e14f45c1b
|
8e0989c127fa440b1356606c5b1616703d76c06d
|
/R/gif.R
|
032417a17600102d424fd27deb5fad5aef4a83fb
|
[] |
no_license
|
HenrikBengtsson/R.graphics
|
8da678165bd6cfad546faf71b78d768a44b3165e
|
c92a1761f82806ecf1b761d58a59ab55532aa518
|
refs/heads/master
| 2021-01-20T11:44:10.433776
| 2014-06-19T04:04:58
| 2014-06-19T04:04:58
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,853
|
r
|
gif.R
|
#########################################################################/**
# @RdocFunction gif
#
# @title "GIF graphics device"
#
# \description{
# Device driver for GIF images.
# Internally \code{png()} and ImageMagick's convert command is used.
# }
#
# @synopsis
#
# \arguments{
# \item{filename}{filename (@character string).}
# \item{width}{width (in pixels) of the saved image (@integer).}
# \item{height}{height (in pixels) of the saved image (@integer).}
# \item{pointsize}{font size (@integer).}
# }
#
# \value{A plot device is opened; nothing is returned to the \R interpreter.}
#
# \note{
# Requires: ImageMagick (\url{http://www.imagemagick.org}) has to be
# installed on the system. To test whether it is installed or not, try
# \code{system("convert")}.
# }
#
# \examples{\dontrun{
# options(imagemagick=c(convert="c:/Program Files/ImageMagick/convert"))
# logo <- BitmapImage$read("logosm.ppm", path = R.graphics$dataPath)
# gif(filename="logosm.gif")
# image(logo)
# dev.off()
# }}
#
# \keyword{device}
#
# @author
#*/#########################################################################
gif <- function(filename="Rplot.gif", width=480, height=480, pointsize=12) {
base.pos <- which(search() == "package:base");
pos <- which(search() == "package:R.graphics");
# Just in case something went wrong last time, remove any overridden
# dev.off().
if (exists("dev.off", where=pos, inherits=FALSE))
rm("dev.off", pos=pos);
# Create the png file.
tmpfile <- tempfile();
png.filename <- paste(tmpfile,".png",sep="");
png(png.filename, width, height, pointsize);
# Call my dev.off() instead (for now).
assign("dev.off", function() {
# Call the original dev.off() in the base package.
fcn <- get("dev.off", mode="function", pos=base.pos);
rm("dev.off", pos=pos);
fcn();
# Get the path to ImageMagick's convert command.
convert <- getOption("imagemagick")["convert"];
if (is.null(convert))
convert <- "convert";
if (regexpr("^[^\"].* .*", convert) != -1)
convert <- paste("\"", convert, "\"", sep="");
# Call 'convert' to convert from temporary png to temporary gif.
gif.filename <- paste(tmpfile, ".gif", sep="");
cmd <- paste(convert, png.filename, gif.filename, sep=" ");
if (.Platform$OS.type == "windows")
system(cmd, invisible=TRUE)
else
system(cmd);
# Renaming the temporary gif file to the wanted filename
file.rename(gif.filename, filename);
# Remove the temporary png file.
file.remove(png.filename);
invisible(filename);
}, pos=pos);
} # gif()
############################################################################
# HISTORY:
# 2001-10-29
# o Updated the gif() command to 1) not create temporary variables in
# .GlobalEnv. It now also convert to a tempory gif file and first then
# rename that to the wanted filename. This means that if the filename
# does not have a *.gif extension a gif file will still be generated.
# Finally, the option "imagemagick" contains named field where one of
# them can be "convert" for specifying the path to the convert command.
# Note that on WinXP Pro there is already a convert command in the path.
# 2001-04-11
# * Created from old com.braju.graphics and made into a static class.
# 2001-03-08 [version 0.2.1]
# * Added close.screen argument to dev.close().
# 2001-03-07 [version 0.2]
# * Added restore.par argument to dev.open().
# * Added dev.close().
# 2001-02-28 [version 0.1]
# * Added Rdoc comments.
# 2001-02-12
# * Added quotaion marks around filenames in 'convert' system call.
# 2001-02-08
# * Created the gif device driver. Removed old eps2gif(); no need for it!
# * Added support for gif outputs.
# 2001-02-07
# * Created.
############################################################################
|
533b49786a6a747e23765df4c9daff6842b9c343
|
58552d1a2700c5a31c1915b27d347fc59ff06283
|
/Insights/tools/All Migration - server - mc.R
|
c3a1ca5c49cab7cb303d870e1b885d915c44e239
|
[
"LicenseRef-scancode-warranty-disclaimer"
] |
no_license
|
Treasury-Analytics-and-Insights/Insights-Tool-April-2018
|
6b56f62df6a87f621abdc14b2e8c6c5b9d833308
|
29944ffc9da5843e6e07613d59c30dc1ceeb5085
|
refs/heads/master
| 2020-03-15T08:21:44.702584
| 2018-07-19T01:10:35
| 2018-07-19T01:10:35
| 132,049,038
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 40,167
|
r
|
All Migration - server - mc.R
|
#Server MC
mig_all_panel <- reactiveValues(
base_data = df.init_mig_all_base_data,
map = spldf.init_mig_all_map,
last_start_year = vt.init_mig_all_param_year_1,
last_end_year = vt.init_mig_all_param_year_2,
tmp_to_use = 1,
last_agg_level = "Total NZ",
vt.last_flow_dir = 'Inflow',
vb_1_value = 'test',
vb_1_text = 'test',
vb_3_value = 'test',
vb_3_text = 'test',
vb_4_value = 'test',
vb_4_text = 'test',
vb_5_value = 'test',
vb_5_text = 'test',
ta_vb_1_value = 'test',
ta_vb_1_text = 'test',
ta_vb_3_value = 'test',
ta_vb_3_text = 'test',
ta_vb_4_value = 'test',
ta_vb_4_text = 'test',
map_init_refresh = 0
)
#Update the base data when the year slider changes
observeEvent(input$mig_all_param_year,{
#Check for unecessary updates caused by the input being bounced after an invalid input
#If the two inputs are directly adjacent and the user then puts them together,
#they will get bounced back to where they were but we don't want to update the data
if(mig_all_panel$last_start_year != input$mig_all_param_year[1] |
mig_all_panel$last_end_year != input$mig_all_param_year[2]){
vt.param_ta <- input$mig_all_param_ta
vt.param_agg_level <- input$mig_all_agg_level
vt.param_year_1 <- input$mig_all_param_year[1]
vt.param_year_2 <- input$mig_all_param_year[2]
#If the user selects the same value for start and end (i.e. pinches the slider together)
if(input$mig_all_param_year[1] == input$mig_all_param_year[2]){
#If the first value has changed then move it back one.
if(input$mig_all_param_year[1] != mig_all_panel$last_start_year &
(input$mig_all_param_year[1] - 1) %in% vt.init_mig_all_param_year_1_select){
updateSliderInput(
session,
"mig_all_param_year",
label = "Year range selection",
min = vt.init_mig_all_year_min,
max = vt.init_mig_all_year_max,
value = c(input$mig_all_param_year[1] - 1, input$mig_all_param_year[2]),
step = 1
)
vt.param_year_1 <- input$mig_all_param_year[1] - 1
}
#If the second value has changed then move it forward one.
if(input$mig_all_param_year[2] != mig_all_panel$last_end_year &
(input$mig_all_param_year[2] + 1) %in% vt.init_mig_all_param_year_2_select){
updateSliderInput(
session,
"mig_all_param_year",
label = "Year range selection",
min = vt.init_mig_all_year_min,
max = vt.init_mig_all_year_max,
value = c(input$mig_all_param_year[1], input$mig_all_param_year[2] + 1),
step = 1
)
vt.param_year_2 <- input$mig_all_param_year[2] + 1
}
}
#If the last update was cancelled and the slider was bounced back to the same position,
#we don't need to update the data
if((vt.param_year_1 != mig_all_panel$last_start_year |
vt.param_year_2 != mig_all_panel$last_end_year) &
vt.param_year_1 != vt.param_year_2){
#Update the base data
if(vt.param_agg_level == "Total NZ"){
df.base_data <- df.db_mig_data %>%
filter(
PREV_YEAR %in% vt.param_year_1,
CURR_YEAR %in% vt.param_year_2
)
}
if(vt.param_agg_level == "Territorial authority"){
df.base_data <- df.db_mig_data %>%
filter(
PREV_YEAR %in% vt.param_year_1,
CURR_YEAR %in% vt.param_year_2,
TA %in% vt.param_ta
)
}
mig_all_panel$base_data <- df.base_data
}
#Record the last position of the slider
mig_all_panel$last_start_year <- vt.param_year_1
mig_all_panel$last_end_year <- vt.param_year_2
}
})
#Update the base data if the TA changes or TA level
observe({
vt.param_ta <- input$mig_all_param_ta
vt.param_agg_level <- input$mig_all_agg_level
isolate({
vt.param_year_1 <- input$mig_all_param_year[1]
vt.param_year_2 <- input$mig_all_param_year[2]
})
if(vt.param_agg_level == "Total NZ"){
df.base_data <- df.db_mig_data %>%
filter(
PREV_YEAR %in% vt.param_year_1,
CURR_YEAR %in% vt.param_year_2
)
mig_all_panel$base_data <- df.base_data
}
if(vt.param_agg_level == "Territorial authority"){
df.base_data <- df.db_mig_data %>%
filter(
PREV_YEAR %in% vt.param_year_1,
CURR_YEAR %in% vt.param_year_2,
TA %in% vt.param_ta
)
if(dim(df.base_data)[1] > 0){
mig_all_panel$base_data <- df.base_data
}
}
})
#Update the map data when the base data, flow dir, TA, or agg_level updates
observe({
df.base_data <- mig_all_panel$base_data
vt.param_flow_name <- input$mig_all_param_flow
vt.param_flow <- input$mig_all_param_flow
vt.param_flow <- df.mig_all_param_flow_map %>%
filter(NAME == vt.param_flow) %>%
.[['VALUE']]
vt.param_perc <- input$mig_all_param_map_perc
vt.param_flow_type <- input$mig_all_param_flow_type
vt.param_flow_type <- df.mig_flow_type_mapping %>%
filter(FLOW_TYPE_LABEL == vt.param_flow_type) %>%
.[["FLOW_TYPE"]]
vt.param_agg_level <- input$mig_all_agg_level
isolate({
vt.last_agg_level <- mig_all_panel$last_agg_level
})
if(vt.param_agg_level == "Total NZ"){
if(vt.param_agg_level != vt.last_agg_level){
updateRadioButtons(session,
inputId = "mig_all_param_flow_type",
label = "Type of Population Change",
choices = vt.init_mig_all_parm_flow_type_select,
selected = vt.init_mig_all_parm_flow_type)
}
if(vt.param_flow_type != 'tot'){
df.base_data = df.base_data %>%
filter(FLOW_DIR == vt.param_flow,
FLOW_TYPE == vt.param_flow_type,
is.na(FLOW_DEMOGRAPHIC),
TA_LEVEL) %>%
inner_join(df.mig_flow_type_mapping,
by = "FLOW_TYPE")
}
if(vt.param_flow_type == 'tot'){
df.base_data = df.base_data %>%
filter(is.na(FLOW_DEMOGRAPHIC),
FLOW_DIR == vt.param_flow,
FLOW_TYPE %in% 2:5) %>%
group_by(TA, TA_CODE, TOTAL_PREV, TOTAL_CURR, PREV_YEAR, CURR_YEAR, FLOW_DIR) %>%
summarise(VALUE = sum(VALUE)) %>%
mutate(FLOW_TYPE = vt.param_flow_type)
}
#Convert to percentage of popn at start period
if(vt.param_perc){
df.base_data = df.base_data %>%
mutate(VALUE = VALUE/TOTAL_PREV)
}
spldf.map_update <- spldf.nz_ta_migration
spldf.map_update@data <- spldf.map_update@data %>%
mutate(code = as.numeric(code)) %>%
left_join(df.base_data,
by = c('code' = 'TA_CODE'))
mig_all_panel$map <- spldf.map_update
}
if(vt.param_agg_level == "Territorial authority"){
if(vt.param_agg_level != vt.last_agg_level){
#Remove other choices at a TA level
updateRadioButtons(session,
inputId = "mig_all_param_flow_type",
label = "Type of Population Change",
choices = vt.init_mig_all_parm_flow_type_ta_select,
selected = vt.init_mig_all_parm_flow_type_ta)
}
df.base_data = df.base_data %>%
filter(FLOW_DIR == vt.param_flow,
FLOW_TYPE == 2,
FLOW_DEMOGRAPHIC == "TA",
TA_LEVEL)
if(vt.param_perc){
df.base_data = df.base_data %>%
mutate(VALUE = VALUE/TOTAL_PREV)
}
spldf.map_update <- spldf.nz_ta_migration
spldf.map_update@data <- spldf.map_update@data %>%
left_join(df.base_data,
by = c('code' = 'FLOW_DEMOGRAPHIC_VALUE'))
mig_all_panel$map <- spldf.map_update
}
mig_all_panel$last_agg_level <- vt.param_agg_level
})
#When the aggregation level is total NZ then disable the TA selection
observeEvent(input$mig_all_agg_level,
{
if(input$mig_all_agg_level == "Total NZ"){
disable("mig_all_param_ta")
}
if(input$mig_all_agg_level != "Total NZ"){
enable("mig_all_param_ta")
}
})
#Help dialogue for chord diagram
observeEvent(input$mig_all_chord_modal_help,
{
vt.param_agg_level <- input$mig_all_agg_level
if(vt.param_agg_level == "Total NZ"){
vt.help_dialogue <- "The chord diagram shows the size and direction of the population moving between Territorial Authorities within NZ. The six TAs with the largest populations are identified separately, as well as Other North Island and Other South Island. Christchurch City has been combined with the Selwyn and Waimakariri Districts and is described as Greater Christchurch. Wellington City has been combined with Porirua, Lower Hutt, and Upper Hutt Cities and is described as Greater Wellington. The size of each segment around the edge of the circle represents the number of people who left that area to move to another area. The width of the chord at each end represents the number of people who moved to the area at the other end of the chord. Each chord is shaded in the colour representing the direction of the predominant movement. For example, chords are shaded with the same colour as the Auckland segment if more people arrived in Auckland than left. When 'Display as a percentage of the population?' is selected the diagram shows the flows as a percentage of the population at the start year of the areas they originate from e.g. if Christchurch -> Auckland is 0.50% this means that 0.50% of the size of the Christchurch population at the start year moved from Christchurch to Auckland. Move the mouse over a segment at the edge of the circle to see all movements related to that area, or over a chord to see more detail about the movements that chord represents."
}
if(vt.param_agg_level == "Territorial authority"){
vt.param_ta <- input$mig_all_param_ta
vt.help_dialogue <- glue("The chord diagram shows the size and direction of the population moving to and from {vt.param_ta}. The five TAs with the largest flows to and from {vt.param_ta} are identified separately, as well as Other North Island and Other South Island. The size of each segment around the edge of the circle represents the number of people who left that area to move to another area. The width of the chord at each end represents the number of people who moved to the area at the other end of the chord. Each chord is shaded in the colour representing the direction of the predominant movement. For example, chords are shaded in with the same colour as the {vt.param_ta} segment if more people arrived in {vt.param_ta} than left. When 'Display as a percentage of the population?' is selected the diagram shows the flows as a percentage of the population at the start year of the areas they originate from e.g. if {vt.param_ta} -> {ifelse(vt.param_ta == 'Auckland', 'Christchurch', 'Auckland')} is 0.50% this means that 0.50% of the size of the {vt.param_ta} population at the start year moved from {vt.param_ta} to {ifelse(vt.param_ta == 'Auckland', 'Christchurch', 'Auckland')}. Move the mouse over a segment at the edge of the circle to see all movements related to that area, or over a chord to see more detail about the movements that chord represents.")
}
showModal(modalDialog(
title = "About this chord diagram",
vt.help_dialogue,
easyClose = TRUE,
footer = NULL
))
})
#Help dialogue for chord diagram
observeEvent(input$mig_all_pyramid_modal_help,
{
vt.param_agg_level <- input$mig_all_agg_level
vt.param_start_year <- input$mig_all_param_year[1]
vt.param_end_year <- input$mig_all_param_year[2]
if(vt.param_agg_level == "Total NZ"){
vt.help_dialogue <- glue("The leftmost population pyramid shows how the NZ population has changed from {vt.param_start_year} to {vt.param_end_year}. The drivers of this change are represented by the pyramids to the right, showing the effects of; international migration; and natural increase and ageing. Click the corresponding legend entry to select or unselect each factor.")
}
if(vt.param_agg_level == "Territorial authority"){
vt.param_ta <- input$mig_all_param_ta
vt.help_dialogue <- glue("The leftmost population pyramid shows how the {vt.param_ta} population has changed from {vt.param_start_year} to {vt.param_end_year}. The drivers of this change are represented by the pyramids to the right, showing the effects of; migration within NZ; international migration; and natural increase and ageing. Click the corresponding legend entry to select or unselect each factor.")
}
showModal(modalDialog(
title = "About this chord diagram",
vt.help_dialogue,
easyClose = TRUE,
footer = NULL
))
})
#Help dialogue for the page
observeEvent({input$mig_all_page_modal_help},
{
vt.help_dialogue1 <- glue("Population explorer presents new data about population change in New Zealand (NZ) between {min(vt.mig_all_year_combn$PREV_YEAR)} and {max(vt.mig_all_year_combn$CURR_YEAR)} through interactive graphs and maps. Using this tool you can investigate changes in the population of New Zealand and specific territorial authorities over time. Different types of population change can be examined, including: internal migration within NZ, international migration to and from NZ, ageing, and births and deaths (natural increase).")
vt.help_dialogue2 <- glue("The grey boxes at the top of the page present key facts about changes in the population for any selected time period and area. The tabs below these boxes allow different aspects of population change to be examined and mapped.")
vt.help_dialogue3 <- glue("There are a number of controls on the left-hand side of the page which you can use to manipulate the data. You can select different time periods by adjusting the year range selection slider. The 'Display as a percentage?' checkbox allows you to view figures as percentages instead of numbers of people. The level of analysis buttons allow you to switch between figures for all of NZ and figures for a specific territorial authority within NZ. Hovering over the visualisations in this tool with your mouse pointer will reveal additional information.")
showModal(modalDialog(
title = "About this tool",
vt.help_dialogue1,
br(),
br(),
vt.help_dialogue2,
br(),
br(),
vt.help_dialogue3,
easyClose = TRUE,
footer = NULL
))
})
#Headlines for value boxes
observe({
df.base_data <- mig_all_panel$base_data
vt.param_start_year <- input$mig_all_param_year[1]
vt.param_end_year <- input$mig_all_param_year[2]
validate(need(vt.mig_all_year_combn %>%
filter(PREV_YEAR %in% vt.param_start_year, CURR_YEAR %in% vt.param_end_year) %>%
dim() %>%
.[1] >= 1,
"Updating..."))
isolate({
vt.param_agg_level <- input$mig_all_agg_level
vt.param_ta <- input$mig_all_param_ta
})
if(vt.param_agg_level == "Total NZ"){
#Get all different rows for the national vbs
tmp.vb_data <- mig_all_panel$base_data %>%
filter(FLOW_TYPE %in% 2:3 &
FLOW_DIR == "out" &
is.na(FLOW_DEMOGRAPHIC) |
(FLOW_TYPE == 3 &
is.na(FLOW_DEMOGRAPHIC) |
FLOW_DEMOGRAPHIC == "Country")|
(FLOW_TYPE == 4))
tmp.vb_2_data <- df.db_mig_data %>%
filter(PREV_YEAR == vt.param_start_year,
CURR_YEAR == vt.param_end_year) %>%
filter(FLOW_DIR == 'net',
FLOW_TYPE %in% 2 &
FLOW_DEMOGRAPHIC == "TA" &
TA_LEVEL |
FLOW_TYPE %in% 4 &
is.na(FLOW_DEMOGRAPHIC_VALUE))
#VB1
mig_all_panel$vb_1_value <- tmp.vb_data %>%
filter(TA_CODE == 999) %>%
slice(1) %>%
mutate(VALUE_PERC = paste0(formatC(100 * (TOTAL_CURR / TOTAL_PREV - 1),
format = 'f',
digits = 1),
"%")) %>%
.[["VALUE_PERC"]]
tmp.vb_1_test_1 <- tmp.vb_data %>%
filter(TA_CODE == 999) %>%
slice(1) %>%
mutate(test = TOTAL_CURR > TOTAL_PREV) %>%
.[['test']]
tmp.vb_1_text_1 <- tmp.vb_data %>%
filter(TA_CODE == 999,
METRIC == "out_3") %>%
mutate(VALUE_PERC = paste0(formatC(100 * (VALUE / TOTAL_PREV),
format = 'f',
digits = 1),
"%")) %>%
.[["VALUE_PERC"]]
tmp.vb_1_text_2 <- tmp.vb_data %>%
filter(TA_CODE == 999,
METRIC == "out_2") %>%
mutate(VALUE_PERC = paste0(formatC(100 * (VALUE / TOTAL_PREV),
format = 'f',
digits = 1),
"%")) %>%
.[["VALUE_PERC"]]
tmp.vb_1_text_3 <- tmp.vb_data %>%
filter(TA_CODE == 999) %>%
.[["TOTAL_PREV"]] %>%
unique()
tmp.vb_1_text_4 <- tmp.vb_data %>%
filter(TA_CODE == 999) %>%
.[["TOTAL_CURR"]] %>%
unique()
tmp.vb_1_text_5 <- tmp.vb_data %>%
filter(TA_CODE == 999) %>%
mutate(DIFF = abs(TOTAL_CURR-TOTAL_PREV)) %>%
.[["DIFF"]] %>%
unique()
mig_all_panel$vb_1_text <- glue({"{ifelse(tmp.vb_1_test_1, 'increase', 'decrease')} in the population of New Zealand between {vt.param_start_year} and {vt.param_end_year} from {comma(tmp.vb_1_text_3)} to {comma(tmp.vb_1_text_4)} ({ifelse(tmp.vb_1_test_1, 'an increase', 'a decrease')} of {comma(tmp.vb_1_text_5)}). Of the {vt.param_start_year} population {tmp.vb_1_text_1} left the country, while {tmp.vb_1_text_2} moved to another area"})
#vb3
tmp.vb_3_value_val <- (tmp.vb_data %>%
filter(METRIC == "in_3",
TA_CODE == 999) %>%
.[["VALUE"]] - tmp.vb_data %>%
filter(METRIC == "in_3_country_NZ",
TA_CODE == 999) %>%
.[["VALUE"]])
tmp.vb_3_value <- tmp.vb_3_value_val %>%
# round(-3) %>%
comma()
mig_all_panel$vb_3_value <- tmp.vb_3_value
tmp.vb_3_text_1_val <- (tmp.vb_data %>%
filter(METRIC == "out_3",
TA_CODE == 999) %>%
.[["VALUE"]] - tmp.vb_data %>%
filter(METRIC == "out_3_country_NZ",
TA_CODE == 999) %>%
.[["VALUE"]])
tmp.vb_3_text_1 <- tmp.vb_3_text_1_val %>%
# round(-3) %>%
comma()
tmp.vb_3_text_2 <- tmp.vb_data %>%
filter(METRIC == "out_3",
TA_CODE == 999) %>%
.[["TOTAL_PREV"]]
tmp.vb_3_test <- (tmp.vb_3_value_val - tmp.vb_3_text_1_val) > 0
tmp.vb_3_text_2 <- paste0(formatC(100 * (tmp.vb_3_value_val - tmp.vb_3_text_1_val) / tmp.vb_3_text_2,
format = 'f',
digits = 1),
"%")
tmp.vb_3_text_5 <- abs(tmp.vb_3_value_val - tmp.vb_3_text_1_val)
tmp.vb_3_text_3 <- tmp.vb_data %>%
filter(FLOW_DEMOGRAPHIC == "Country",
FLOW_TYPE == 3,
FLOW_DIR == 'in',
!(FLOW_DEMOGRAPHIC_VALUE %in% c("NZ", "ZZ")),
TA_CODE == 999) %>%
filter(VALUE == max(VALUE))
tmp.vb_3_text_4 <- tmp.vb_3_text_3 %>%
.[["VALUE"]]%>%
# round(-3) %>%
comma()
tmp.vb_3_text_3 <- tmp.vb_3_text_3 %>%
inner_join(df.mig_all_world_country_vb_mapping,
by = c("FLOW_DEMOGRAPHIC_VALUE" = "ID")) %>%
.[["code"]]
mig_all_panel$vb_3_text <- glue("overseas migrants arrived in New Zealand between {vt.param_start_year} and {vt.param_end_year}, while {tmp.vb_3_text_1} left. This represents {ifelse(tmp.vb_3_test, 'an increase', 'a decrease')} of {comma(tmp.vb_3_text_5)} or {tmp.vb_3_text_2} of the NZ population. The largest source of migrants was {tmp.vb_3_text_3} with {tmp.vb_3_text_4} arrivals.")
#vb 4
mig_all_panel$vb_4_value <- tmp.vb_data %>%
filter(METRIC == "in_3_country_NZ",
TA_CODE == 999) %>%
.[["VALUE"]]%>%
# round(-3) %>%
comma()
tmp.vb_4_text_1 <- tmp.vb_data %>%
filter(METRIC == "out_3_country_NZ",
TA_CODE == 999) %>%
.[["VALUE"]] %>%
# round(-3) %>%
comma()
tmp.vb_4_text_2 <- tmp.vb_data %>%
filter(METRIC == "net_3_country_NZ",
TA_CODE == 999) %>%
mutate(VALUE_PERC = paste0(formatC(100 * abs(VALUE)/TOTAL_PREV,
format = 'f',
digits = 1),
"%"))
tmp.vb_4_text_5 <- tmp.vb_4_text_2 %>%
mutate(VALUE=abs(VALUE)) %>%
.[["VALUE"]]
tmp.vb_4_text_2 <- tmp.vb_4_text_2 %>%
.[["VALUE_PERC"]]
tmp.vb_4_test <- tmp.vb_data %>%
filter(METRIC == "net_3_country_NZ",
TA_CODE == 999) %>%
mutate(test = VALUE > 0) %>%
.[['test']]
mig_all_panel$vb_4_text <- glue("New Zealanders arrived in NZ by {vt.param_end_year} after living overseas in {vt.param_start_year}, while {tmp.vb_4_text_1} departed by {vt.param_end_year} after living in NZ in {vt.param_start_year}. This represents {ifelse(tmp.vb_4_test, 'an increase', 'a decrease')} of {comma(tmp.vb_4_text_5)} or {tmp.vb_4_text_2} of the NZ population.")
mig_all_panel$vb_5_value <- tmp.vb_data %>%
filter(METRIC == "in_4",
TA_CODE == 999) %>%
.[["VALUE"]] %>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.vb_5_text_1 <- tmp.vb_data %>%
filter(METRIC == "out_4",
TA_CODE == 999) %>%
.[["VALUE"]] %>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.vb_5_text_2 <- tmp.vb_data %>%
filter(METRIC == "net_4",
TA_CODE == 999) %>%
mutate(VALUE_PERC = paste0(formatC(100 * abs(VALUE) / TOTAL_PREV,
format = 'f',
digits = 1),
"%"))
tmp.vb_5_test <- tmp.vb_data %>%
filter(METRIC == "net_4",
TA_CODE == 999) %>%
mutate(test = VALUE > 0) %>%
.[['test']]
tmp.vb_5_text_3 <- tmp.vb_2_data %>%
filter(FLOW_TYPE == 4) %>%
mutate(VALUE_PERC = VALUE / TOTAL_PREV) %>%
filter(VALUE_PERC == max(VALUE_PERC, na.rm = T)) %>%
mutate(VALUE_PERC = paste0(formatC(100 * VALUE_PERC,
format = 'f',
digits = 1),
"%"))
tmp.vb_5_text_4 <- tmp.vb_5_text_3 %>%
.[["TA"]]
tmp.vb_5_text_3 <- tmp.vb_5_text_3 %>%
.[["VALUE_PERC"]]
tmp.vb_5_text_5 <- tmp.vb_5_text_2 %>%
.[["VALUE"]]
tmp.vb_5_text_2 <- tmp.vb_5_text_2 %>%
.[["VALUE_PERC"]]
mig_all_panel$vb_5_text <- glue("children were born in New Zealand between June {vt.param_start_year} and June {vt.param_end_year} while {tmp.vb_5_text_1} people died. This represents a natural increase of {comma(tmp.vb_5_text_5)} or {tmp.vb_5_text_2}. The largest natural increase of {tmp.vb_5_text_3} was in {tmp.vb_5_text_4}.")
}
if(vt.param_agg_level == "Territorial authority"){
#vb1
tmp.vb_data <- mig_all_panel$base_data
#National facts for comparison
tmp.vb_data_nat <- df.db_mig_data %>%
filter(TA_CODE == 999,
PREV_YEAR == vt.param_start_year,
CURR_YEAR == vt.param_end_year,
METRIC %in% c("net_3",
"net_3_country_NZ",
"net_4") |
(FLOW_DEMOGRAPHIC == "Country" &
!is.na(FLOW_DEMOGRAPHIC_VALUE) &
FLOW_DEMOGRAPHIC_VALUE != "NZ" &
FLOW_DIR == 'net' &
FLOW_TYPE == 3)
)
#VB1
tmp.ta_vb_1_value <- tmp.vb_data %>%
slice(1) %>%
mutate(VALUE = TOTAL_CURR / TOTAL_PREV - 1,
VALUE_PERC = paste0(formatC(100 * abs(TOTAL_CURR / TOTAL_PREV - 1),
format = 'f',
digits = 1),
"%"))
tmp.ta_vb_1_value_test <- tmp.ta_vb_1_value %>%
.[["VALUE"]] > 0
mig_all_panel$ta_vb_1_value <- tmp.ta_vb_1_value %>%
.[["VALUE_PERC"]]
tmp.ta_vb_1_text_1 <- tmp.vb_data_nat %>%
slice(1) %>%
mutate(VALUE_PERC = paste0(formatC(100 * abs(TOTAL_CURR / TOTAL_PREV - 1),
format = 'f',
digits = 1),
"%")) %>%
.[['VALUE_PERC']]
tmp.ta_vb_1_text_1_test <- tmp.vb_data_nat %>%
slice(1) %>%
mutate(test = (TOTAL_CURR / TOTAL_PREV - 1) > 0) %>%
.[["test"]]
tmp.ta_vb_1_text_2 <- tmp.vb_data %>%
filter(METRIC == "net_2") %>%
mutate(VALUE_PERC = paste0(formatC(100 * (VALUE / TOTAL_PREV),
format = 'f',
digits = 1),
"%")) %>%
.[['VALUE_PERC']]
tmp.ta_vb_1_text_6 <- tmp.vb_data %>%
filter(METRIC == "net_2") %>%
mutate(VALUE = abs(VALUE)) %>%
.[['VALUE']]
tmp.ta_vb_1_text_2_test <- tmp.vb_data %>%
filter(METRIC == "net_2") %>%
mutate(test = VALUE > 0) %>%
.[["test"]]
tmp.ta_vb_1_text_3 <- tmp.vb_data %>%
.[["TOTAL_PREV"]] %>%
unique()
tmp.ta_vb_1_text_4 <- tmp.vb_data %>%
.[["TOTAL_CURR"]] %>%
unique()
tmp.ta_vb_1_text_5 <- tmp.vb_data %>%
mutate(DIFF = abs(TOTAL_CURR-TOTAL_PREV)) %>%
.[["DIFF"]] %>%
unique()
tmp.ta_vb_1_text <- glue("{ifelse(tmp.ta_vb_1_value_test, 'increase', 'decrease')} in the population of {vt.param_ta} between {vt.param_start_year} and {vt.param_end_year} from {comma(tmp.ta_vb_1_text_3)} to {comma(tmp.ta_vb_1_text_4)} ({ifelse(tmp.ta_vb_1_value_test, 'an increase', 'a decrease')} of {comma(tmp.ta_vb_1_text_5)}). This compares to {ifelse(tmp.ta_vb_1_text_1_test, 'an increase', 'a decrease')} of {tmp.ta_vb_1_text_1} for all NZ. Migration within NZ contributed {tmp.ta_vb_1_text_2} ({ifelse(tmp.ta_vb_1_text_2_test, 'an increase', 'a decrease')} of {comma(tmp.ta_vb_1_text_6)}) to this {ifelse(tmp.ta_vb_1_value_test, 'increase', 'decrease')}.")
#VB3
mig_all_panel$ta_vb_3_value <- tmp.vb_data %>%
filter(FLOW_DEMOGRAPHIC == "Country",
!is.na(FLOW_DEMOGRAPHIC_VALUE),
FLOW_DEMOGRAPHIC_VALUE != "NZ",
FLOW_DIR == 'in',
FLOW_TYPE == 3) %>%
summarise(VALUE = sum(VALUE)) %>%
.[["VALUE"]] %>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.ta_vb_3_text_1 <- tmp.vb_data %>%
filter(FLOW_DEMOGRAPHIC == "Country",
!is.na(FLOW_DEMOGRAPHIC_VALUE),
FLOW_DEMOGRAPHIC_VALUE != "NZ",
FLOW_DIR == 'out',
FLOW_TYPE == 3) %>%
summarise(VALUE = sum(VALUE)) %>%
.[["VALUE"]] %>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.ta_vb_3_text_2 <- tmp.vb_data %>%
filter(FLOW_DEMOGRAPHIC == "Country",
!is.na(FLOW_DEMOGRAPHIC_VALUE),
FLOW_DEMOGRAPHIC_VALUE != "NZ",
FLOW_DIR == 'net',
FLOW_TYPE == 3) %>%
summarise(VALUE = sum(VALUE),
TOTAL_PREV = TOTAL_PREV[1]) %>%
mutate(
test = VALUE > 0,
VALUE_PERC = paste0(formatC(100 * abs(VALUE/TOTAL_PREV),
format = 'f',
digits = 1),
"%"))
tmp.ta_vb_3_text_2_test <- tmp.ta_vb_3_text_2 %>%
.[['test']]
tmp.ta_vb_3_text_6 <- tmp.ta_vb_3_text_2 %>%
.[["VALUE"]]
tmp.ta_vb_3_text_2 <- tmp.ta_vb_3_text_2 %>%
.[["VALUE_PERC"]]
tmp.ta_vb_3_text_3 <- tmp.vb_data_nat %>%
filter(FLOW_DEMOGRAPHIC == "Country",
!is.na(FLOW_DEMOGRAPHIC_VALUE),
FLOW_DEMOGRAPHIC_VALUE != "NZ",
FLOW_DIR == 'net',
FLOW_TYPE == 3) %>%
summarise(VALUE = sum(VALUE),
TOTAL_PREV = TOTAL_PREV[1]) %>%
mutate(test = VALUE > 0,
VALUE_PERC = paste0(formatC(100 * abs(VALUE)/TOTAL_PREV,
format = 'f',
digits = 1),
"%"))
tmp.ta_vb_3_text_3_test <- tmp.ta_vb_3_text_3 %>%
.[['test']]
tmp.ta_vb_3_text_3 <- tmp.ta_vb_3_text_3 %>%
.[['VALUE_PERC']]
tmp.ta_vb_3_text_4 <- tmp.vb_data %>%
filter(FLOW_DEMOGRAPHIC == "Country",
!is.na(FLOW_DEMOGRAPHIC_VALUE),
FLOW_TYPE == 3,
FLOW_DIR == 'in',
!(FLOW_DEMOGRAPHIC_VALUE %in% c("NZ", "ZZ"))) %>%
filter(VALUE == max(VALUE)) %>%
#Don't want ties
slice(1) %>%
inner_join(df.mig_all_world_country_vb_mapping,
by = c("FLOW_DEMOGRAPHIC_VALUE" = 'ID')) %>%
.[["code"]]
tmp.ta_vb_3_text_5 <- tmp.vb_data %>%
filter(FLOW_DEMOGRAPHIC == "Country",
!is.na(FLOW_DEMOGRAPHIC_VALUE),
FLOW_TYPE == 3,
FLOW_DIR == 'in',
!(FLOW_DEMOGRAPHIC_VALUE %in% c("NZ", "ZZ"))) %>%
filter(VALUE == max(VALUE)) %>%
#No ties
slice(1) %>%
.[["VALUE"]] %>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.ta_vb_3_text <- glue("overseas migrants arrived in {vt.param_ta} between {vt.param_start_year} and {vt.param_end_year}, while {tmp.ta_vb_3_text_1} left. This represents {ifelse(tmp.ta_vb_3_text_2_test, 'an increase', 'a decrease')} of {comma(tmp.ta_vb_3_text_6)} or {tmp.ta_vb_3_text_2} of the population, compared to a {tmp.ta_vb_3_text_3} {ifelse(tmp.ta_vb_3_text_3_test, 'increase', 'decrease')} nationally. The largest source of migrants was {tmp.ta_vb_3_text_4}, with {tmp.ta_vb_3_text_5} arrivals.")
#VB4
mig_all_panel$ta_vb_4_value <- tmp.vb_data %>%
filter(METRIC == "in_3_country_NZ") %>%
.[["VALUE"]] %>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.ta_vb_4_text_1 <- tmp.vb_data %>%
filter(METRIC == "out_3_country_NZ") %>%
.[["VALUE"]] %>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.ta_vb_4_text_2 <- tmp.vb_data %>%
filter(METRIC == "net_3_country_NZ") %>%
mutate(VALUE_PERC = paste0(formatC(100 * abs(VALUE)/TOTAL_PREV,
format = 'f',
digits = 1),
"%")) %>%
.[["VALUE_PERC"]]
tmp.ta_vb_4_test_1 <- tmp.vb_data %>%
filter(METRIC == "net_3_country_NZ") %>%
mutate(test = VALUE > 0) %>%
.[['test']]
tmp.ta_vb_4_text_3 <- tmp.vb_data_nat %>%
filter(METRIC == "net_3_country_NZ") %>%
mutate(VALUE_PERC = paste0(formatC(100 * abs(VALUE)/TOTAL_PREV,
format = 'f',
digits = 1),
"%")) %>%
.[["VALUE_PERC"]]
tmp.ta_vb_4_text_5 <- tmp.vb_data %>%
filter(METRIC == "net_3_country_NZ") %>%
mutate(VALUE = abs(VALUE)) %>%
.[["VALUE"]]
tmp.ta_vb_4_test_2 <- tmp.vb_data_nat %>%
filter(METRIC == "net_3_country_NZ") %>%
mutate(test = VALUE > 0) %>%
.[['test']]
tmp.ta_vb_4_text <- glue("New Zealanders arrived in {vt.param_ta} by {vt.param_end_year} after living overseas in {vt.param_start_year}, while {tmp.ta_vb_4_text_1} departed NZ by {vt.param_end_year} after living in {vt.param_ta} in {vt.param_start_year}. This represents {ifelse(tmp.ta_vb_4_test_1, 'an increase', 'a decrease')} of {comma(tmp.ta_vb_4_text_5)} or {tmp.ta_vb_4_text_2} of the {vt.param_ta} population, compared to {ifelse(tmp.ta_vb_4_test_2, 'an increase', 'a decrease')} of {tmp.ta_vb_4_text_3} across NZ.")
#VB5
tmp.ta_vb_5_value <- tmp.vb_data %>%
filter(METRIC == 'in_4') %>%
.[["VALUE"]]
mig_all_panel$ta_vb_5_value <- tmp.ta_vb_5_value %>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.ta_vb_5_text_1_val <- tmp.vb_data %>%
filter(METRIC == 'out_4') %>%
.[["VALUE"]]
tmp.ta_vb_5_text_1 <- tmp.ta_vb_5_text_1_val%>%
as.integer() %>%
# round(-(nchar(.) - 3)) %>%
comma()
tmp.ta_vb_5_test_1 <- tmp.ta_vb_5_value > tmp.ta_vb_5_text_1_val
tmp.ta_vb_5_text_2 <- paste0(formatC(100 * (tmp.ta_vb_5_value - tmp.ta_vb_5_text_1_val) / tmp.vb_data %>%
filter(METRIC == 'in_4') %>%
.[["TOTAL_PREV"]],
format = 'f',
digits = 1),
"%")
tmp.ta_vb_5_text_5 <- abs(tmp.ta_vb_5_value - tmp.ta_vb_5_text_1_val)
tmp.ta_vb_5_text_3 <- tmp.vb_data_nat %>%
filter(METRIC == 'net_4') %>%
mutate(VALUE_PERC = paste0(formatC(100 * VALUE / TOTAL_PREV,
format = 'f',
digits = 1),
"%")) %>%
.[["VALUE_PERC"]]
tmp.ta_vb_5_text_3_test <- tmp.vb_data_nat %>%
filter(METRIC == 'net_4') %>%
.[['VALUE']] > 0
tmp.ta_vb_5_text <- glue("children were born in {vt.param_ta} between June {vt.param_start_year} and June {vt.param_end_year} while {tmp.ta_vb_5_text_1} people died. This represents a natural {ifelse(tmp.ta_vb_5_test_1, 'increase', 'decrease')} of {comma(tmp.ta_vb_5_text_5)} or {tmp.ta_vb_5_text_2}, compared to a natural {ifelse(tmp.ta_vb_5_text_3_test, 'increase', 'decrease')} of {tmp.ta_vb_5_text_3} across New Zealand.")
mig_all_panel$ta_vb_1_text <- tmp.ta_vb_1_text
mig_all_panel$ta_vb_3_text <- tmp.ta_vb_3_text
mig_all_panel$ta_vb_4_text <- tmp.ta_vb_4_text
mig_all_panel$ta_vb_5_text <- tmp.ta_vb_5_text
}
})
#Add map download
output$mig_all_map_download <- downloadHandler(
filename = function(){
vt.param_agg_level <- input$mig_all_agg_level
if(vt.param_agg_level == "Total NZ"){
return(paste0("NZ_migration_map_data_", format(Sys.time(), "%x_%H:%M"), ".csv"))
} else{
vt.param_ta <- input$mig_all_param_ta
return(paste0(vt.param_ta, "_migration_map_data_", format(Sys.time(), "%x_%H:%M"), ".csv"))
}
},
content = function(file) {
# browser()
isolate({
vt.param_agg = input$mig_all_agg_level
vt.param_perc = input$mig_all_param_map_perc
vt.param_map_flow = input$mig_all_param_flow
})
df = mig_all_panel$map@data
if(vt.param_agg == vt.init_mig_all_agg_level_select[1]){
if(!vt.param_perc){
df = national_map_csv_reformat(df, df.mig_all_map_national_rename_absolute)
} else{
df = df %>%
mutate(VALUE = paste0(formatC(100 * VALUE,
format = 'f',
digits = 2),
"%"))
df = national_map_csv_reformat(df, df.mig_all_map_national_rename_percentage)
}
} else{
vt.flow_dir_value = df.mig_natural_change_flow_dir_mapping %>%
filter(FLOW_DIR %in% vt.param_map_flow) %>%
.[["FLOW_DIR_VALUE"]]
if(!vt.param_perc){
if(vt.flow_dir_value %in% vt.mig_all_param_flow_dir_in){
df = ta_map_csv_reformat(df, df.mig_all_map_ta_rename_in_absolute)
} else{
df = ta_map_csv_reformat(df, df.mig_all_map_ta_rename_out_absolute)
}
} else{
if(vt.flow_dir_value %in% vt.mig_all_param_flow_dir_in){
df = df %>%
mutate(VALUE = paste0(formatC(100 * VALUE,
format = 'f',
digits = 2),
"%"))
df = ta_map_csv_reformat(df, df.mig_all_map_ta_rename_in_percentage)
} else{
df = df %>%
mutate(VALUE = paste0(formatC(100 * VALUE,
format = 'f',
digits = 2),
"%"))
df = ta_map_csv_reformat(df, df.mig_all_map_ta_rename_out_percentage)
}
}
}
write.csv(df, file, row.names = F)
})
#Invalidate the map the first time it's displayed.
observeEvent({input$grey_tabBox},
{
if(mig_all_panel$map_init_refresh == 0 &
input$grey_tabBox == vt.mig_all_map_tab_title){
mig_all_panel$map_init_refresh <- mig_all_panel$map_init_refresh + 1
}
})
|
a058f88a4f7110e068e7556ce0186b8adde3851f
|
38915da347869e164d9485f9bbd394fe56d2fcb0
|
/1_1MFScondist_jp/ui_E.R
|
0c7008e0eecffc750e814ce622eecd8c1584d83d
|
[
"MIT"
] |
permissive
|
mephas/mephas_web
|
24df65c5bdbf1e65c91523f4bfd120abae03e409
|
197e99828d6b9a6a3c1d11b2fc404c9631103ec0
|
refs/heads/master
| 2023-07-21T14:29:37.597163
| 2023-07-13T03:32:32
| 2023-07-13T03:32:32
| 161,571,944
| 8
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,546
|
r
|
ui_E.R
|
#****************************************************************************************************************************************************1.2. Exp distribution
sidebarLayout(
sidebarPanel(
h4(tags$b("Step 1. データソースを選ぶ")),
p("数式ベース、シミュレーションベース、又は、ユーザのデータベース"),
#Select Src
selectInput(
"InputSrc_e", "選択肢",
c("数式ベース" = "MathDist",
"シミュレーションベース" = "SimuDist",
"ユーザデータベース" = "DataDist")),
hr(),
#Select Src end
h4(tags$b("Step 2. パラメータの設定")),
#condiPa 1
conditionalPanel(
condition = "input.InputSrc_e == 'MathDist'",
#"Draw an Exponential Distribution", p(br()),
HTML("<b>パラメータの設定(E(Rate))</b></h4>"),
numericInput("r", HTML(" 率(> 0) とは変化率を指します。率を入力"), value = 2, min = 0),
hr(),
numericInput("e.mean", HTML("または平均とSD (平均 = SD) から率を計算するために平均を入力"), value = 0.5, min = 0),
verbatimTextOutput("e.rate"),
p("Mean = SD = 1/Rate"),
hr(),
numericInput("e.xlim", "2. x軸の範囲(> 0)を変更します。", value = 5, min = 1)
),
#condiPa 2
conditionalPanel(
condition = "input.InputSrc_e == 'SimuDist'",
numericInput("e.size", "シミュレートした数の標本サイズ", value = 100, min = 1,step = 1),
sliderInput("e.bin", "ヒストグラムのビンの数", min = 0, max = 100, value = 0),
p("ビンの数が0の場合はプロットでデフォルトのビンの数が使用されます。")
),
#condiPa 2 end
#condiPa 3
conditionalPanel(
condition = "input.InputSrc_e == 'DataDist'",
tabsetPanel(
tabPanel("手入力",p(br()),
p("データポイントは「,」「;」「Enter」「Tab」で区切ることができます。"),
p(tags$b("データはCSV(1列)からコピーされ、ボックスに貼り付けられます")),
tags$textarea(
id = "x.e", #p
rows = 10,
"2.6\n0.5\n0.8\n2.3\n0.3\n2\n0.5\n4.4\n0.1\n1.1\n0.7\n0.2\n0.7\n0.6\n3.7\n0.3\n0.1\n1\n2.6\n1.3"
),
p("欠損値はNAとして入力されます")
),
tabPanel.upload.num(file ="e.file", header="e.header", col="e.col", sep="e.sep")
),
sliderInput("bin.e", "ヒストグラムのビンの数", min = 0, max = 100, value = 0),
p("ビンの数が0の場合はプロットでデフォルトのビンの数が使用されます。")
), #condiPa 3 end
hr(),
h4(tags$b("Step 3. 確率を表示する")),
numericInput("e.pr", HTML("赤線の左側の面積の割合 = Pr(X < x<sub>0</sub>)で、赤線の位置が x<sub>0</sub> です。"), value = 0.05, min = 0, max = 1, step = 0.05),
hr()
), #sidePa end
mainPanel(
h4(tags$b("Outputs")),
conditionalPanel(
condition = "input.InputSrc_e == 'MathDist'",
h4("数式ベースプロット"),
tags$b("指数分布プロット"),
plotOutput("e.plot", click = "plot_click9"),#
verbatimTextOutput("e.info"),
#p(br()),
#p(tags$b("赤線の位置")),
#p(tags$b("赤線の位置、 x<sub>0</sub>")),
#verbatimTextOutput("e"),
hr(),
# plotly::plotlyOutput("e.plot.cdf")
plotOutput("e.plot.cdf")
),
conditionalPanel(
condition = "input.InputSrc_e == 'SimuDist'",
h4("シミュレーションベースプロット"),
tags$b("乱数から作成したヒストグラム"),
plotly::plotlyOutput("e.plot2"),#click = "plot_click10",
#verbatimTextOutput("e.info2"),
downloadButton("download2", "乱数をダウンロードする"),
p(tags$b("サンプルの記述統計")),
tableOutput("e.sum")
),
conditionalPanel(
condition = "input.InputSrc_e == 'DataDist'",
tags$b("データの確認"),
DT::DTOutput("Y"),
h4("データの分布"),
tags$b("アプロードされたデータの密度プロット"),
plotly::plotlyOutput("makeplot.e2"),
tags$b("アプロードされたデータのヒストグラム"),
plotly::plotlyOutput("makeplot.e1"),
tags$b("アプロードされた累積密度プロット（CDF）"),
plotly::plotlyOutput("makeplot.e3"),
p(tags$b("データの記述統計")),
tableOutput("e.sum2")
)
)
)
|
0df7e8e54a78248aa59b2eed3e02684cff1ff8f0
|
afdac1aa116adcd2cb004a636ebd1cf342b878cc
|
/man/calculate_conditioned_rdi.Rd
|
1faec6b41babb608cd29fd59c30d3425ce729a9d
|
[] |
no_license
|
aNing1210/Test1
|
6260ae7b5f37d1c3073d146f60ceaf8dedd68533
|
fdc476a998bd1647e121f1af7c1fd9c1a6c7380e
|
refs/heads/master
| 2020-09-16T19:20:09.248930
| 2019-12-12T04:25:31
| 2019-12-12T04:25:31
| 223,865,898
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,890
|
rd
|
calculate_conditioned_rdi.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/function.R
\name{calculate_conditioned_rdi}
\alias{calculate_conditioned_rdi}
\title{Calculate conditionally RDI value}
\usage{
calculate_conditioned_rdi(cds_subset, super_graph = NULL, rdi_list,
top_incoming_k = 1, uniformalize = FALSE, log = TRUE,
pseudo_cnt = 1)
}
\arguments{
\item{cds_subset}{A cds_subset which has been ordered with Monocle 2}
\item{super_graph}{A graph including all possible interactions used for performing the causality inference. When it is NULL,
all possible pairs of interactions for the genes_data are used, otherwise only the interactions specified in the graph are used.
Note that the super_graph only accept integer matrix for now (each integer corresponds to a particular gene in the genes_data).}
\item{rdi_list}{a list returned from calculate_rdi function.}
\item{top_incoming_k}{The number of genes to be conditioned on when calculating the conditional RDI values}
\item{uniformalize}{Whether or not you want to use ucmi to calculate rdi. Default to be false.}
}
\value{
a dataframe storing conditional RDI results. First two columns are the id names for the genes.
}
\description{
This function estimates the conditional RDI value for all gene-pair combination from the genes_data (or that specified in the super_graph)
in the pseudotime/time series data, conditioned on the top top_incoming_k incoming nodes.
The result from the calculate_rdi function will be used to identify the proper delay for the gene-pair under test corresponds to that
with highest RDI as well as the proper delays for the k incoming nodes which corresponds to that with highest RDI values.
}
\examples{
\dontrun{
lung <- load_lung()
rdi_res <- calculate_rdi(exprs(lung)[, order(pData(lung)$Pseudotime)], delays = 5)
lung_res_cRDI <- calculate_conditioned_rdi(lung, rdi_list = rdi_res)
}
}
|
e222ade27c2e4c6b0014cac1b9e3de5f569cf559
|
ee314b07bbbcdff0683cb1083a00f2d94e36ab82
|
/man/rftk-package.Rd
|
ef23dcc0b2ffebd1cb636066fa5e3a294460d436
|
[] |
no_license
|
kingweck/rftk
|
08a6adf3c304d765af7958b948f76849dc2b0b7c
|
d92513178afd258c3aa0db0dd471c5d688d30073
|
refs/heads/master
| 2023-01-22T17:20:32.103051
| 2020-11-30T17:54:06
| 2020-11-30T17:54:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 331
|
rd
|
rftk-package.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rftk.R
\docType{package}
\name{rftk-package}
\alias{rftk}
\alias{rftk-package}
\title{rftk: RF Toolkit}
\description{
R functions for working with RF/wireless data.
}
\author{
\strong{Maintainer}: Aaron Anderson \email{aaron.m.anderson.3@gmail.com}
}
|
79311186b602ac668f3993a0b45f2db961c8874c
|
25c4d0cf195c5907f0ebddb12cb1e8aa9cb74b2e
|
/reporting/Summary_of_Findings.R
|
24dbb25d04e2d8a7153800f417143b9b1f1a3770
|
[] |
no_license
|
rcatlord/reviewing
|
32ad628e9c406c3a8ca655f9e3e40a4ea953e3c1
|
3b9d6ff3ab3af899624a5cbc4d2fa986a131dbd3
|
refs/heads/master
| 2021-06-16T14:22:01.789570
| 2017-05-10T11:01:56
| 2017-05-10T11:01:56
| 79,554,838
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,028
|
r
|
Summary_of_Findings.R
|
## Calculate the corresponding risk for GRADE Summary of Findings tables
# See http://cccrg.cochrane.org/sites/cccrg.cochrane.org/files/public/uploads/Summary%20of%20Findings%20Tables.pdf (p.23)
# Data: Petrosino et al., 2013
# 1. Calculate the Assumed Risk
# i.e. risk in control group = n events / N
p <- 98 / 358
round(p*1000,0)
# 2. Convert the Odds Ratio to Risk Ratio
# RR = OR / (1 – p + (p x OR))
OR <- 1.68
RR <- OR / (1 - p * (1 - OR))
round(RR, 2)
# RR = 1.42
# 3. Repeat for OR confidence intervals
OR_lower <- 1.20
RR_lower <- OR_lower / (1 - p * (1 - OR_lower))
round(RR_lower, 2)
## CI (lower) = 1.14
OR_upper <- 2.36
RR_upper <- OR_upper / (1 - p * (1 - OR_upper))
round(RR_upper, 2)
## CI (upper) = 1.72
# 4. Multiply the RR by the Assumed Risk to obtain the Corresponding Risk
CR <- (RR * p)*1000
round(CR, 0)
# ie 39 per 1000
# 5. Repeat for confidence intervals
CR_lower <- (RR_lower * p)*1000
round(CR_lower, 0)
# ie 311 per 1000
CR_upper <- (RR_upper * p)*1000
round(CR_upper, 0)
# ie 471 per 1000
|
a95d3e35f738f396e848466222aba05716cc7472
|
f2c79b247620463f045187e335e993d8c82e12e8
|
/plot1.R
|
6767785017e01e36e9b6bd8b4d9a6942b66e1dfe
|
[] |
no_license
|
opalh/Exploratory-Data-Analysis-Course-Assignment-1
|
d7721111cb4986780188451825167ddf040a0283
|
5ec1de556739f45cd9c91d01525778d7c6b54ad2
|
refs/heads/master
| 2021-06-22T09:50:54.289684
| 2017-08-22T21:35:40
| 2017-08-22T21:35:40
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 826
|
r
|
plot1.R
|
library(dplyr)
if(!file.exists("E:/R/Exploratory Data Analysis/1/assiment")){dir.create("E:/R/Exploratory Data Analysis/1/assiment")}
url<-"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(url,"E:/R/Exploratory Data Analysis/1/assiment/dataset.zip")
unzip("E:/R/Exploratory Data Analysis/1/assiment/dataset.zip", exdir = "E:/R/Exploratory Data Analysis/1/assiment")
alldata <- read.table("E:/R/Exploratory Data Analysis/1/assiment/household_power_consumption.txt",header=TRUE, na.strings="?", sep=";")
data<-filter(alldata, Date == "1/2/2007"| Date=="2/2/2007")
str(data)
hist(data$Global_active_power,xlab = "Global Active Power (kilowatts)",main="Global Active Power",col = "red")
dev.copy(png, file="plot1.png", height=480, width=480)
dev.off()
|
1111b3fce7b187c8e834e1163daeb0b878af290f
|
7919d7f2e4b9bdef8553efa8b4f814f218d85321
|
/PenalizeMimicry.R
|
f5a685d36b4f9c8aa6aba79df9fb94c6a9098d4e
|
[] |
no_license
|
nklange/Gumbel
|
3ed5e1432f243bf3b73c1956ab3313a3aea7cfc0
|
5fd73cef0ee9d64b5349ddd32ad34cacce35bb68
|
refs/heads/main
| 2023-06-19T20:15:22.359652
| 2021-07-26T08:30:05
| 2021-07-26T08:30:05
| 372,556,096
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 29,611
|
r
|
PenalizeMimicry.R
|
# based on Wagenmakers et al 2004 - find optimal decision rule between models
# i.e. to what extent need which models be penalized to find the optimal decision rule
# in other words, how mimic-y are models even under the optimal decision rule
library(cowplot)
GetGsquared <- function(LL,observedData){
temp <- c(observedData[1:6] * log(observedData[1:6]/sum(observedData[1:6])),
observedData[7:12] * log(observedData[7:12]/sum(observedData[7:12])))
temp[observedData == 0] <- 0
GSq = 2*(LL - -sum(temp) ) # Calculate G^2 (better approximator to Chi-sq dist than Pearson Chi-Sq)
# f$df <- length(observedData)-length(names(get_start_par(model)))-1 # df model (L&F,p.63) n_observations - n_freeparas - 1
#f$pGSq <- 1-pchisq(f$GSq,f$df) # p value of GSq
return(GSq)
}
MimicryFits <- NULL
for (gen in c("genGumbelEVSDT","genGaussianUVSDT","genExGaussNormEVSDT")){
fits <- readRDS("LargeNSimulation/LargeN_mimicry_bestfits.rds") %>%
filter(genmodel == substr(gen,start=4,stop=nchar(gen))) %>% arrange(id,model)
# fits <- readRDS(paste0("SimulationFits/",gen,"_mimicry_bestfits.rds")) %>%
# arrange(id,model) %>% mutate(genmodel = substr(gen,start=4,stop=nchar(gen)))
data <- readRDS(paste0("LargeNSimulation/simulate_",gen,"_data_LN.rds")) %>%
arrange(id)
# data <- readRDS(paste0("SimulationData/simulate_",gen,"_data_mimicry.rds")) %>%
# arrange(id)
LL <- fits %>% #mutate(genmodel = substr(gen,start=4,stop=nchar(gen))) %>%
group_by(genmodel,id,model) %>%
group_nest(keep=T,.key="fit")
# LL <- fits %>% mutate(genmodel = substr(gen,start=4,stop=nchar(gen))) %>%
# group_by(genmodel,id,model) %>%
# group_nest(keep=T,.key="fit")
observedData <- data %>% group_by(genmodel,id) %>% group_nest(keep=T)%>%
dplyr::slice(rep(1:n(),each=3))
# observedData <- data %>% group_by(genmodel,id) %>%
# dplyr::slice(rep(1:n(),each=3))
getGsquaredvals <- LL %>% bind_cols(observedData %>% ungroup() %>% dplyr::select(data)) %>%
mutate(Gsq = map2(.x = fit,.y = data, .f = ~GetGsquared(.x$objective,.y$Freq))) %>%
mutate(objective = map(.x = fit,.f = ~mean(.x$objective))) %>%
mutate(Dev = map(.x = fit,.f = ~mean(2*.x$objective))) %>%
mutate(AIC = map(.x = fit,.f = ~mean(.x$AIC)))
MimicryFits <- MimicryFits %>% bind_rows(getGsquaredvals)
}
testdat <- MimicryFits %>% dplyr::select(genmodel,id,model,Gsq,objective,Dev,AIC) %>%
unnest(cols=c(Gsq,objective,Dev,AIC))
testdat <- testdat %>% filter(model %in% c("GaussianUVSDT","ExGaussNormEVSDT",
"GumbelEVSDT"),
genmodel %in% c("GaussianUVSDT",
"ExGaussNormEVSDT",
"GumbelEVSDT")) %>%
dplyr::select(genmodel,id,model,Dev,AIC)
custompenalty_pair <- function(data,par,modelpair){
calc<-data %>%
mutate(penobjective = case_when(model == modelpair[[2]] ~ Dev + par,
model == modelpair[[1]] ~ Dev)) %>%
pivot_longer(cols = c("Dev","AIC","penobjective"),
names_to = "penalty",values_to="value") %>%
group_by(genmodel,penalty,id) %>%
mutate(winning = ifelse(value == min(value),1,0)) %>%
group_by(genmodel,penalty,model) %>%
summarize(num = sum(winning)) %>%
group_by(genmodel,penalty) %>%
mutate(winprop = num/sum(num))
return(calc)
}
opt_penalty_maxmeanacc_pair <- function(data,par,modelpair){
res <- custompenalty_pair(data,par,modelpair)
meanpw <- sum(res %>% filter(penalty == "penobjective") %>%
filter(genmodel==model) %>% .$num)/
sum(res %>% filter(penalty == "penobjective") %>% .$num)
return(-meanpw)
}
custompenalty <- function(data,par){
UVSDT_pen <- par[[1]]
EG_pen <- par[[2]]
calc<-data %>% mutate(penobjective = case_when(model == "GaussianUVSDT" ~ Dev + UVSDT_pen,
model == "ExGaussNormEVSDT" ~ Dev + EG_pen,
model == "GumbelEVSDT" ~ Dev)) %>%
pivot_longer(cols = c("Dev","AIC","penobjective"),names_to = "penalty",values_to="value") %>%
group_by(genmodel,penalty,id) %>%
mutate(winning = ifelse(value == min(value),1,0)) %>%
group_by(genmodel,penalty,model) %>%
summarize(num = sum(winning),
meanval = mean(value)) %>%
group_by(genmodel,penalty) %>%
mutate(winprop = num/sum(num))
return(calc)
}
opt_penalty_maxmeanacc <- function(data,par){
res <- custompenalty(data,par)
meanpw <- sum(res %>% filter(penalty == "penobjective") %>%
filter(genmodel==model) %>% .$num)/
sum(res %>% filter(penalty == "penobjective") %>% .$num)
return(-meanpw)
}
resultsMaxAcc <- NULL
for(i in c(1:200)){
start <- as_tibble(t(c(runif(1,-1,3),runif(1,-1,3)))) %>% set_colnames(c("s_UVSDT_pen","s_ExGN_pen"))
fits2 <- optim(par = start, opt_penalty_maxmeanacc, data = testdat)
res2 <- as_tibble(t(fits2$par)) %>% set_colnames(c("UVSDT_pen","ExGN_pen")) %>%
mutate(meandiagonal = -fits2$value,
fcteval = fits2$counts[[1]],
rep = i) %>%
bind_cols(start)
resultsMaxAcc <- resultsMaxAcc %>% bind_rows(res2)
}
test <- resultsMaxAcc %>% arrange(-meandiagonal)
saveRDS(resultsMaxAcc,file=paste0("SimulationFits/optimizeconfusionmatrix_meanacc_LN.rds"))
# Optimize pairwise confusion matrices ----------------------------------------
#A,B,C in order
makeplotGOF <- function(i){
modelexpression <- list(c("GumbelEVSDT","GaussianUVSDT"),
c("GumbelEVSDT","ExGaussNormEVSDT"),
c("ExGaussNormEVSDT","GaussianUVSDT"))
modelnames <- list(c("Gumbel","UVSDT"),
c("Gumbel","ExGauss"),
c("ExGauss","UVSDT"))
prepdata<- testdat %>% filter(genmodel %in% modelexpression[[i]] &
model %in% modelexpression[[i]]) %>%
mutate(model = factor(model,levels=modelexpression[[i]])) %>%
group_by(genmodel,id) %>% arrange(genmodel,id,model) %>%
mutate(genmodel= factor(genmodel,levels=modelexpression[[i]]))
dists <- prepdata %>%
mutate(GOFAB = Dev[[1]]-Dev) %>%
filter(model != modelexpression[[i]][[1]])
#
# FgenA <- ecdf(dists %>% filter(genmodel == modelexpression[[i]][[1]]) %>% .$GOFAB)
# FgenB <- ecdf(dists %>% filter(genmodel == modelexpression[[i]][[2]]) %>% .$GOFAB)
#
# z <- uniroot(function(z) FgenA(z) + FgenB(z) - 1,
# interval<-c(min(dists$GOFAB),max(dists$GOFAB)))
# calcempopt <- dists %>% group_by(genmodel) %>%
# summarize(sayA = length(GOFAB[GOFAB < z$root])/length(GOFAB),
# sayB = length(GOFAB[GOFAB > z$root])/length(GOFAB)) %>%
# pivot_longer(names_to="model",values_to="value",cols=c(sayA,sayB)) %>%
# mutate(crit = paste0("optimal = ",round(z$root,2)),
# crittype = "threshold")
#
#
# calcemp0 <- dists %>% group_by(genmodel) %>%
# summarize(sayA = length(GOFAB[GOFAB < 0])/length(GOFAB),
# sayB = length(GOFAB[GOFAB > 0])/length(GOFAB)) %>%
# pivot_longer(names_to="model",values_to="value",cols=c(sayA,sayB)) %>%
# mutate(crit = "nominal = 0",
# crittype = "threshold")
maxmean <- optimize(f=opt_penalty_maxmeanacc_pair,data = prepdata,
interval = c(-50,
50),
modelpair=modelexpression[[i]])
naxm <- custompenalty_pair(prepdata,maxmean$minimum,modelexpression[[i]])
critmaxdiag <- naxm %>%
mutate(model = case_when(model == modelexpression[[i]][[1]] ~ "sayA",
TRUE~"sayB")) %>% ungroup() %>% dplyr::select(-num) %>%
rename("value" = winprop) %>%
mutate(genmodel = as.character(genmodel))
Accuracy <- naxm %>% group_by(penalty) %>%
filter(genmodel==model) %>%
summarize(value = mean(winprop))%>%
mutate(model = "sayA") %>%
mutate(genmodel = "ZAcc")
Penalty <- naxm %>% group_by(penalty) %>% dplyr::select(penalty,model) %>% distinct() %>%
mutate(value = case_when(model != "GumbelEVSDT" & penalty == "AIC" ~ ifelse(i == 3,0,2),
penalty == "Dev" ~ 0,
penalty == "penobjective" & model != modelexpression[[i]][[1]] ~ maxmean$minimum,
TRUE ~ 0))%>%
mutate(model = case_when(model == modelexpression[[i]][[1]] ~ "sayA",
TRUE~"sayB")) %>%
mutate(genmodel = "YPen")
tablegraph <- bind_rows(critmaxdiag,Accuracy,Penalty) %>%
mutate(penalty = factor(penalty,levels=c("Dev","AIC","penobjective"),
labels=c("-2LL","AIC","Max Acc"))) %>%
# mutate(model = factor(model)) %>%
mutate(genmodel = factor(genmodel,levels=c("ZAcc","YPen",rev(modelexpression[[i]])),
labels = c("Accuracy","Penalty",rev(modelnames[[i]])))) %>%
mutate(colorunder = ifelse(!genmodel %in% modelnames[[i]],1,0)) %>%
mutate(colorunder = factor(colorunder))
nameexpression <- c(expression(paste(Delta, GOF[GumUVSD]," = ", GOF[Gumbel] - GOF[UVSD])),
expression(paste(Delta, GOF[GumExG]," = ", GOF[Gumbel] - GOF[ExGauss])),
expression(paste(Delta, GOF[ExGUVSD]," = ", GOF[ExGauss] - GOF[UVSD])))
placeopt <- which(sort(c(seq(-5,10,5),maxmean$minimum)) == maxmean$minimum)
prelabels <- c(-5,0,5,10)
xlabels <- append(prelabels,"\nopt",after=placeopt-1)
colorexpression <- list(c("#009E73","#E69F00" ),
c( "#009E73","#0072B2"),
c("#0072B2","#E69F00"))
A <- ggplot(dists,aes(x=GOFAB))+
geom_histogram(data=dists,aes(x=GOFAB,fill=genmodel,y=..density..),
alpha=0.3,binwidth = 0.05,
position = "identity")+
# geom_density_ridges(data=dists,aes(x=GOFAB,fill=genmodel,
# y = genmodel, height = stat(density)),
# stat = "binline", bins = 200, #scale = 0.95,
# scale = 50,alpha=0.4,
# draw_baseline = FALSE
# )+
# geom_density(aes(group=genmodel),size=1,color="black",adjust=0.5,
# bw = "nrd",
# kernel = "gaussian",
# n = 1024)+
scale_color_manual(name="Generating model",values = colorexpression[[i]],
labels=modelnames[[i]])+
scale_fill_manual(name = "Generating model",values = colorexpression[[i]],
labels=modelnames[[i]])+
geom_vline(size=0.5,color="black",xintercept=0)+
geom_vline(size=0.5,linetype="dashed",color="black",xintercept=maxmean$minimum)+
coord_cartesian(xlim=c(-5,10),ylim=c(0,1)) +
annotate("text",label=paste0("Recover: ",modelnames[[i]][[1]]),x=-3,y=0.9)+
annotate("text",label=paste0("Recover: ",modelnames[[i]][[2]]),x=5,y=0.9)+
scale_x_continuous(name = nameexpression[i],breaks=sort(c(seq(-5,10,5),maxmean$minimum)),
labels=xlabels)+
theme(axis.text.x = element_text(size=12),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
#legend.title = element_blank(),
panel.background = element_rect(fill = "white"),
legend.position = c(0.7,0.5),
panel.border = element_rect(colour = "black", fill = "transparent"),
strip.background = element_rect(fill = "white"),
strip.placement = "outside",
strip.text = element_text(size=12))
B <- ggplot(tablegraph,aes(y = genmodel,x = model,fill=colorunder)) +
geom_tile(color="white") +
scale_fill_manual(values=c("#F0F0F0","white"))+
geom_text(aes(label=ifelse(value > abs(0.0005), ifelse(genmodel=="Accuracy" | abs(value)<.005,
stringr::str_remove(round(value,3), "^0+"),
stringr::str_remove(round(value,2), "^0+")),"0")),size=4)+
scale_x_discrete(position = "top",name="Recovered",labels=modelnames[[i]])+
scale_y_discrete(position = "left",
name="Generating")+
facet_grid(.~penalty)+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title.y = element_text(hjust=0.9),
axis.ticks = element_blank(),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = "transparent",colour="black"),
strip.placement = "outside",
legend.position = "none",
strip.background = element_rect(fill = "transparent",
colour="transparent"),
strip.text = element_text(size = 12))
plot_grid(A,B,nrow=2,rel_heights=c(0.6,0.4))
}
pwplots <- plot_grid(makeplotGOF(2),
makeplotGOF(1),
makeplotGOF(3),labels=c("A","B","C"),scale=.9,nrow=1,
label_x = 0, label_y = 1)
# Make tablegraph for 3-model situation
# First: estimate penalties like in two-model case
maxacc <-readRDS(paste0("SimulationFits/optimizeconfusionmatrix_meanacc_LN.rds"))
optthresh <- maxacc %>% dplyr::filter(meandiagonal == max(meandiagonal)) %>% dplyr::slice(1)
penaltybased <- custompenalty(testdat,c(optthresh %>% .$UVSDT_pen,
optthresh %>% .$ExGN_pen))
critmaxdiag <- penaltybased %>%
mutate(model = case_when(model == "GumbelEVSDT" ~ "sayA",
model == "ExGaussNormEVSDT" ~ "sayB",
TRUE~"sayC")) %>% ungroup() %>% dplyr::select(-num,-meanval) %>%
rename("value" = winprop) %>%
mutate(genmodel = as.character(genmodel))
meanvalues <- penaltybased %>%
mutate(model = case_when(model == "GumbelEVSDT" ~ "sayA",
model == "ExGaussNormEVSDT" ~ "sayB",
TRUE~"sayC")) %>% ungroup() %>% dplyr::select(-num,-winprop) %>%
rename("value" = meanval) %>%
mutate(genmodel = as.character(genmodel)) %>%
group_by(genmodel,penalty) %>%
mutate(minval = value - min(value))
Accuracy <- penaltybased %>% group_by(penalty) %>%
filter(genmodel==model) %>%
summarize(value = mean(winprop))%>%
mutate(model = "sayA") %>%
mutate(genmodel = "ZAcc")
Penalty <- penaltybased %>% group_by(penalty) %>% dplyr::select(penalty,model) %>% distinct() %>%
mutate(value = case_when(model != "GumbelEVSDT" & penalty == "AIC" ~ 2,
penalty == "objective" ~ 0,
penalty == "penobjective" & model == "GaussianUVSDT" ~ optthresh$UVSDT_pen,
penalty == "penobjective" & model == "ExGaussNormEVSDT" ~ optthresh$ExGN_pen,
TRUE ~ 0))%>%
mutate(model = case_when(model == "GumbelEVSDT" ~ "sayA",
model == "ExGaussNormEVSDT" ~ "sayB",
TRUE~"sayC")) %>%
mutate(genmodel = "YPen")
tablegraph <- bind_rows(critmaxdiag,Accuracy,Penalty) %>%
mutate(penalty = factor(penalty,levels=c("Dev","AIC","penobjective"),
labels=c("-2LL","AIC","Max Acc"))) %>%
# mutate(model = factor(model)) %>%
mutate(genmodel = factor(genmodel,levels=c("ZAcc","YPen","GaussianUVSDT","ExGaussNormEVSDT","GumbelEVSDT"),
labels = c("Accuracy","Penalty","UVSDT","ExGauss","Gumbel"))) %>%
mutate(colorunder = ifelse(!genmodel %in%c("UVSDT","ExGauss","Gumbel"),1,0)) %>%
mutate(colorunder = factor(colorunder))
tablegraph2 <- bind_rows(meanvalues) %>%
mutate(penalty = factor(penalty,levels=c("Dev","AIC","penobjective"),
labels=c("-2LL","AIC","Max Acc"))) %>%
# mutate(model = factor(model)) %>%
mutate(genmodel = factor(genmodel,levels=c("GaussianUVSDT","ExGaussNormEVSDT","GumbelEVSDT"),
labels = c("UVSDT","ExGauss","Gumbel"))) %>%
mutate(colorunder = ifelse(!genmodel %in%c("UVSDT","EGNorm","Gumbel"),1,0)) %>%
mutate(colorunder = factor(colorunder))
Dgraph <- ggplot(tablegraph,aes(y = genmodel,x = model,fill=colorunder)) +
geom_tile(color="white") +
scale_fill_manual(values=c("#E8E8E8","white"))+
geom_text(aes(label=ifelse(genmodel=="Accuracy",
stringr::str_remove(round(value,3), "^0+"),
ifelse(genmodel=="Penalty" | value > .005,
paste(round(value,2)),
ifelse(!genmodel %in% c("Accuracy","Penalty"),
stringr::str_remove(round(value,2), "^0+"),
"0")))),size=4)+
scale_x_discrete(position = "top",name="Recovered",labels=c("Gumbel","ExGauss","UVSDT"))+
scale_y_discrete(position = "left",
name="Generating")+
facet_grid(.~penalty)+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title.y = element_text(hjust=0.75),
axis.ticks = element_blank(),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = "transparent",colour="black"),
strip.placement = "outside",
legend.position = "none",
strip.background = element_rect(fill = "transparent",
colour="transparent"),
strip.text = element_text(size = 12))
Dgraph2 <- ggplot(tablegraph2,aes(y = genmodel,x = model,fill=colorunder)) +
geom_tile(color="white") +
scale_fill_manual(values=c("#E8E8E8","white"))+
geom_text(aes(label = round(minval,2)),size=4)+
scale_x_discrete(position = "top",name=expression(paste("Recovered (",Delta,frac(1,N),Sigma,")")),labels=c("Gumbel","ExGauss","UVSD"))+
scale_y_discrete(position = "left",
name="Generating")+
facet_grid(.~penalty)+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_blank(),
#axis.title.y = element_text(hjust=0.75),
axis.ticks = element_blank(),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = "transparent",colour="black"),
strip.background = element_blank(),
strip.text.x = element_blank(),
strip.placement = "outside",
legend.position = "none")
#Visualize distributions
testdat <- MimicryFits %>%
dplyr::select(genmodel,id,model,Gsq,objective,Dev,AIC) %>%
# dplyr::select(genmodel,condition,id,model,Gsq,objective,Dev,AIC) %>%
# filter(condition == cond) %>% ungroup() %>% dplyr::select(-condition) %>%
unnest(cols=c(Gsq,objective,Dev,AIC))
plotty <- testdat %>% mutate(logobj = log(Gsq)) %>% dplyr::select(-AIC,-objective,-Dev,-Gsq) %>%
mutate(genmodel = factor(genmodel,levels=c("GumbelEVSDT","ExGaussNormEVSDT","GaussianUVSDT"),
labels=c("Gen: Gumbel","Gen: EGNorm","Gen:UVSDT"))) %>%
mutate(model = factor(model,levels=c("GumbelEVSDT","ExGaussNormEVSDT","GaussianUVSDT"),
labels=c("Fit: Gumbel","Fit: EGNorm","Fit: UVSDT")))
library(ggridges)
densitypl <- ggplot(plotty, aes(x = logobj,fill=genmodel,y=genmodel))+
geom_density_ridges(aes(fill = genmodel),scale=10,alpha=0.9,
quantile_lines = TRUE, quantiles = 2)+
scale_x_continuous(name=expression(paste("log(",G^2,")")))+
scale_fill_manual(values=c("#009E73","#56B4E9","#E69F00"),
name= "Generating model",
labels=c("Gumbel","ExGauss","UVSDT"))+
facet_grid(.~model)+
guides(fill = guide_legend(reverse = TRUE)) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = "transparent",colour="black"),
strip.placement = "outside",
legend.position = c(0.075,0.6),
strip.background = element_rect(fill = "transparent",
colour="transparent"),
strip.text = element_text(size = 12))
densitypl2 <- ggplot(plotty, aes(x = logobj,fill=model,y=model))+
geom_density_ridges(aes(fill = model),scale=5,alpha=0.7,
quantile_lines = TRUE, quantiles = 2)+
scale_x_continuous(name=expression(paste("log(",G^2,")")))+
scale_fill_manual(values=c("#009E73","#56B4E9", "#E69F00"), name= "Fitted model",
labels=c("Gumbel","ExGauss","UVSDT"))+
coord_cartesian(xlim=c(-10,40))+
guides(fill = guide_legend(reverse = TRUE)) +
facet_grid(.~genmodel)+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = "transparent",colour="black"),
strip.placement = "outside",
legend.position = c(0.075,0.6),
strip.background = element_rect(fill = "transparent",
colour="transparent"),
strip.text = element_text(size = 12))
plotty <- testdat %>% mutate(logobj = Gsq) %>% dplyr::select(-AIC,-objective,-Dev,-Gsq) %>%
mutate(genmodel = factor(genmodel,levels=c("GumbelEVSDT","ExGaussNormEVSDT","GaussianUVSDT"),
labels=c("Gen: Gumbel","Gen: ExGauss","Gen:UVSDT"))) %>%
mutate(model = factor(model,levels=c("GumbelEVSDT","ExGaussNormEVSDT","GaussianUVSDT"),
labels=c("Fit: Gumbel","Fit: ExGauss","Fit: UVSDT")))
densitypl3 <- ggplot(plotty, aes(x = logobj,fill=model,y=model))+
geom_density_ridges(aes(fill = model),scale=5,alpha=0.7,
quantile_lines = TRUE, quantiles = 2)+
scale_x_continuous(name=expression(paste(G^2)))+
scale_fill_manual(values=c("#56B4E9","#009E73", "#E69F00"), name= "Fitted model",
labels=c("Gumbel","EGNorm","UVSDT"))+
guides(fill = guide_legend(reverse = TRUE)) +
facet_grid(.~genmodel)+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = "transparent",colour="black"),
strip.placement = "outside",
legend.position = c(0.15,0.6),
strip.background = element_rect(fill = "transparent",
colour="transparent"),
strip.text = element_text(size = 12))
Table <- plot_grid(Dgraph,Dgraph2,nrow=2,rel_heights= c(0.6,0.4),labels=c("",""))
threedim <- plot_grid(densitypl2,Table,ncol=2,labels=c("D",""),scale=.9,
label_x = 0.05, label_y = 1)
plot_grid(pwplots,threedim,labels=c("",""),nrow=2,rel_heights=c(1,0.8))
ggsave(paste0("MimicryFull.png"), units="cm", width=35, height=25, dpi=600)
twodi <- plot_grid( makeplotGOF(1),
makeplotGOF(3))
mimicry <- plot_grid(twodi,Dgraph,nrow=2,rel_heights=c(2.2,1))
ggsave(paste0("Figures/Mimicry_LN.png"), units="cm", width=25, height=15, dpi=600)
# Other stufff ----
# knn
# riffing on multi-modelPBCM (MMPBCM) Schultheis & Naidu (2014)
# CogSci proceedings
# https://escholarship.org/content/qt7544w9b0/qt7544w9b0.pdf
# don't use difference GOF but use dimensionality of GOF to find
# use k-nearest-neighbor
# makeTibbleCM <- function(cm) {
# tibble(data.frame(num = c(as.numeric(cm[1,c(1:3)]),
# as.numeric(cm[2,c(1:3)]),
# as.numeric(cm[3,c(1:3)])),
# genmodel = rep(row.names(cm),each=3),
# model = rep(row.names(cm),3)
# ))
#
# }
#
# library(caret)
#
# prep <- testdat %>%
# dplyr::select(-AIC) %>%
# pivot_wider(names_from="model",id_cols=c("genmodel","id"),values_from="objective")
#
#
# # Splitting data into train
# # and test data
#
# datprep <- prep %>% mutate(split = sample(c("TRUE","FALSE"),size=length(dim(prep)[[1]]),replace=T,prob=c(0.7,0.3))) %>%
# mutate(genmodel = factor(genmodel)) %>% ungroup() %>% dplyr::select(-id,-split)
#
# set.seed(300)
# #Spliting data as training and test set. Using createDataPartition() function from caret
# indxTrain <- createDataPartition(y = datprep$genmodel,p = 0.75,list = FALSE)
# training <- datprep[indxTrain,] %>% ungroup()
# testing <- datprep[-indxTrain,] %>% ungroup()
#
# set.seed(400)
# ctrl <- trainControl(method="repeatedcv",repeats = 3) #,classProbs=TRUE,summaryFunction = twoClassSummary)
# knnFit <- train(genmodel ~ ., data = training, method = "knn", trControl = ctrl,
# preProcess = c("center","scale"), tuneLength = 20)
#
# #Output of kNN fit
# knnFit$results
#
# knnPredict <- predict(knnFit,newdata = testing)
# #Get the confusion matrix to see accuracy value and other parameter values
# test <- confusionMatrix( testing$genmodel,knnPredict, dnn = c("Generated", "Recovered"))
# cm <- test$table
#
#
# knnacc <- tibble(value = mean(testing$genmodel==knnPredict)) %>%
#
# mutate(model = "sayA") %>%
# mutate(genmodel = "ZAcc") %>%
# mutate(penalty = "kNN")
#
# knntab <- makeTibbleCM(cm) %>% mutate(penalty = "kNN")
#
# # LDA
#
# library(MASS)
#
# # Estimate preprocessing parameters
# preproc.param <- training %>%
# preProcess(method = c("center", "scale"))
# # Transform the data using the estimated parameters
# train.transformed <- preproc.param %>% predict(training)
# test.transformed <- preproc.param %>% predict(testing)
#
# model <- lda(genmodel~., data =train.transformed)
# predictions <- model %>% predict(test.transformed)
#
# cm <- table(test.transformed$genmodel,predictions$class)
# ldatab <- makeTibbleCM(cm) %>% mutate(penalty = "LDA")
#
# ldaacc <- tibble(value = mean(test.transformed$genmodel==predictions$class)) %>%
#
# mutate(model = "sayA") %>%
# mutate(genmodel = "ZAcc") %>%
# mutate(penalty = "LDA")
#
# # QDA
#
# model <- qda(genmodel~., data =train.transformed)
# predictions <- model %>% predict(test.transformed)
# # Model accuracy
# mean(predictions$class==test.transformed$genmodel)
#
# model
#
# cm <- table(test.transformed$genmodel, predictions$class)
# qdatab <- makeTibbleCM(cm) %>% mutate(penalty = "QDA")
#
# qdaacc <- tibble(value =mean(predictions$class==test.transformed$genmodel)) %>%
#
# mutate(model = "sayA") %>%
# mutate(genmodel = "ZAcc") %>%
# mutate(penalty = "QDA")
#
# # MDA
#
# library(mda)
# model <- mda(genmodel~., data =train.transformed)
# predicted.classes <- model %>% predict(test.transformed)
# # Model accuracy
# mean(predicted.classes == test.transformed$genmodel)
#
# cm <- table(test.transformed$genmodel, predicted.classes)
# mdatab <- makeTibbleCM(cm) %>% mutate(penalty = "MDA")
#
# mdaacc <- tibble(value =mean(predicted.classes == test.transformed$genmodel)) %>%
#
# mutate(model = "sayA") %>%
# mutate(genmodel = "ZAcc") %>%
# mutate(penalty = "MDA")
#
#
# mlmax <- bind_rows(knntab,ldatab,qdatab,mdatab) %>% group_by(penalty,genmodel) %>%
# mutate(value = num/sum(num)) %>% dplyr::select(-num) %>%
# mutate(model = case_when(model == "GumbelEVSDT" ~ "sayA",
# model == "ExGaussNormEVSDT" ~ "sayB",
# TRUE~"sayC"))
#
# mlaccuracy <- bind_rows(knnacc,ldaacc,qdaacc,mdaacc)
#
# tablegraph <- bind_rows(critmaxdiag,Accuracy,Penalty,mlmax,mlaccuracy) %>%
# mutate(penalty = factor(penalty,levels=c("objective","AIC","penobjective","kNN","LDA","QDA","MDA"),
# labels=c("-2LL","AIC","Max Acc","k(=15)NN","LDA","QDA","MDA"))) %>%
# # mutate(model = factor(model)) %>%
# mutate(genmodel = factor(genmodel,levels=c("ZAcc","YPen","GaussianUVSDT","ExGaussNormEVSDT","GumbelEVSDT"),
# labels = c("Accuracy","Penalty","UVSDT","EGNorm","Gumbel"))) %>%
# mutate(colorunder = ifelse(!genmodel %in%c("UVSDT","EGNorm","Gumbel"),1,0)) %>%
# mutate(colorunder = factor(colorunder))
#
# plotty <- testdat %>% mutate(logobj = log(Gsq)) %>% dplyr::select(-AIC,-objective,-Dev,-Gsq) %>%
# mutate(genmodel = factor(genmodel,levels=c("GumbelEVSDT","ExGaussNormEVSDT","GaussianUVSDT"),
# labels=c("Gumbel","EGNorm","UVSDT"))) %>%
# pivot_wider(names_from="model",values_from="logobj") %>%
# relocate(GumbelEVSDT,before=ExGaussNormEVSDT) %>%
# rename("Fit: Gumbel"=GumbelEVSDT,
# "Fit: EGNorm" =ExGaussNormEVSDT,
# "Fit: UVSDT" =GaussianUVSDT)
# library(GGally)
# p1 <- ggpairs(plotty,columns = c(1,2,5),
# mapping = ggplot2::aes(color = genmodel),
# legend = 1,
# upper = list(continuous = "blank"),
# lower = list(continuous = wrap("points", alpha = 0.1),
# combo = wrap("dot", alpha = 0.4)),
# diag = list(continuous = wrap("densityDiag", alpha = 0.3)),
# xlab = expression(paste("log(",G^2,")")),
# ylab = NULL,
# axisLabels = c("show"))
#
# for(i in 1:p1$nrow) {
# for(j in 1:p1$ncol){
# p1[i,j] <- p1[i,j] +
# scale_fill_manual(values=c("#56B4E9","#009E73", "#E69F00"), name= "Generating model") +
# scale_color_manual(values=c("#56B4E9","#009E73", "#E69F00"), name= "Generating model")
# }
# }
#
# plotface <- p1 + theme(legend.position = "bottom",
# panel.background = element_rect(fill = "white"),
#
# panel.border = element_rect(colour = "black", fill = "transparent"),
# strip.background = element_rect(fill = "#F0F0F0",color="black"),
# strip.text = element_text(size=12))
|
466fc846ae7bef5081c22a1d6721642337d58a9a
|
5a1147e95d9e1a88b81c22fe2de9910066045871
|
/Classification in R.R
|
98e2057c9e44704dc7f6292c5b5583f07dd3efec
|
[] |
no_license
|
sikrimanas/Statistics
|
fb7b700afefdfef20ab7d6a203a244eda7a19899
|
9a3b070c1dbdad810a709328e8f8447c107ae0e4
|
refs/heads/master
| 2021-01-12T03:28:28.606005
| 2017-01-06T15:05:33
| 2017-01-06T15:05:33
| 78,214,121
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,390
|
r
|
Classification in R.R
|
# Classification in R ###
# Author : Manas Sikri
# Date: 13-Dec-2016
# install and load the required packages in R
require(ISLR)
# check the column names for stock market dataset
names(Smarket)
View(Smarket) # view the data
# check the summary of the dataset
summary(Smarket)
?Smarket # help
# plot the variable pairs in stock market dataset
pairs(Smarket,col=Smarket$Direction)
# Logistic regression
# glm fit function
# direction variable is the response which has two variables i.e. up and down
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume # function
,data=Smarket # dataset
,family=binomial) # type of family i.e. tells to fit logistic regression model
# summary of the model
summary(glm.fit)
# predict the model
glm.probs=predict(glm.fit,type="response")
glm.probs[1:5]
# setting the thresholds for classification by using ifelse commands
glm.pred=ifelse(glm.probs>0.5,"Up","Down")
attach(Smarket)
# create a table for predicted as well as original values of direction variable
table(glm.pred,Direction)
mean(glm.pred==Direction) # mean of the proportion
# Make training and test set
# create a train data set
train = Year<2005
# fit the data in glm model
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume
,data=Smarket
,family=binomial
, subset=train) # give the input as training
# predict for Snamrket dataset which has rows not in train dataset
glm.probs=predict(glm.fit
,newdata=Smarket[!train,]
,type="response")
# perform the classification
glm.pred=ifelse(glm.probs >0.5,"Up","Down")
# test data
Direction.2005=Smarket$Direction[!train]
# create a tabular form
table(glm.pred,Direction.2005)
mean(glm.pred==Direction.2005)
#Fit smaller model
# we are going to fit lag1 and lag2 and leave other variables
glm.fit=glm(Direction~Lag1+Lag2
,data=Smarket
,family=binomial
,subset=train)
# predict
glm.probs=predict(glm.fit
,newdata=Smarket[!train,]
,type="response")
# classification
glm.pred=ifelse(glm.probs >0.5,"Up","Down")
# table
table(glm.pred,Direction.2005)
# mean
mean(glm.pred==Direction.2005)
106/(76+106)
# Now lets try our hands on Linear Discriminant Analysis
require(MASS)
## Linear Discriminant Analysis
# fit the model with the help of lda function
lda.fit=lda(Direction~Lag1+Lag2
,data=Smarket
,subset=Year<2005)
# check the model
lda.fit
# plot
plot(lda.fit)
# get the test data
Smarket.2005=subset(Smarket,Year==2005)
# predict
lda.pred=predict(lda.fit,Smarket.2005)
lda.pred[1:5,]
# check for the class of lda.pred
class(lda.pred)
# convert it into a dataframe
data.frame(lda.pred)[1:5,]
# check the result
# it will give you a sort of confusion matrix
table(lda.pred$class,Smarket.2005$Direction)
mean(lda.pred$class==Smarket.2005$Direction)
## K-Nearest Neighbors
# load the library
library(class)
# help
?knn
# attach function makes available variables of the dataset by name
attach(Smarket)
# create a matrix for lag1 and lag2
Xlag=cbind(Lag1,Lag2)
# train dataset
train=Year<2005
# model fit and predict with knn method
knn.pred=knn(Xlag[train,],Xlag[!train,],Direction[train],k=1)
# confusion matrix
table(knn.pred,Direction[!train])
# mean
mean(knn.pred==Direction[!train])
|
12e3646bd1907e141b044a2b815065007fc318a5
|
e93cf6300b21ad0e89fcfd5d2ce05029c3247952
|
/astsa/R/Kfilter2.R
|
d3e79e50bb7d2aed35faf7d4c95d23180620bc2f
|
[
"GPL-3.0-only"
] |
permissive
|
wilsonify/TimeSeries
|
51a1b80afa512aad4512471d8031049699b890f1
|
0d67fc256e6a406c560711d448f2db3f66203a23
|
refs/heads/master
| 2023-01-08T17:43:00.863159
| 2023-01-07T16:57:55
| 2023-01-07T16:57:55
| 146,666,517
| 0
| 0
|
MIT
| 2018-09-25T12:50:18
| 2018-08-29T22:38:08
|
Jupyter Notebook
|
UTF-8
|
R
| false
| false
| 2,281
|
r
|
Kfilter2.R
|
Kfilter2 <-
function(num,y,A,mu0,Sigma0,Phi,Ups,Gam,Theta,cQ,cR,S,input){
#
######## Reference Property 6.5 in Section 6.6 ###########
# num is the number of observations
# y is the data matrix (num by q)
# mu0 initial mean is converted to mu1 in code
# Sigma0 initial var is converted to Sigma1 in code
#mu1= E(x_1) = x_1^0 = Phi%*%mu0 + Ups%*%input1
# Sigma1 = var(x_1)= P_1^0 = Phi%*%Sigma0%*%t(Phi)+Theta%*%Q%*%t(Theta)
# input has to be a matrix (num by r) - similar to obs y
# set Ups or Gam or input to 0 if not used
# Must give Cholesky decomp: cQ=chol(Q), cR=chol(R)
Q=t(cQ)%*%cQ
R=t(cR)%*%cR
#
Phi=as.matrix(Phi)
pdim=nrow(Phi)
y=as.matrix(y)
qdim=ncol(y)
rdim=ncol(as.matrix(input))
if (max(abs(Ups))==0) Ups = matrix(0,pdim,rdim)
if (max(abs(Gam))==0) Gam = matrix(0,qdim,rdim)
ut=matrix(input,num,rdim)
xp=array(NA, dim=c(pdim,1,num)) # xp=x_t^{t-1}
Pp=array(NA, dim=c(pdim,pdim,num)) # Pp=P_t^{t-1}
xf=array(NA, dim=c(pdim,1,num)) # xf=x_t^{t}
Pf=array(NA, dim=c(pdim,pdim,num)) # Pf=P_t^{t}
Gain=array(NA, dim=c(pdim,qdim,num))
innov=array(NA, dim=c(qdim,1,num)) # innovations
sig=array(NA, dim=c(qdim,qdim,num)) # innov var-cov matrix
like=0 # -log(likelihood)
xp[,,1]=Phi%*%mu0 + Ups%*%as.matrix(ut[1,],rdim) # mu1
Pp[,,1]=Phi%*%Sigma0%*%t(Phi)+Theta%*%Q%*%t(Theta) #Sigma1
for (i in 1:num){
B = matrix(A[,,i], nrow=qdim, ncol=pdim)
innov[,,i] = y[i,]-B%*%xp[,,i]-Gam%*%as.matrix(ut[i,],rdim)
sigma = B%*%Pp[,,i]%*%t(B)+R
sigma=(t(sigma)+sigma)/2 # make sure sig is symmetric
sig[,,i]=sigma
siginv=solve(sigma)
Gain[,,i]=(Phi%*%Pp[,,i]%*%t(B)+Theta%*%S)%*%siginv
K=as.matrix(Gain[,,i], nrow=qdim, ncol=pdim)
xf[,,i]=xp[,,i]+ Pp[,,i]%*%t(B)%*%siginv%*%innov[,,i]
Pf[,,i]=Pp[,,i] - Pp[,,i]%*%t(B)%*%siginv%*%B%*%Pp[,,i]
sigma=matrix(sigma, nrow=qdim, ncol=qdim)
like= like + log(det(sigma)) + t(innov[,,i])%*%siginv%*%innov[,,i]
if (i==num) break
xp[,,i+1]=Phi%*%xp[,,i] + Ups%*%as.matrix(ut[i+1,],rdim) + K%*%innov[,,i]
Pp[,,i+1]=Phi%*%Pp[,,i]%*%t(Phi)+ Theta%*%Q%*%t(Theta) - K%*%sig[,,i]%*%t(K)
}
like=0.5*like
list(xp=xp,Pp=Pp,xf=xf,Pf=Pf, K=Gain,like=like,innov=innov,sig=sig)
}
|
9742ab83b96fc56463d68afe090585f5e6356de1
|
2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0
|
/fuzzedpackages/bigsparser/tests/testthat/test-SFBM.R
|
226355d4691219d7edf81750199a980498994474
|
[] |
no_license
|
akhikolla/testpackages
|
62ccaeed866e2194652b65e7360987b3b20df7e7
|
01259c3543febc89955ea5b79f3a08d3afe57e95
|
refs/heads/master
| 2023-02-18T03:50:28.288006
| 2021-01-18T13:23:32
| 2021-01-18T13:23:32
| 329,981,898
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,102
|
r
|
test-SFBM.R
|
################################################################################
test_that("can create an SFBM from a dgCMatrix", {
spmat <- Matrix::rsparsematrix(1000, 1000, 0.01)
X <- as_SFBM(spmat)
expect_identical(X$p, spmat@p)
expect_equal(dim(X), spmat@Dim)
expect_equal(X$nval, length(spmat@x))
expect_equal(file.size(X$sbk), length(spmat@x) * 16)
con <- file(X$sbk, open = "rb")
i <- rep(0, length(spmat@x))
x <- rep(0, length(spmat@x))
for (k in seq_along(x)) {
# i[k] <- readBin(con, n = 1, what = integer(), size = 4)
i[k] <- readBin(con, n = 1, what = double())
x[k] <- readBin(con, n = 1, what = double())
}
expect_equal(i, spmat@i)
expect_identical(x, spmat@x)
close(con)
expect_false(X$is_saved)
X <- X$save()
expect_true(X$is_saved)
X2 <- readRDS(X$rds)
expect_equal(X2$p, X$p)
expect_equal(X2$sbk, X$sbk)
})
################################################################################
test_that("can create an SFBM from a dsCMatrix", {
spmat0 <- Matrix::rsparsematrix(1000, 1000, 0.01, symmetric = TRUE)
X <- as_SFBM(spmat0)
spmat <- as(spmat0, "dgCMatrix")
X2 <- as_SFBM(spmat)
expect_identical(readBin(X$sbk, what = 1, n = 1e6),
readBin(X2$sbk, what = 1, n = 1e6))
expect_equal(X$p, spmat@p)
expect_equal(dim(X), spmat@Dim)
expect_equal(X$nval, length(spmat@x))
expect_equal(file.size(X$sbk), length(spmat@x) * 16)
expect_false(X$is_saved)
X <- X$save()
expect_true(X$is_saved)
X2 <- readRDS(X$rds)
expect_equal(X2$p, X$p)
expect_equal(X2$sbk, X$sbk)
})
################################################################################
test_that("products between an SFBM and a vector work", {
spmat <- Matrix::rsparsematrix(1000, 1000, 0.1)
X <- as_SFBM(spmat)
y <- runif(1000)
expect_equal(sp_prodVec(X, y), as.vector(spmat %*% y))
expect_equal(sp_cprodVec(X, y), as.vector(Matrix::crossprod(spmat, y)))
})
################################################################################
|
f8cab0dc128ef74fc9b15b1e21e168201335fcc4
|
25bf83bed09a2121375343998cad3e7fd2f6fc8e
|
/man/hopach2tree.Rd
|
ca57ebf6e4026f82e722e2cfce8963fe4754d55a
|
[] |
no_license
|
keinstein/hopach
|
200f7bdff5dbc51465264a8e274de437f6ca583b
|
9a294568a5153541efd8f78461fcac337448c718
|
refs/heads/master
| 2021-01-12T11:51:07.155403
| 2016-05-03T21:30:44
| 2016-05-03T21:30:44
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,165
|
rd
|
hopach2tree.Rd
|
\name{hopach2tree}
\alias{hopach2tree}
\alias{makeTree}
\title{function to write MapleTree files for viewing hopach hierarchical clustering results}
\description{
The MapleTree software (http://mapletree.sourceforge.net/) is an open source, cross-platform, visualization tool to graphically browse results of cluster analyses. The \code{hopach2tree} function takes a data matrix, plus corresponding \code{hopach} clustering output for genes and/or arrays, and writes the (.cdt, .gtr, and .atr) files needed to view these hierarchical clustering results in MapleTree. The function \code{makeTree} is called internally by \code{hopach2tree}.
}
\usage{
hopach2tree(data, file = "HOPACH", hopach.genes = NULL, hopach.arrays = NULL,
dist.genes = NULL, dist.arrays = NULL, d.genes = "cosangle",
d.arrays = "euclid", gene.wts = NULL, array.wts = NULL, gene.names = NULL)
makeTree(labels, ord, medoids, dist, side = "GENE")
}
\arguments{
\item{data}{data matrix, data frame or exprSet of gene expression measurements. Each column corresponds to an array, and each row corresponds to a gene. All values must be numeric. Missing values are ignored.}
\item{file}{name for the output files (the extensions .cdt, .gtr and .atr will be added).}
\item{hopach.genes}{output of the \code{hopach} function applied to genes (rows of \code{data}. If only arrays are clustered, hopach.genes can be NULL. There must be at least K=2 levels in the hopach final tree (ie: hopach.genes$final$labels greater than 1 digit) for a gtr file to be generated.}
\item{hopach.arrays}{optional output of the \code{hopach} function applied to arrays (columns of \code{data}. There must be at least K=2 levels in the hopach final tree (ie: hopach.arrays$final$labels greater than 1 digit) for an atr file to be generated.}
\item{dist.genes}{matrix of pair wise distances between all genes. All values must be numeric, and missing values are not allowed. If NULL, this matrix is computed using the metric specified by \code{d.genes}. Only needed if genes are clustered (hopach.genes!=NULL).}
\item{dist.arrays}{matrix of pair wise distances between all arrays. All values must be numeric, and missing values are not allowed. If NULL, this matrix is computed using the metric specified by \code{d.arrays}. Only needed if arrays are clustered (hopach.arrays!=NULL).}
\item{d.genes}{character string specifying the metric to be used for calculating dissimilarities between genes. The currently available options are "cosangle" (cosine angle or uncentered correlation distance), "abscosangle" (absolute cosine angle or absolute uncentered correlation distance), "euclid" (Euclidean distance), "abseuclid" (absolute Euclidean distance), "cor" (correlation distance), and "abscor" (absolute correlation distance). Advanced users can write their own distance functions and add these to the functions \code{distancematrix()} and \code{distancevector()}.}
\item{d.arrays}{character string specifying the metric to be used for calculating dissimilarities between arrays.}
\item{gene.wts}{an optional vector of numeric weights for the genes.}
\item{array.wts}{an optional vector of numeric weights for the arrays.}
\item{gene.names}{optional vector of names or annotations for the genes, which can be different from the row names of \code{data}.}
\item{labels}{final cluster labels from a hopach object.}
\item{ord}{final ordering from a hopach object.}
\item{medoids}{final medoids matrix from a hopach object.}
\item{dist}{gene or array distance matrix.}
\item{side}{character string specifying if the tree is for genes ("GENE", default) or arrays ("ARRY").}
}
\value{
The function \code{hopach2tree} has no value. It writes up to three text files to the current working directory. A .cdt file is always produced. This file can be used to visualize the data matrix as a heat map in MapleTree or other viewers such as TreeView (http://rana.lbl.gov/EisenSoftware.htm), jtreeview (http://sourceforge.net/projects/jtreeview/), and GeneXPress (http://genexpress.stanford.edu/). When \code{hopach.genes}!=NULL, a .gtr is produced, and gene clustering results can be viewed, including ordering the genes in the heat map according to the final level of the \code{hopach} tree and drawing the dendogram for hierarchical gene clustering. Similarly, when \code{hopach.arrays}!=NULL, an .atr file is produced and array clustering results can be viewed.
The function \code{makeTree} is called internally by \code{hopach2tree} to make the objects needed to write the MapleTree files for a gene and/or array HOAPCH clustering result.
}
\references{
van der Laan, M.J. and Pollard, K.S. A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap. Journal of Statistical Planning and Inference, 2003, 117, pp. 275-303.
\url{http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/hopach.pdf}
}
\author{Katherine S. Pollard <kpollard@gladstone.ucsf.edu>}
\note{Thank you to Lisa Simirenko <lsimirenko@lbl.gov> for providing HOPACH views in MapleTree, and to Karen Vranizan <vranizan@uclink.berkeley.edu> for her input.
The MapleTree software can be downloaded from: http://sourceforge.net/projects/mapletree/}
\section{Warning }{Operating systems use different end of line characters. These characters can cause errors in MapleTree when files generated on one OS are visualized on another OS. Hence, \code{hopach2tree} should be run on the same OS as MapleTree whenever possible.}
\seealso{\code{\link{hopach}}, \code{\link{boothopach}}, \code{\link{bootmedoids}}, \code{\link{boot2fuzzy}}}
\examples{
#25 variables from two groups with 3 observations per variable
mydata<-rbind(cbind(rnorm(10,0,0.5),rnorm(10,0,0.5),rnorm(10,0,0.5)),cbind(rnorm(15,5,0.5),rnorm(15,5,0.5),rnorm(15,5,0.5)))
dimnames(mydata)<-list(paste("Var",1:25,sep=""),paste("Exp",1:3,sep=""))
mydist<-distancematrix(mydata,d="cosangle") #compute the distance matrix.
#clusters and final tree
clustresult<-hopach(mydata,dmat=mydist)
#write MapleTree files
hopach2tree(mydata,hopach.genes=clustresult,dist.genes=mydist)
}
\keyword{cluster}
\keyword{multivariate}
|
31a08f506e13d9a263e0422f028fe83f875c26d8
|
a32eb8f6c0402f0d1ce059fb9edad3b75cc39904
|
/ratchets/R/team-types.R
|
0e002ec900e67274a447258de3e5c5a7b556ca66
|
[] |
no_license
|
pedmiston/ratchets
|
322591c849aa6429f4946645caccf794cab1b923
|
69973708226633b76b693240ffd52fcfc36c6924
|
refs/heads/master
| 2020-06-17T15:00:28.551616
| 2017-02-17T15:03:44
| 2017-02-17T15:03:44
| 74,994,330
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,643
|
r
|
team-types.R
|
#' Assign team types by median splits on time and submissions.
#'
#' See also \code{\link{recode_team_type}}.
#'
#' @import dplyr
#' @export
divide_into_team_types <- function(leaderboards) {
# Augment team type map to merge based on TimeSplit and SubmissionSplit
team_type_map <- recode_team_type() %>%
mutate(
TimeSplit = c(1, 0, 0, 1),
SubmissionsSplit = c(1, 1, 0, 0)
)
# Remove any team type columns already in leaderboards
for(map_col in colnames(team_type_map)) {
if(map_col %in% colnames(leaderboards)) leaderboards[map_col] <- NULL
}
median_split <- function(x) as.numeric(x < median(x))
leaderboards %>%
mutate(
TimeSplit = median_split(TotalTime),
SubmissionsSplit = median_split(TotalSubmissions)
) %>%
left_join(team_type_map) %>%
select(-TimeSplit, -SubmissionsSplit) # only needed for merge
}
#' Recode team type for labels and contrast coding.
#'
#' If no data is provided, the team type map is returned.
#'
#' @import dplyr
#' @export
recode_team_type <- function(frame) {
team_type_levels <- c("steady", "long", "short", "rapid")
contr_rel_short <- contr.treatment(n = length(team_type_levels), base = 3) %>%
as_data_frame()
names(contr_rel_short) <- c("ShortVSteady", "ShortVLong", "ShortVRapid")
contr_rel_short$TeamType <- team_type_levels
team_type_map <- data_frame(
TeamType = team_type_levels,
TeamLabel = factor(team_type_levels, levels = team_type_levels),
TeamNum = seq_along(team_type_levels)
) %>%
left_join(contr_rel_short)
if (missing(frame)) return(team_type_map)
left_join(frame, team_type_map)
}
|
5a5741d36f206e87d50e6833e18bcfb4ff9cb8ac
|
d3653226fdc95dff7392b7c18e70e799d40b35d7
|
/man/parseGmt.Rd
|
cecde97ade48b6cacf13d82916eb1324c12cd7ea
|
[] |
no_license
|
cran/GiANT
|
6645b847883d0433728af457d17c7751f57f914a
|
c82bd331a5ee768c1d3abe9caf878c11f30c4580
|
refs/heads/master
| 2021-01-21T21:43:11.150637
| 2020-09-29T03:30:02
| 2020-09-29T03:30:02
| 40,326,102
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,033
|
rd
|
parseGmt.Rd
|
\name{parse GMT files}
\alias{parseGmt}
\title{
Venn Euler Diagramm
}
\description{
Parses a GMT file as downloadable from MSigDB (presented in Subramanian et al.) and returns a list of gene sets.
}
\usage{
parseGmt(file)
}
\arguments{
\item{file}{
A file name.
}
}
\details{
Parses a GMT file and returns a list of gene sets. Each list element named according to the included gene set. The gene set files can be downloaded from http://www.broadinstitute.org/gsea/msigdb.
}
\value{
A named \code{list} of gene sets.
}
\references{
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., Mesirov, J. P. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. \emph{Proceedings of the National Academy of Science of the United States of America}, \bold{102}, 15545--15550.
}
\seealso{
\code{\link{geneSetAnalysis}}, \code{\link{predefinedAnalyses}}, \code{\link{gsAnalysis}}
}
|
94fbed515275e5eb48958d3c3f1d84dbab2506e0
|
f8393f4a20b0e1195ad41e8c326ab565ef281195
|
/Heatmap/MODUSHeatmap/MODUSHeatmap_Whitesheet.R
|
1fea9f736e822a5e180e5b21b270ee9256d2f1be
|
[] |
no_license
|
maxgotts/RubensteinMpalaMaps
|
bcb3609df7a30a69d5f726abc84ba0da1a7aae10
|
b862878d6f3d09118ef4c24906d60d85152d23f8
|
refs/heads/master
| 2023-07-17T02:38:58.306930
| 2021-09-05T20:10:38
| 2021-09-05T20:10:38
| 381,844,819
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,115
|
r
|
MODUSHeatmap_Whitesheet.R
|
rm(list=ls())
library(lubridate)
library(dplyr)
## Useful functions
military_to_24 <- function(military) { # military is a number
military_time <- paste0(military)
split_military_time <- strsplit(military_time,"")[[1]]
if (length(split_military_time) == 3) split_military_time <- c("0", split_military_time)
split_hour24_time <- c(split_military_time[1], split_military_time[2], ":", split_military_time[3], split_military_time[4])
if (split_hour24_time[1] == 0) split_hour24_time <- split_hour24_time[2:5]
hour24_time <- paste(split_hour24_time, collapse = "")
return(hour24_time)
}
whitesheets <- read.csv("~/Desktop/MPALA/Whitesheets/ConvertedWhitesheets.csv")
# Create a simplified sheet for a basic heat map
ws <- whitesheets[,c("Date", "Time", "Latitude", "Longitude", "Species", "Multispecies", "Activity", "Grass.height", "Grass.color", "Grass.spp.1", "Grass.spp.2", "Grass.spp.3", "Grass.spp.4", "Bush.type", "Total.animals")]
df <- filter(ws,!is.na(Time), !is.na(Total.animals))
df$Date <- mdy(df$Date)
df$Population <- (4/75)*(df$Total.animals)^(0.75)
df_simple <- df[,c("Latitude","Longitude","Total.animals","Date","Species","Population")]
camel.abbr <- c("Camel","ZC","Comm_Camel")
cattle.abbr <- c("Cattle","CKC","CC","MC")
zebra.abbr <- c("GZ","PZ")
write.csv(filter(df_simple,Species=="GZ"),"~/Desktop/MPALA/Maps/Heatmap/MODUSHeatmap/MODUSHeatmap_Whitesheet_GZ.csv",row.names = FALSE)
write.csv(filter(df_simple,Species=="PZ"),"~/Desktop/MPALA/Maps/Heatmap/MODUSHeatmap/MODUSHeatmap_Whitesheet_PZ.csv",row.names = FALSE)
write.csv(filter(df_simple,Species%in%cattle.abbr),"~/Desktop/MPALA/Maps/Heatmap/MODUSHeatmap/MODUSHeatmap_Whitesheet_Cattle.csv",row.names = FALSE)
write.csv(filter(df_simple,Species%in%zebra.abbr),"~/Desktop/MPALA/Maps/Heatmap/MODUSHeatmap/MODUSHeatmap_Whitesheet_Zebras.csv",row.names = FALSE)
write.csv(filter(df_simple,Species%in%camel.abbr),"~/Desktop/MPALA/Maps/Heatmap/MODUSHeatmap/MODUSHeatmap_Whitesheet_Camels.csv",row.names = FALSE)
if (FALSE) {
source('~/Desktop/MPALA/Maps/Heatmap/MODUSHeatmap/MODUSHeatmap_Whitesheet.R')
}
|
7fdd8f334fadd84c77395ba5576f9db893263d4d
|
6a11db10db1f055e737911611760ff1335b8e554
|
/R/locale.R
|
ea5d5d1e143673aa7cdffa1d273b837ee811a1d7
|
[] |
no_license
|
cran/assertive.reflection
|
947258eb8406a8dd0804a9d0a4c2ca95b25aa9af
|
c7b09e7fafddc9a8b6845abca45bbd05fb95e87b
|
refs/heads/master
| 2021-05-04T11:23:00.367866
| 2020-07-31T00:00:14
| 2020-07-31T00:00:14
| 48,076,808
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,749
|
r
|
locale.R
|
#' Get or set the system locale
#'
#' Wrappers to \code{Sys.getlocale} and \code{Sys.setlocale} for getting and
#' setting the system locale.
#'
#' @param simplify If \code{TRUE}, the locale settings are returned as a
#' character vector, otherwise a list.
#' @param remove_empty_categories if \code{TRUE}, don't include empty categories.
#' @param ... Name-value pairs of locale categories to set.
#' @param l A list, as an alternative method of passing local categories to set.
#' @return A named list or vector giving the system locale names.
#' \code{sys_set_locale} invisibly returns the locale settings *before* making
#' changes (like \code{setwd} and \code{options} do).
#' @examples
#' (current_locale <- sys_get_locale())
#'
#' # Output simplified to character vector
#' sys_get_locale(simplify = TRUE)
#' \dontrun{
#' # Not run since it (temporarily) affects system settings
#' english <- if(is_windows()) "English.United_Kingdom" else
#' if(is_mac()) "en_GB" else
#' if(is_linux()) "en_GB.utf8" else
#' "en"
#' sys_set_locale(LC_MONETARY = english)
#' sys_get_locale()
#' sys_set_locale(l = current_locale) #restore everything
#' }
#' @seealso \code{\link[base]{Sys.getlocale}}.
#' @export
sys_get_locale <- function(simplify = FALSE, remove_empty_categories = TRUE)
{
categories <- c(
"LC_COLLATE", "LC_CTYPE", "LC_MONETARY", "LC_NUMERIC",
"LC_TIME", "LC_MESSAGES", "LC_PAPER", "LC_MEASUREMENT"
)
names(categories) <- categories
locale <- lapply(categories, Sys.getlocale)
if(remove_empty_categories)
{
locale <- locale[nzchar(locale)]
}
if(simplify)
{
unlist(locale)
} else
{
locale
}
}
#' @rdname sys_get_locale
#' @importFrom assertive.base merge_dots_with_list
#' @export
sys_set_locale <- function(..., l = list())
{
old_locale <- sys_get_locale()
values <- merge_dots_with_list(..., l = l)
categories <- names(values)
categories <- match.arg(
categories,
locale_categories(),
several.ok = TRUE
)
for(i in seq_along(values))
{
Sys.setlocale(categories[i], values[[i]])
}
invisible(old_locale)
}
#' Allowed locale categories.
#'
#' The categories of locale that can be gotten/set.
#'
#' @param include_all If \code{TRUE}, the value \code{LC_ALL} is included.
#' @param include_unix If \code{TRUE}, the extra unix-only values are included.
#' @return A character vector of locale categories.
#' @seealso \code{\link{sys_get_locale}}.
#' @noRd
locale_categories <- function(include_all = TRUE, include_unix = is_unix())
{
allowed_categories <- c(
if(include_all) "ALL",
"COLLATE", "CTYPE", "MONETARY", "NUMERIC", "TIME",
if(include_unix) c("MESSAGES", "PAPER", "MEASUREMENT")
)
paste0("LC_", allowed_categories)
}
|
85d78bbc0beca5d60040528443a2df5d22365bd0
|
97dd874e245ef075bb2d573d27023ce2b5a1b9c3
|
/man/gains_chart.Rd
|
bd6c4f5865d2764464a1c92de18a9255891f03b9
|
[] |
no_license
|
ISS-Analytics/gainslift
|
af0900ac358700a36dd15270c567576de9e6f605
|
8597fefcedefd71bd30357b1aa090ba28e752fce
|
refs/heads/master
| 2020-05-24T10:47:14.696216
| 2019-06-03T15:32:48
| 2019-06-03T15:32:48
| 176,554,571
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 307
|
rd
|
gains_chart.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotSettings.R
\name{gains_chart}
\alias{gains_chart}
\title{plotFunctions
Functions to plot identified charts}
\usage{
gains_chart(data, title, xlabel, ylabel)
}
\description{
plotFunctions
Functions to plot identified charts
}
|
79ecf9989bfffaa7d24e39b2fc89b36be0aeda25
|
227ea96bda4039edae16a8765cf45dd5db21b475
|
/202-multivariet-regression.R
|
06f7f13b2438ddfc75a1a09062233b7d674b18c3
|
[] |
no_license
|
data-stache/covid-tracking
|
4764883a0085e075120c39cadb1a5a2023985af5
|
5d3f6e74a5e1356a0afbb758529f2023be803ff0
|
refs/heads/main
| 2023-04-01T11:23:52.328619
| 2021-04-02T14:39:52
| 2021-04-02T14:39:52
| 303,205,157
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,402
|
r
|
202-multivariet-regression.R
|
load("rda/mobility.rda")
load("rda/weather.rda")
load("rda/covid.rda")
# REGRESSION DF
dat <- covid %>%
select(date, state, state_name, new_cases_percap) %>%
inner_join(mobility) %>%
select(1:10) %>%
inner_join(weather_usa) %>%
select(1:11) %>%
group_by(state) %>%
mutate(cases = rollapply(new_cases_percap, width = 7, FUN=function(x) mean(x, na.rm=TRUE), by=1, by.column=TRUE, partial=TRUE, fill=NA, align="left"),
ret = rollapply(retail_recreation, width = 7, FUN=function(x) mean(x, na.rm=TRUE), by=1, by.column=TRUE, partial=TRUE, fill=NA, align="right"),
groc = rollapply(grocery_pharmacy, width = 7, FUN=function(x) mean(x, na.rm=TRUE), by=1, by.column=TRUE, partial=TRUE, fill=NA, align="right"),
parks = rollapply(parks, width = 7, FUN=function(x) mean(x, na.rm=TRUE), by=1, by.column=TRUE, partial=TRUE, fill=NA, align="right"),
trans = rollapply(transit, width = 7, FUN=function(x) mean(x, na.rm=TRUE), by=1, by.column=TRUE, partial=TRUE, fill=NA, align="right"),
work = rollapply(workplace, width = 7, FUN=function(x) mean(x, na.rm=TRUE), by=1, by.column=TRUE, partial=TRUE, fill=NA, align="right"),
res = rollapply(residential, width = 7, FUN=function(x) mean(x, na.rm=TRUE), by=1, by.column=TRUE, partial=TRUE, fill=NA, align="right"),
temp = rollapply(tavg, width = 7, FUN=function(x) mean(x, na.rm=TRUE), by=1, by.column=TRUE, partial=TRUE, fill=NA, align="right"))
cor <- dat %>%
filter(!is.na(parks) & !is.na(trans) & date >= ymd(20200401)) %>%
summarise(cor_ret = cor(cases, ret),
cor_groc = cor(cases, groc),
cor_parks = cor(cases, parks),
cor_trans = cor(cases, trans),
cor_work = cor(cases, work),
cor_res = cor(cases, res),
cor_temp = cor(cases, temp)) %>%
summarise(cor_ret = mean(cor_ret),
cor_groc = mean(cor_groc),
cor_parks = mean(cor_parks),
cor_trans = mean(cor_trans),
cor_work = mean(cor_work),
cor_res = mean(cor_res),
cor_temp = mean(cor_temp, na.rm = TRUE))
cor
dat %>%
filter(date >= ymd(20200301)) %>%
ggplot(aes(x = temp, y = cases)) +
geom_point(alpha = .5) +
geom_smooth()
dat %>%
filter(date >= ymd(20200401)) %>%
ggplot(aes(x = trans, y = cases)) +
geom_point(alpha = .5) +
geom_smooth() +
facet_wrap(. ~ state)
|
d318538dc5ac7349156695ced19e4fa500252505
|
04ab1edfb4b08adabbf89eb8011b78c35c6f4e0f
|
/scripts/08_getAndParseMiscFeatures.R
|
23a3719fa24c5f54c51d78d65b5c63c8bec993ec
|
[] |
no_license
|
alanlorenzetti/tlr_v2
|
b4bc9d7c00580dd08015273957e919a35cc9b4df
|
ea4c3e2701cbd5e6357fac83b4f764e6b7969d30
|
refs/heads/master
| 2023-05-27T11:57:20.296063
| 2021-06-08T22:17:02
| 2021-06-08T22:17:02
| 290,974,388
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,533
|
r
|
08_getAndParseMiscFeatures.R
|
# alorenzetti 202008
# description ####
# this script will get/load
# a few additional misc features
# LSm binding sites
# UTR info
# GC content
# processing starts ####
# getting and parsing LSm binding info
# here I will load the table, remove the asRNAs
# and lookup into genes and 5UTRs
# if LSm binding sites are detected
# in genes or 5UTRs, sense or antisense,
# that will be taken into consideration
lsmGenes = read_tsv(file = "data/interactionListNRTX.tsv") %>%
select(name = representative,
lsmSense = LSmInteraction,
lsmAntiSense = LSmInteractionAS) %>%
mutate(lsmSense = case_when(lsmSense == "Sim" ~ "yes",
TRUE ~ "no"),
lsmAntiSense = case_when(lsmAntiSense == "Sim" ~ "yes",
TRUE ~ "no"))
# # getting and parsing info about UTRs
# utr = read_delim(file = "data/5UTR.txt",
# delim = "\t",
# col_names = T) %>%
# dplyr::select(-length)
#
# # getting and parsing info about UTR mfe
# utrmfe = read_delim(file = "data/5UTRplus18ntdownstream_mfe.txt",
# delim = "\t",
# col_names = T) %>%
# dplyr::select(-seq,-dotbracket)
# getting GC content info for each gene
GCcontent = tibble(locus_tag = names(nrtxseqs) %>%
sub("\\|.*$", "", .),
GC = nrtxseqs %>%
letterFrequency(letters = "GC", as.prob = T) %>%
as.numeric(),
GCdev = GC - mean(GC))
|
2871528610f4ef2a4f92348877b3311f09eb7085
|
04b81a5bafa79294872b828bc9760065963a5f27
|
/preprocess_nna/add_labels.R
|
a7440c1ea989a9e0c92d2624a421884492208d44
|
[
"CC0-1.0"
] |
permissive
|
MinCiencia/ECQQ
|
34088ab1cb625f01f5f62ea6594e07667fcf46f7
|
f93a01ce2dd140d073bd81afb9b4733c1d8a34c3
|
refs/heads/main
| 2023-08-15T06:46:05.025570
| 2021-10-05T13:23:09
| 2021-10-05T13:23:09
| 413,440,130
| 4
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,177
|
r
|
add_labels.R
|
library(tidyverse)
library(ggplot2)
library(readxl)
# obtenemos datos mesa
var_diags <- read_excel("input/nna_resulta.xlsx",
sheet = "base") %>% select(ORDEN,everything()) %>%
janitor::clean_names()
var_diags$organizacion <- tolower(var_diags$organizacion)
sum(is.na(var_diags$organizacion))
var_diags$rango_edades_id[var_diags$rango_edades_id==1] <- "4 a 5"
var_diags$rango_edades_id[var_diags$rango_edades_id==2] <- "6 a 9"
var_diags$rango_edades_id[var_diags$rango_edades_id==3] <- "10 a 13"
var_diags$rango_edades_id[var_diags$rango_edades_id==4] <- "14 a 18"
var_diags$rango_edades_id[var_diags$rango_edades_id==5] <- "Todos"
var_diags$contexto_id[var_diags$contexto_id==1] <- "Establecimiento Educacional"
var_diags$contexto_id[var_diags$contexto_id==2] <- "Consejo Consultivo"
var_diags$contexto_id[var_diags$contexto_id==3] <- "Oficina de Protección de Derechos"
var_diags$contexto_id[var_diags$contexto_id==4] <- "Oficina Local de la Niñez"
var_diags$contexto_id[var_diags$contexto_id==5] <- "Programa Sename"
var_diags$contexto_id[var_diags$contexto_id==6] <- "Otro"
writexl::write_xlsx(var_diags,"temp/var_diags2_inst.xlsx")
|
5d9d46e49e67a8f35cb7ebb816aa0bcafbdb6a02
|
6d4fe0d45fcb7e7f7bc0238abf09d2b6ff2dc566
|
/man/move_all.Rd
|
d5f6246aa73a479d34608968842184b5579cae8f
|
[] |
no_license
|
anhnguyendepocen/simachine
|
a1f584c8d5df145f76bd54a4dc0f9eaff348fca3
|
853aa22fced505dbf09e51b9cef405ddb1b7cb19
|
refs/heads/master
| 2020-03-29T12:19:53.129901
| 2016-12-07T19:24:14
| 2016-12-07T19:24:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 376
|
rd
|
move_all.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/move.R
\name{move_all}
\alias{move_all}
\title{Move all objects from one environment to another}
\usage{
move_all(from, to)
}
\arguments{
\item{from}{environment to pull objects from.}
\item{to}{environment to write objects to.}
}
\description{
Move all objects from one environment to another
}
|
e84a223660952c21a1aa5fe2fce2659414a5371a
|
4e929f4a92a2533e713b87adb06f773748814126
|
/R/RProjects/HITHATStats/R/mh19.R
|
452bcf0fabd73d02a0dc45632337582f0a32de41
|
[
"LicenseRef-scancode-warranty-disclaimer"
] |
no_license
|
jlthomps/EflowStats
|
f4fe56f17cb675bcc1d618bc838003c2f2e9f81b
|
016c9cb65a2f13a041af3eb87debb4f83793238a
|
refs/heads/master
| 2021-01-01T05:33:44.189671
| 2013-12-04T23:30:21
| 2013-12-04T23:30:21
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,007
|
r
|
mh19.R
|
#' Function to return the MH19 hydrologic indicator statistic for a given data frame
#'
#' This function accepts a data frame that contains columns named "discharge" and "year_val" and
#' calculates the skewness in log of annual maximum flows.
#'
#' @param x data frame containing a "discharge" column containing daily flow values
#' @return mh19 numeric value of the skewness of log of maximum annual flows for the given data frame
#' @export
#' @examples
#' load_data<-paste(system.file(package="HITHATStats"),"/data/obs_data.csv",sep="")
#' x<-read.csv(load_data)
#' mh19(x)
mh19 <- function(x) {
annmax <- aggregate(x$discharge,list(x$wy_val),FUN=max,na.rm=TRUE)
log_disch <- log10(annmax$x)
sumq3 <- sum(log_disch^3)
sumq2 <- sum(log_disch^2)
sumq <- sum(log_disch)
num_years <- length(unique(x$wy_val))
qstd <- sd(log_disch)
mh19 <- ((num_years*num_years*sumq3) - (3*num_years*sumq*sumq2) + (2*sumq*sumq*sumq))/(num_years*(num_years-1)*(num_years-2)*qstd*qstd*qstd)
return(mh19)
}
|
25f9b48f7a54c71100d1e3e45de5c9aa4d63fedd
|
6b6b90966a1db5f00240ace9425c608900de149c
|
/amazon_clustering.r
|
898dbc0168009e013397feef86aad24b410e9832
|
[] |
no_license
|
ambarket/comp597
|
e78716284fd27a8d8ad0bc3d8fa3e235c20a6c5b
|
eddce73de18df6250e1503f18d63b663008cc05f
|
refs/heads/master
| 2021-01-01T20:00:01.395463
| 2015-05-01T08:20:40
| 2015-05-01T08:20:40
| 32,291,850
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,971
|
r
|
amazon_clustering.r
|
rm(list = ls())
setwd("C:\\Users\\ambar_000\\Desktop\\597\\Amazon dataset")
rm(list = ls())
#amazon <- read.csv("Amazon_Instant_Video_no_text2.csv")
amazon_instant_video <- read.csv("Amazon_Instant_Video.csv")
str(amazon)
table(count.fields("Amazon_Instant_Video_no_text2.csv", sep=",", quote=""))
txt <-readLines("output.csv")[which(count.fields("output.csv", quote="", sep=",") == 19)]
load("amazonInstantVideo.rdata")
convertNAsToZero <- function(v) {
ifelse(is.na(v),0,v)
}
ptm <- proc.time()
amazonNoNA = as.data.frame(apply(amazon,2,convertNAsToZero))
proc.time() - ptm
amazonSample <- t(amazonNoNA[sample(1:nrow(amazonNoNA), nrow(amazonNoNA)/10, replace=FALSE),])
calcDistForRowAndAllOthers <- function (row) {
apply(amazonSample, 1, calcDistForTwoRows, row)
}
calcDistForTwoRows <- function(row1,row2) {
intersectionEx1Ex2 <- sum(pmin(row1,row2))
unionEx1Ex2 <- sum(row1) + sum(row2) - intersectionEx1Ex2
intersectionEx1Ex2 / unionEx1Ex2
}
do <- apply (amazonSample,1, calcDistForRowAndAllOthers)
clusterTree <- hclust(as.dist(do),method="ward.D")
plot(clusterTree, labels = NULL, hang = 0.1, check = TRUE,
axes = TRUE, frame.plot = FALSE, ann = TRUE,
main = "Cluster Dendrogram",
sub = NULL, xlab = NULL, ylab = "Height");
amazon$product.productId = NULL
sampled <- amazon[sample(nrow(amazon), 10000), ]
d <- dist(sampled, method = "euclidean")
clusters <- hclust(d, method = "ward.D")
plot(clusters, lebels = NULL, hang = 0.1, check = TRUE, axes = TRUE, frame.plot = FALSE, ann = TRUE, main = "Cluster Dendrogram", sub = NULL, sxlab = NULL, ylab = "Height")
groups <- cutree(clusters, k = 4)
png("clusplot.png")
clusplot(sampled, groups, color=TRUE, shade=TRUE, labels=2, lines=0)
dev.off()
pamCLust <- pam(sampled, 3, metric="euclidean")
png("clusplot.png")
clusplot(sampled, pamCLust$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
dev.off()
|
5a899990bfe1eae94bbcced3cd11eeac2110265a
|
6e4cf749acf3c491bd76e95a462aa97aae0b1a7f
|
/man/merge2nodes.Rd
|
99255511c3872dd507b6990cdbcba3c3546bb31e
|
[] |
no_license
|
cran/gRapfa
|
56c44e6cbad56ab6e13700082615477cc167467d
|
69adad4efb0cfca0b5b312c4aca1ab33b634b5a1
|
refs/heads/master
| 2021-01-02T08:52:35.659438
| 2014-04-10T00:00:00
| 2014-04-10T00:00:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,883
|
rd
|
merge2nodes.Rd
|
\name{merge2nodes}
\alias{merge2nodes}
%\alias{MergeNodes}
\title{
Merge two nodes
}
\description{
Calculates various quantities in connection with merging two nodes in a level of a sample tree.
}
\usage{
merge2nodes(NS, mnode, test = TRUE, get.map = FALSE, doMerge = FALSE)
}
\arguments{
\item{NS}{
\code{NS} is a node by symbol array, the 1st half of the columns are node ids, the 2nd half the edge counts.
When the corresponding edge is absent, the edge id is set to 0.
}
\item{mnode}{
\code{mnode} is a vector of nodes to be merged, specified as vertex ids (rather than names). Required to be of length two.
}
\item{test}{
If \code{test=TRUE}, the deviance and df associated with the merging are returned.
}
\item{get.map}{
If \code{get.map=TRUE}, a map is returned.
}
\item{doMerge}{
If \code{doMerge=TRUE}, \code{NS} returned is the node by symbol array after merging (used in \code{MergeNodes})
}
}
\value{
A list of computed quantities
\item{mmat }{An integer matrix containing the nodes to be merged (the original and the induced).}
\item{map}{A integer vector of length vcount(G)) containing the vertex ids of the vertices after merging}
\item{devtest}{A numeric vector of length two containg the degrees of freedom and deviance associated with the merging}
\item{NS}{A node by symbol array representing the result of the merging}
}
\references{
Ankinakatte, S. and Edwards, D. (2014?) Modelling discrete longitudinal data using acyclic probabilistic finite automata. Submitted to C.S.D.A.
}
\author{
Smitha Ankinakatte and David Edwards
}
%%\note{
%%further notes.
%%}
%\seealso{
%\code{link{apfa2NS}, link{MergeNodes}}, \code{\link{merge.select}}
%}
\examples{
library(gRapfa)
data(Wheeze)
G <- st(Wheeze)
G.c <- contract.last.level(G)
NS <- apfa2NS(G.c)
n2n <- merge2nodes(NS, c(5,3))
}
|
204c5c4403ac29e9d09828ca1d4d6839d6201a53
|
745d585395acad1376d84f8ca1284c13f2db70f0
|
/tests/testthat/test-replace_column.R
|
226987f8a798d7874c79bbf4905f5a9565b8f70a
|
[] |
no_license
|
pik-piam/quitte
|
50e2ddace0b0e2cbfabf8539a0e08efe6bb68a0b
|
4f5330695bd3d0e05d70160c1af64f0e436f89ea
|
refs/heads/master
| 2023-08-20T04:15:16.472271
| 2023-08-09T08:14:32
| 2023-08-09T08:14:32
| 206,053,101
| 0
| 8
| null | 2023-08-09T08:14:34
| 2019-09-03T10:39:07
|
R
|
UTF-8
|
R
| false
| false
| 4,213
|
r
|
test-replace_column.R
|
context('replace_column()')
test_that(
'Test replace_column() results',
{
# ---- simple example with matching old and match column names ----
model_data <- data.frame(
model = c('Model1', '2ndModel', 'Model Three'),
region = c('Region 1', 'Region 2', 'Region 1'),
value = 1:3,
stringsAsFactors = FALSE)
mask <- data.frame(
model = c('Model1', '2ndModel', 'Model Three', 'fourth Model'),
clear_name = paste('Model', 1:4),
stringsAsFactors = FALSE)
expect_equal(
object = replace_column(model_data, mask, model, clear_name),
expected = data.frame(model = paste('Model', 1:3),
region = paste('Region', c(1, 2, 1)),
value = 1:3,
stringsAsFactors = FALSE)
)
# ---- mismatched column names ----
model_data <- data.frame(
model = c('Model1', '2ndModel', 'Model Three', 'fourth Model'),
region = c('Region 1', 'Region 2', 'Region 1', 'Region 2'),
value = 1:4,
stringsAsFactors = FALSE)
mask <- data.frame(
ugly_name = c('Model1', '2ndModel', 'Model Three'),
clear_name = paste('Model', 1:3),
stringsAsFactors = FALSE)
expect_equal(
object = replace_column(model_data, mask, model = ugly_name, clear_name),
expected = data.frame(
model = c(paste('Model', 1:3), NA),
region = paste('Region', c(1, 2, 1, 2)),
value = 1:4,
stringsAsFactors = FALSE)
)
# ---- SE example ----
expect_equal(
object = replace_column_(model_data, mask, 'model', 'ugly_name',
'clear_name'),
expected = data.frame(
model = c(paste('Model', 1:3), NA),
region = paste('Region', c(1, 2, 1, 2)),
value = 1:4,
stringsAsFactors = FALSE)
)
# ---- dropping the extra entries in model ----
expect_equal(
object = replace_column(model_data, mask, model = ugly_name, clear_name,
drop.extra = TRUE),
expected = data.frame(model = paste('Model', 1:3),
region = paste('Region', c(1, 2, 1)),
value = 1:3,
stringsAsFactors = FALSE)
)
# ---- also works on quitte objects ----
quitte <- tibble(model = c('Model1', '2ndModel'),
scenario = 'Scenario',
region = 'Region',
variable = 'Variable',
unit = 'Unit',
period = 2010,
value = 1:2) %>%
as.quitte()
expect_equal(
object = replace_column(quitte, mask, model = ugly_name, clear_name),
expected = tibble(model = paste('Model', 1:2),
scenario = 'Scenario',
region = 'Region',
variable = 'Variable',
unit = 'Unit',
period = 2010,
value = 1:2) %>%
as.quitte()
)
})
test_that(
desc = 'replace_column() warning for ambiguous mask',
code = {
model_data <- data.frame(
model = c('Model1', '2ndModel', 'Model Three'),
region = c('Region 1', 'Region 2', 'Region 3'),
value = 1:3,
stringsAsFactors = FALSE)
mask <- data.frame(
model = c('Model1', '2ndModel', 'Model Three')[c(1, 2, 3, 3, 2)],
clear_name = paste('Model', 1:5),
stringsAsFactors = FALSE)
expect_warning(
object = replace_column(model_data, mask, model, clear_name),
regexp = 'No unambiguous match for.*')
expect_failure(
expect_warning(
object = replace_column(model_data, mask, model, clear_name,
ignore.ambiguous.match = TRUE),
regexp = 'No unambiguous match for.*')
)
mask <- data.frame(
model = c('Model1', '2ndModel', 'Model Three')[c(1, 2, 3)],
clear_name = paste('Model', 1:3),
stringsAsFactors = FALSE)
expect_failure(
expect_warning(
object = replace_column(model_data, mask, model, clear_name),
regexp = 'No unambiguous match for.*')
)
}
)
|
5556e8a8a1d2ca87c0dcd0ee846d1c88d5cdb5da
|
aa684167a345fdd921969c0d66e6ec98166990fb
|
/tests/testthat/test-rogue.R
|
163e40030d7b1aa82f885feccc9923def8c72457
|
[
"MIT"
] |
permissive
|
ashther/ashr.rogue
|
ae6b19bf59cbd0688e61bfbd8c2ef61b5957e4b0
|
adbde55c9963d95aa61f0b97b750371b1b990ae3
|
refs/heads/master
| 2020-04-23T01:03:55.521301
| 2019-08-02T08:47:05
| 2019-08-02T08:47:05
| 170,791,022
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,045
|
r
|
test-rogue.R
|
context("test-Rogue")
test_that("can create a rogue instance", {
expect_silent({rogue <- Rogue$new()})
expect_equal(rogue$proxy, NULL)
expect_equal(rogue$useragent, NULL)
expect_equal(rogue$iter_max, 1)
expect_equal(rogue$is_quite, FALSE)
expect_equal(rogue$is_random, FALSE)
expect_equal(rogue$is_record, FALSE)
proxys <- list(list(ip = '1.1.1.1', port = 1),
list(ip = '2.2.2.2', port = 2),
list(ip = '3.3.3.3', port = 3))
proxys_long <- lapply(letters[1:11], function(x) {
list(ip = x, port = 1)
})
proxys_df <- do.call(
rbind, lapply(proxys, function(x)
data.frame(ip = x$ip, port = x$port, stringsAsFactors = FALSE))
)
proxys_modify <- lapply(proxys, function(x) {
x$times <- 1
x
})
useragents <- c('ua1', 'ua2', 'ua3')
expect_silent({rogue <- Rogue$new(proxys, useragents)})
expect_equal(rogue$proxy, proxys_modify)
expect_equal(rogue$useragent, useragents)
expect_equal(rogue$iter_max, length(proxys))
expect_silent({rogue <- Rogue$new(proxys_long)})
expect_equal(length(rogue$proxy), 11)
expect_equal(rogue$iter_max, 10)
expect_silent({rogue <- Rogue$new(proxys_df)})
expect_equal(rogue$proxy, proxys_modify)
expect_equal(rogue$iter_max, length(proxys))
expect_error(Rogue$new('test'), 'proxy must be list or data.frame')
expect_error(Rogue$new(list(list(port = 1))), 'proxy must have ip and port at least')
expect_error(Rogue$new(list(list(ip = '1'))), 'proxy must have ip and port at least')
expect_error(Rogue$new(list(list(ip = 1, port = 1))), 'ip must be character, not double')
expect_error(Rogue$new(list(list(ip = '1', port = '1'))), 'port must be integer or numeric, not character')
temp <- list(list(ip = '1', port = 1, times = 1))
expect_silent({rogue <- Rogue$new(temp)})
expect_equal(rogue$proxy, temp)
expect_error(Rogue$new(list(list(ip = '1', port = 1, times = '1'))), 'times must be integer or numeric, not character')
temp <- replicate(2, list(ip = '1', port = 1), FALSE)
expect_silent({rogue <- Rogue$new(temp)})
expect_equal(rogue$proxy, list(list(ip = '1', port = 1, times = 1)))
expect_equal(rogue$iter_max, 1)
})
test_that("can add and show proxy", {
rogue <- Rogue$new()
temp <- rogue$.__enclos_env__$private$proxySetdiff(list(), list(1))
expect_equal(temp, list())
temp <- rogue$.__enclos_env__$private$proxySetdiff(list(1), list())
expect_equal(temp, list(1))
temp <- rogue$.__enclos_env__$private$proxySetdiff(
list(list(ip = 1, port = 1), list(ip = 2, port = 2)),
list(list(ip = 2, port = 2))
)
expect_equal(temp, list(list(ip = 1, port = 1)))
temp <- rogue$.__enclos_env__$private$proxySetdiff(
list(list(ip = 1, port = 1), list(ip = 2, port = 2)),
list(list(ip = 3, port = 3))
)
expect_equal(temp, list(list(ip = 1, port = 1), list(ip = 2, port = 2)))
proxys <- list(list(ip = '1', port = 1),
list(ip = '2', port = 2),
list(ip = '3', port = 3))
proxys_modify <- lapply(proxys, function(x) {
x$times <- 1
x
})
expect_silent(rogue$proxy_add(proxys))
expect_equal(rogue$proxy, proxys_modify)
expect_warning(rogue$proxy_add(list()), 'no new proxy to add')
expect_warning(rogue$proxy_add(proxys), 'no new proxy to add')
expect_silent(rogue$proxy_add(list(list(ip = '4', port = 4))))
expect_equal(length(rogue$proxy), 4)
expect_silent(rogue$proxy_add(list(list(ip = '4', port = 4), list(ip = '5', port = 5))))
expect_equal(length(rogue$proxy), 5)
expect_silent(rogue$proxy_add(list(list(ip = '6', port = 6)), delete = TRUE))
expect_equal(length(rogue$proxy), 5)
rogue$proxy <- lapply(rogue$proxy, function(x){
x$times <- 2
x
})
expect_silent(rogue$proxy_add(list(list(ip = '7', port = 7)), delete = TRUE))
expect_equal(length(rogue$proxy), 6)
expect_silent(rogue$proxy_add(list(list(ip = '8', port = 8), list(ip = '9', port = 9)), delete = TRUE))
expect_equal(length(rogue$proxy), 7)
expect_is(rogue$proxy_show(), 'data.frame')
expect_named(rogue$proxy_show(), c('ip', 'port', 'times'))
rogue <- Rogue$new()
expect_is(rogue$proxy_show(), 'data.frame')
expect_named(rogue$proxy_show(), c('ip', 'port', 'times'))
expect_equal(nrow(rogue$proxy_show()), 0)
})
test_that('can use proxy and useragent', {
rogue <- Rogue$new()
expect_silent({temp <- rogue$get('http://httpbin.org/get')})
expect_equal(status_code(temp), 200)
expect_silent({temp <- rogue$post('http://httpbin.org/post')})
expect_equal(status_code(temp), 200)
expect_silent({temp <- rogue$put('http://httpbin.org/put')})
expect_equal(status_code(temp), 200)
expect_silent({temp <- rogue$delete('http://httpbin.org/delete')})
expect_equal(status_code(temp), 200)
temp <- rogue$get('http://httpbin.org/get', httr::authenticate('u', 'p'))
expect_equal(content(temp)$header$Authorization, 'Basic dTpw')
proxy <- list(list(ip = '121.33.220.158', port = 808))
rogue <- Rogue$new(proxy)
temp <- rogue$get('http://httpbin.org/ip')
expect_true(proxy[[1]]$ip %in% trimws(unlist(strsplit(content(temp)$origin, ','))))
rogue <- Rogue$new(useragent = 'test user')
temp <- rogue$get('http://httpbin.org/user-agent')
expect_equal(content(temp)$`user-agent`, 'test user')
})
test_that('can select proxys', {
proxys <- unname(mapply(function(i, p) {
list(ip = i, port = p)
}, letters[1:6], 1:6, SIMPLIFY = FALSE))
# iter_max samller, random
rogue <- Rogue$new(proxys, is_random = TRUE)
set.seed(1)
selected <- sapply(rogue$.__enclos_env__$private$proxySelect(), `[[`, 'ip')
set.seed(1)
temp <- sapply(sample(proxys, 6), `[[`, 'ip')
expect_equal(selected, temp)
proxys_modify <- lapply(proxys, function(x) {
x$times <- sample.int(100, 1)
x
})
# iter_max samller, not random
rogue <- Rogue$new(proxys_modify, is_random = FALSE)
set.seed(1)
selected <- sapply(rogue$.__enclos_env__$private$proxySelect(), `[[`, 'ip')
expect_true(any(selected != temp))
set.seed(1)
temp <- sapply(
sample(proxys, 6, prob = sapply(proxys_modify, `[[`, 'times')), `[[`, 'ip'
)
expect_equal(selected, temp)
# iter_max larger, random
suppressWarnings({
rogue <- Rogue$new(proxys, is_random = TRUE, iter_max = 10)
})
set.seed(1)
selected <- sapply(rogue$.__enclos_env__$private$proxySelect(), `[[`, 'ip')
expect_equal(length(selected), 10)
set.seed(1)
temp <- sapply(sample(proxys, 10, replace = TRUE), `[[`, 'ip')
expect_equal(selected, temp)
# iter_max larger, not random
suppressWarnings({
rogue <- Rogue$new(proxys_modify, is_random = FALSE, iter_max = 15)
})
set.seed(1)
selected <- sapply(rogue$.__enclos_env__$private$proxySelect(), `[[`, 'ip')
expect_equal(length(selected), 15)
set.seed(1)
temp <- sapply(unlist(append(
replicate(
2, sample(proxys, 6, prob = sapply(proxys_modify, `[[`, 'times')), FALSE
),
list(sample(proxys, 3, prob = sapply(proxys_modify, `[[`, 'times')))
), recursive = FALSE), `[[`, 'ip')
expect_equal(selected, temp)
})
test_that('can confirm good and bad', {
proxys <- unname(mapply(function(i, p) {
list(ip = i, port = p)
}, letters[1:6], 1:6, SIMPLIFY = FALSE))
proxy_good <- list(ip = 'a', port = 1)
proxy_not_exist <- list(ip = 'a', port = 2)
rogue <- Rogue$new(proxys)
expect_silent(rogue$.__enclos_env__$private$proxyGoodConfirm(proxy_good))
expect_equal(sapply(rogue$proxy, `[[`, 'times'), c(2, rep(1, 5)))
rogue$.__enclos_env__$private$proxyGoodConfirm(proxy_not_exist)
expect_equal(sapply(rogue$proxy, `[[`, 'times'), c(2, rep(1, 5)))
rogue$.__enclos_env__$private$proxyGoodConfirm(proxy_good)
proxy_bad <- list(list(ip = 'a', port = 1))
expect_silent(rogue$.__enclos_env__$private$proxyBadConfirm(proxy_bad))
expect_equal(sapply(rogue$proxy, `[[`, 'times'), c(2, rep(1, 5)))
proxy_bad <- list(list(ip = 'a', port = 1), list(ip = 'b', port = 2))
rogue$.__enclos_env__$private$proxyBadConfirm(proxy_bad)
expect_equal(sapply(rogue$proxy, `[[`, 'times'), rep(1, 6))
})
|
90381bd5deb51112ef0fd8f364afa0c90ff663ab
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/StreamMetabolism/examples/window_chron.Rd.R
|
5d0a141386978db6e986810b29bd936e92036e70
|
[] |
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
| 269
|
r
|
window_chron.Rd.R
|
library(StreamMetabolism)
### Name: window_chron
### Title: Time Windows of Diurnal Curves
### Aliases: window_chron
### Keywords: chron
### ** Examples
#with real data
data(DOTemp)
d <- window_chron(DOTemp, "8/18/70", "06:00:00", "8/18/70", "20:15:00")
plot(d)
|
dfc30ed8c89fc9753cbbfb37044503eb3a57c3a3
|
8002f1c25aaf74fde2aa4a162a23906083189f52
|
/tests/testthat/test-FilterGB.R
|
96d78fbe8ab3732166e77d50d09b57c5d21b8607
|
[] |
no_license
|
zijianni/scCB2
|
8d4e657cbd8e121559acbc48936384e286cd9b48
|
5ba289850b4785e6fd71b4a2b4217620ad9ffc4f
|
refs/heads/master
| 2023-04-27T09:44:42.589719
| 2023-04-19T04:14:44
| 2023-04-19T04:14:44
| 191,231,709
| 11
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 578
|
r
|
test-FilterGB.R
|
data(mbrainSub)
test_that("Input data format", {
expect_error(FilterGB(1), "at least two dimensions")
expect_error(FilterGB("1"), "at least two dimensions")
})
test_that("Gene filtering threshold", {
for(k in sample(0:100,5)){
dat_temp <- FilterGB(mbrainSub,g_threshold = k)
expect_true(all(Matrix::rowSums(dat_temp) > k))
}
})
test_that("Barcode filtering threshold", {
for(k in sample(0:100,5)){
dat_temp <- FilterGB(mbrainSub,b_threshold = k)
expect_true(all(Matrix::colSums(dat_temp) > k))
}
})
|
5df7e535dce1b153eb5d29968758d6a282938560
|
b220e9b4c9db0a1590358b22480ea00beb3fba0f
|
/R/Region_TwoSampleTTest.R
|
6a36f2c32d2b88d4e066011b9d428d1c7720a4de
|
[] |
no_license
|
catfishy/jagust
|
9741a74812c15c4c812b7143404ae1092e76e4d7
|
8877d29b340d6489fbb20542dc5d1188ec512531
|
refs/heads/master
| 2021-04-19T01:21:36.722907
| 2016-11-09T22:54:44
| 2016-11-09T22:54:44
| 37,677,259
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,485
|
r
|
Region_TwoSampleTTest.R
|
input_file = '/usr/local/jagust/ADNI_BL_AV45Neg_Normals.csv'
df = read.csv(input_file)
slope.thresh = 0.004
# get the accumulators
# df = df[complete.cases(df$AV45_NONTP_BigRef_Slope),]
# df$accum = factor(df$AV45_NONTP_BigRef_Slope > slope.thresh)
df = df[complete.cases(df$AV45_NONTP_BigRef_Slope_3pts),]
df$accum = factor(df$AV45_NONTP_BigRef_Slope_3pts > slope.thresh)
# output file name
# out.name = '/usr/local/jagust/AV45_NegNormal_accumAllgroup_region_anova.csv'
out.name = '/usr/local/jagust/AV45_NegNormal_accum3ptgroup_region_anova.csv'
demog = c('RID','Gender','accum','APOE4_BIN','Age.AV45','AV45_NONTP_BigRef_Slope','AV45_NONTP_BigRef_Slope_3pts')
# regions = c('CTX_LH_BANKSSTS','CTX_LH_CAUDALANTERIORCINGULATE','CTX_LH_CAUDALMIDDLEFRONTAL','CTX_LH_CUNEUS','CTX_LH_ENTORHINAL','CTX_LH_FRONTALPOLE','CTX_LH_FUSIFORM','CTX_LH_INFERIORPARIETAL','CTX_LH_INFERIORTEMPORAL','CTX_LH_INSULA','CTX_LH_ISTHMUSCINGULATE','CTX_LH_LATERALOCCIPITAL','CTX_LH_LATERALORBITOFRONTAL','CTX_LH_LINGUAL','CTX_LH_MEDIALORBITOFRONTAL','CTX_LH_MIDDLETEMPORAL','CTX_LH_PARACENTRAL','CTX_LH_PARAHIPPOCAMPAL','CTX_LH_PARSOPERCULARIS','CTX_LH_PARSORBITALIS','CTX_LH_PARSTRIANGULARIS','CTX_LH_PERICALCARINE','CTX_LH_POSTCENTRAL','CTX_LH_POSTERIORCINGULATE','CTX_LH_PRECENTRAL','CTX_LH_PRECUNEUS','CTX_LH_ROSTRALANTERIORCINGULATE','CTX_LH_ROSTRALMIDDLEFRONTAL','CTX_LH_SUPERIORFRONTAL','CTX_LH_SUPERIORPARIETAL','CTX_LH_SUPERIORTEMPORAL','CTX_LH_SUPRAMARGINAL','CTX_LH_TEMPORALPOLE','CTX_LH_TRANSVERSETEMPORAL','CTX_RH_BANKSSTS','CTX_RH_CAUDALANTERIORCINGULATE','CTX_RH_CAUDALMIDDLEFRONTAL','CTX_RH_CUNEUS','CTX_RH_ENTORHINAL','CTX_RH_FRONTALPOLE','CTX_RH_FUSIFORM','CTX_RH_INFERIORPARIETAL','CTX_RH_INFERIORTEMPORAL','CTX_RH_INSULA','CTX_RH_ISTHMUSCINGULATE','CTX_RH_LATERALOCCIPITAL','CTX_RH_LATERALORBITOFRONTAL','CTX_RH_LINGUAL','CTX_RH_MEDIALORBITOFRONTAL','CTX_RH_MIDDLETEMPORAL','CTX_RH_PARACENTRAL','CTX_RH_PARAHIPPOCAMPAL','CTX_RH_PARSOPERCULARIS','CTX_RH_PARSORBITALIS','CTX_RH_PARSTRIANGULARIS','CTX_RH_PERICALCARINE','CTX_RH_POSTCENTRAL','CTX_RH_POSTERIORCINGULATE','CTX_RH_PRECENTRAL','CTX_RH_PRECUNEUS','CTX_RH_ROSTRALANTERIORCINGULATE','CTX_RH_ROSTRALMIDDLEFRONTAL','CTX_RH_SUPERIORFRONTAL','CTX_RH_SUPERIORPARIETAL','CTX_RH_SUPERIORTEMPORAL','CTX_RH_SUPRAMARGINAL','CTX_RH_TEMPORALPOLE','CTX_RH_TRANSVERSETEMPORAL','LEFT_ACCUMBENS_AREA','LEFT_AMYGDALA','LEFT_CAUDATE','LEFT_CHOROID_PLEXUS','LEFT_HIPPOCAMPUS','LEFT_PALLIDUM','LEFT_PUTAMEN','LEFT_THALAMUS_PROPER','LEFT_VENTRALDC','RIGHT_ACCUMBENS_AREA','RIGHT_AMYGDALA','RIGHT_CAUDATE','RIGHT_CHOROID_PLEXUS','RIGHT_HIPPOCAMPUS','RIGHT_PALLIDUM','RIGHT_PUTAMEN','RIGHT_THALAMUS_PROPER','RIGHT_VENTRALDC')
regions = c('CTX_LH_CAUDALMIDDLEFRONTAL', 'CTX_LH_LATERALORBITOFRONTAL', 'CTX_LH_MEDIALORBITOFRONTAL', 'CTX_LH_PARSOPERCULARIS', 'CTX_LH_PARSORBITALIS', 'CTX_LH_PARSTRIANGULARIS', 'CTX_LH_ROSTRALMIDDLEFRONTAL', 'CTX_LH_SUPERIORFRONTAL', 'CTX_LH_FRONTALPOLE', 'CTX_RH_CAUDALMIDDLEFRONTAL', 'CTX_RH_LATERALORBITOFRONTAL', 'CTX_RH_MEDIALORBITOFRONTAL', 'CTX_RH_PARSOPERCULARIS', 'CTX_RH_PARSORBITALIS', 'CTX_RH_PARSTRIANGULARIS', 'CTX_RH_ROSTRALMIDDLEFRONTAL', 'CTX_RH_SUPERIORFRONTAL', 'CTX_RH_FRONTALPOLE', 'CTX_LH_INFERIORPARIETAL', 'CTX_LH_PRECUNEUS', 'CTX_LH_SUPERIORPARIETAL', 'CTX_LH_SUPRAMARGINAL', 'CTX_RH_INFERIORPARIETAL', 'CTX_RH_PRECUNEUS', 'CTX_RH_SUPERIORPARIETAL', 'CTX_RH_SUPRAMARGINAL', 'CTX_LH_MIDDLETEMPORAL', 'CTX_LH_SUPERIORTEMPORAL', 'CTX_RH_MIDDLETEMPORAL', 'CTX_RH_SUPERIORTEMPORAL', 'CTX_LH_CAUDALANTERIORCINGULATE', 'CTX_LH_ISTHMUSCINGULATE', 'CTX_LH_POSTERIORCINGULATE', 'CTX_LH_ROSTRALANTERIORCINGULATE', 'CTX_RH_CAUDALANTERIORCINGULATE', 'CTX_RH_ISTHMUSCINGULATE', 'CTX_RH_POSTERIORCINGULATE', 'CTX_RH_ROSTRALANTERIORCINGULATE')
for (region in regions) {
df[region] = df[region]/df['COMPOSITE_REF']
}
df = df[,c(demog,regions)]
# create factors
df$RID = factor(df$RID)
df$Gender = factor(df$Gender)
df$APOE4_BIN = factor(df$APOE4_BIN)
# Run anova
results = data.frame()
for (region in regions) {
form = paste(region,'~ accum + Gender + APOE4_BIN + Age.AV45')
fm = lm(form,df)
fm.summary = summary(fm)
accum_coef = as.data.frame(fm.summary$coefficients['accumTRUE',])
colnames(accum_coef) = c(region)
results = rbind(results,t(accum_coef))
}
results = results[order(results$`Pr(>|t|)`),]
# bonferroni correct
results$p.bonferroni = p.adjust(results$`Pr(>|t|)`, method = "bonferroni")
results$p.holm = p.adjust(results$`Pr(>|t|)`, method = "holm")
write.csv(results,file=out.name)
|
0db091ca4f18d78c804a7bc356129772052414a5
|
b022e68f0139455784d95133deb4cf8f487142ce
|
/man/traprule.Rd
|
d8d1644596852b35c15f17624792ea47fd8bcce8
|
[] |
no_license
|
cran/REBayes
|
1e311610a28f37509d2da28f81385b17e84b4bbf
|
e26237baf78f2dc4bb776ae29a2ddfce68963435
|
refs/heads/master
| 2022-05-13T04:13:53.603760
| 2022-03-22T17:20:02
| 2022-03-22T17:20:02
| 17,681,954
| 4
| 2
| null | null | null | null |
UTF-8
|
R
| false
| true
| 413
|
rd
|
traprule.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{traprule}
\alias{traprule}
\title{Integration by Trapezoidal Rule}
\usage{
traprule(x, y)
}
\arguments{
\item{x}{points of evaluation}
\item{y}{function values}
}
\value{
A real number.
}
\description{
Integration by Trapezoidal Rule
}
\details{
Crude Riemann sum approximation.
}
\author{
R. Koenker
}
\keyword{utility}
|
45132d05f0267c6ccbec4f4c143504d7967737ad
|
3aa5f28626782b046730018b9727f3bcd1ce024b
|
/ch_4__advance_R/33__Lists.R
|
f83cfdc00296b52223588c0b1fd86c9bbc252103
|
[] |
no_license
|
asamadgithub/R__complete
|
1c854d56f4f3add69256c011c8871b911a108886
|
3d3899aa77958e305bc8a523ed4afd946822bf15
|
refs/heads/master
| 2022-11-26T23:19:17.183332
| 2020-08-04T15:36:35
| 2020-08-04T15:36:35
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,248
|
r
|
33__Lists.R
|
# --------------------------------------Import the data and formating date_Time stamps via POSIXct
utl <- read.csv("https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P3-Machine-Utilization.csv")
utl$Utilization <- 1 - utl$Percent.Idle
#?POSIX,?POSIXct,...
utl$POSIX_time <- as.POSIXct(utl$Timestamp, format = "%d/%m/%Y %H:%M")
utl <- utl[,c(5,2,3,4)]
head(utl, n = 6)
colnames(utl)
# --------------------------------------What is a list?
# vector = only data element of same type ( e.g if one is character, it will convert all other to character as well)
# Here I need a list <- name of machine, stats(min,median,max), logical(if efficiny <90%)
head(utl)
tail(utl)
m1 = utl$Machine == "RL1" # subsetting according to machine name
RL1 <- utl[m1,]
tail(RL1)
#max,min,median
is.na(RL1$Utilization)
RL1[!complete.cases(RL1$Utilization),] # means there are some "NA" entries...
minimum_RL1 = min(RL1$Utilization, na.rm = T)
maximum_RL1 = max(RL1$Utilization, na.rm = T)
median_RL1 = median(RL1$Utilization, na.rm = T)
# logical RL1
logical_RL1 <- length(which(RL1$Utilization < 0.90)) > 0
# combine in a list
list_RL1 <- list(RL1,c(minimum_RL1,median_RL1,maximum_RL1),logical_RL1)
list_RL1
# --------------------------------------Naming different components of list
# method-1
list_RL1
names(list_RL1) # its NULL at this time
names(list_RL1) <- c("Machine","Stats","Low_Threshold")
list_RL1
length(list_RL1)
# method-2 ; righ at the begining..
rm(list_RL1)
list_RL1 <- list(Machine = RL1,Stats = c(minimum_RL1,median_RL1,maximum_RL1),Low_Threshold = logical_RL1)
list_RL1
# --------------------------------------Extract components of a list
# method-1: [ ] --> always return a list
names(list_RL1)
list_RL1["Machine"]
list_RL1[1]
list_RL1[2]
typeof(list_RL1[2])
# method-2: [[]] --> return the actual object
list_RL1[[1]]
list_RL1[[2]]
typeof(list_RL1[[2]])
# method-3: $- same as [[]] but prettier
list_RL1$Machine
list_RL1$Stats
typeof(list_RL1$Stats)
# quiz... access the 3rd element of vector of list_RL1$Stats
# method-1
list_RL1[2] # this will not work ofcourse.. buc it return list..
list_RL1[[2]][3]
# method-2
list_RL1$Stats
length(list_RL1$Stats)
list_RL1$Stats[3]
# --------------------------------------Adding/subtracting from list
#rm(list_RL1)
#list_RL1 <- list(Machine = "RL1",Stats = c(minimum_RL1,median_RL1,maximum_RL1),Low_Threshold = logical_RL1)
#list_RL1
# case-1 within in one sub-list
# ----------------------------
# adding/insering
list_RL1
list_RL1$Machine[2] <- "222"
list_RL1$Machine[3] <- "333"
list_RL1$Machine[4] <- "444"
# deleting
list_RL1$Machine
list_RL1$Machine <- list_RL1$Machine[-which(list_RL1$Machine == "222")]
# case-2 in th main list
# ----------------------
# adding/insering
# method-1
# with predefined index number
list_RL1
list_RL1[4] <- "Addition at index 4 in the main list"
# method-2
# assume column is already there and assign the data/values
# add hours of null vallues in current list
list_RL1
head(RL1)
RL1[is.na(RL1$Utilization),"POSIX_time"]
list_RL1$Unknown_Data_Hrs <- RL1[is.na(RL1$Utilization),"POSIX_time"]
list_RL1$Data <- RL1
# method-3 removing main element(vector) in the list
list_RL1 # lets say remove number 4th position
list_RL1[4] <- NULL
list_RL1 # indexing also shifted little bit above...
# --------------------------------------Subsetting a list
# task-1: access 1st or 2nd element of "Unknown_Data_Hrs"
list_RL1$Unknown_Data_Hrs
list_RL1$Unknown_Data_Hrs[1]
#or
list_RL1[[4]][1]
names(list_RL1)
list_RL1[1]
list_RL1[1:2]
list_RL1[c(1,4)] # we also use names in "name-1", "name-2" inside c()
subset_1 <- list_RL1[c(1,4)]
subset_1 # names carry over
##
## double [[]] are desingned only to access the component not for subsetting
## [[1:3]] , i.e. its wrong
# --------------------------------------Plotting
library(ggplot2)
head(utl, n = 3)
#plot_data <- utl[complete.cases(utl),]
#head(plot_data)
obj <- ggplot(data = plot_data)
obj + geom_line(na.rm = T, aes(x = POSIX_time, y = Utilization, color = Machine ), size = 1.2) +
facet_grid(Machine~.) +
geom_hline(yintercept = 0.90, color = "Blue", linetype = 3)
# --------------------------------------add plot to the list
|
c1110ea4ee1b59b259d001d27e85fb1df229a62c
|
60fe479cf2eb4cda139dd758af86af30d31f3006
|
/Forecasting using Covid19 data.R
|
0f23234af35363220bf8a0d4077ee621c6dba7fe
|
[] |
no_license
|
riteshtripathi2010/Rstudio
|
3b1bc14eb59fe3bb5432d024a830a0dfae1ba95c
|
c27226fa0c5a299956bdf7d11aa797dda169f4c1
|
refs/heads/master
| 2021-07-16T18:08:56.262023
| 2020-08-02T11:41:22
| 2020-08-02T11:41:22
| 195,048,572
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,972
|
r
|
Forecasting using Covid19 data.R
|
#Forecasting COVID19 Cases with R, using facebook's prophet library
install.packages("covid19.analytics")
install.packages("ape")
library(covid19.analytics)
library(dplyr)
library(prophet)
library(lubridate)
library(ggplot2)
setwd("/Users/riteshtripathi/Desktop/")
tsc <- read.csv("ts-confirmed.csv", header = TRUE)
#we got a coln called Country, lets select US from it
tsc <- tsc %>% filter(Country.Region == 'US')
#we see the cols from are in rows, lets transponse into colns
tsc <- data.frame(t(tsc))
#dates are: 22-01-2020 and ownwards
#currently date is in row names, we need to make them in coln names
tsc <- cbind(rownames(tsc), data.frame(tsc, row.names = NULL))
#Change coln names
colnames(tsc) = c('Date', 'Confirmed')
#now lets remove first four rows
tsc <- tsc[-c(1:4), ]
#lets change dates
tsc$Date <- ymd(tsc$Date)#didnt work
tsc$Date <- ymd(tsc$Date)
str(tsc)
#it says confirmed in char, lets convert that
tsc$Confirmed <- as.numeric(tsc$Confirmed)
# Plot
qplot(Date, Confirmed, data = tsc,
main = "Covid19 confirmed cases in UK")
ds <- tsc$Date
y <- tsc$Confirmed
df <- data.frame(ds, y)
# Forecasting
m <- prophet(df)
# Prediction
future <- make_future_dataframe(m, periods = 28)#we want to make predcitions for next 28 days
#last date now is June 18 2020
forecast <- predict(m, future)
# Plot forecast
plot(m, forecast)
dyplot.prophet(m, forecast)#making above
prophet_plot_components(m, forecast)
#Forecast Components
prophet_plot_components(m, forecast)
#this shows two plots
#the bottom one is number of confrimed cases on an average at a lower side, Frid and sat numbers are bigger
#data is not realisty, it only tries to approximate reality
#due to lag or reporting,
# Model performance
pred <- forecast$yhat[1:121]#this forecast has a coln called yhat
actual <- m$history$y
plot(actual, pred)
#lets add line
abline(lm(pred~actual), col = 'red')#we dont see underestimation or overestimation
#Summary
summary(lm(pred~actual))
|
d055b728010f59c06a1cf4a66c286073253d3870
|
fb7e9d43adb368b42dc85124a591f5622fc53c7c
|
/nba_stat/nba_stat.R
|
42252f9207bc2f17832fe207df3edb1023929593
|
[] |
no_license
|
expdal3/data-visualisation
|
ba63a999dc2ec3730033aa032eb1327b86e02167
|
ef9cb5fed7f66a8b46d3eb327b4e71f98fb7cb07
|
refs/heads/master
| 2021-01-13T22:00:31.288451
| 2020-04-01T13:10:07
| 2020-04-01T13:10:07
| 242,508,336
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 939
|
r
|
nba_stat.R
|
library(dplyr)
library(plyr)
library(data.table)
## read in csv ad text file
dataset = read.csv("C:\\Users\\uC260543\\Work\\DataSciencePractices\\R\\nba-players-stats\\Seasons_Stats.csv",header=T)
dataset <- data.frame(dataset)
colnames(dataset)
#head(dataset,5)
dataset[is.na(dataset)]<-0 #replace NA with 0
#subset = dataset[dataset$Year >=2000,] #just take rows after 2000
#head(dataset,5)
#summary(subset)
## calculate sum of 3points in one year per player
dset <- aggregate(X3P~Player + Year, data=dataset, FUN=sum)
#head(arrange(dset,desc(Player)), n = 200) #display result
#group_by(dset, Player)
## CREATE 1 and 2 LAG
dset <- as.data.table(dset) #convert to data.table
#dset2 <- dset1[, lag.X3P:=c(0, X3P[-.N]), by=Player]
dset1 <- dset[, lag.X3P:=lag(X3P,1, 0), by=Player]
dset1 <- dset1[, lag2.X3P:=lag(X3P,2, 0), by=Player]
head(arrange(dset1,desc(Player)), n = 100) #display result
|
49ab3ff92aa5849027996440bbdd2835a562eb09
|
a70271ce4777831c477b4272021e5bfa8555f47f
|
/man/restrictions.Rd
|
a54080f40676811c856df655bd1f490cfcad8b74
|
[] |
no_license
|
moquedace/aqp
|
9f016e990a6c522e04afb100dcfa1a81debd046e
|
73f7f6a64edf727f24258341c94eb591fd783173
|
refs/heads/master
| 2023-04-08T18:59:15.038547
| 2021-05-04T20:58:41
| 2021-05-04T20:58:41
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 506
|
rd
|
restrictions.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Class-SoilProfileCollection.R
\docType{methods}
\name{restrictions}
\alias{restrictions}
\alias{restrictions,SoilProfileCollection-method}
\title{Retrieve restriction data from SoilProfileCollection}
\arguments{
\item{object}{a SoilProfileCollection}
}
\description{
Get restriction data from SoilProfileCollection. Result is returned in the same \code{data.frame} class used to initially construct the SoilProfileCollection.
}
|
fe54345772061b093682a43f167d033a07ec9393
|
c98eff6792c2f139086ddce955f6e980788cba7e
|
/code/functions/pres.abs.lag.R
|
1b5dc2e19b3598e93ef7f44e9292394c40865055
|
[] |
no_license
|
efcaguab/ws-allith-habitat-selection
|
82ac41e46e8ce549d39e7c4eb7506a09f887f6f5
|
0fc73a947a450f46ee33bc8bc4858025f457182b
|
refs/heads/master
| 2022-01-23T10:44:53.804290
| 2019-07-10T21:38:24
| 2019-07-10T21:38:24
| 109,056,794
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,927
|
r
|
pres.abs.lag.R
|
# FUNCTION ----------------------------------------------------------------
# Function to calculate the vectors of presence absence
pres.abs.lag <- function (start.date, end.date, sightings, dates){
# Create a data frame with the detections
sight <- data.frame (id = sightings, date = dates) %>%
filter (date >= start.date, date <= end.date) %>%
mutate (id = factor (id)) %>%
arrange (date)
# For each shark we'll start with the first detection only
individuals <- levels (sight$id)
# Cycle trough each shark
presence.absence <-
foreach (i=1:length (individuals), .combine = rbind) %dopar% {
# message(individuals[i])
# Find dates in which the shark was present
dates.present <- sight$date[sight$id == individuals[i]] %>%
as.numeric ()
# Establish all dates in which it was tagged (only dates in which there was monitoring)
dates.tagged <- unique(sight$date)[unique(sight$date) >=
sight$date[match (individuals[i], sight$id)]]
if(length(dates.tagged)>1) {
# Find all possible combinations of dates in which it was tagged
dates.comb <- as.data.frame (t (combn (dates.tagged, 2))) %>%
tbl_df()
names (dates.comb) <- c ("date.1", "date.2")
dates.comb <- mutate (dates.comb, lag = date.2 - date.1, # Find the lag between given dates
# Establish if it was present for in that lag
present = (date.1 %in% dates.present) &
(date.2 %in% dates.present),
date.1 = as.Date(date.1, origin = "1970-01-01"),
date.2 = as.Date(date.2, origin = "1970-01-01"),
id = individuals[i])
return (dates.comb)
} else {
return(NULL)
}
}
return (presence.absence)
}
|
947a540355e12d7c4007dc97affec8f25d0388e1
|
3e508d7cd0798f70f70242a837f99123397fc1c4
|
/tests/sim/20210909-sim/look-at-results.R
|
d16546938a20637bbc09bc722ada03a68a5e70eb
|
[] |
no_license
|
jlivsey/Dvar
|
f0d142df9866a4232947d114891111097c4c5851
|
824cdcc822829d655b905bfeca54a40b82f16e36
|
refs/heads/master
| 2023-07-26T19:17:28.531305
| 2023-07-06T13:55:28
| 2023-07-06T13:55:28
| 237,249,747
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 934
|
r
|
look-at-results.R
|
library(tidyverse, warn.conflicts = FALSE, quietly = TRUE)
simDir <- "~/Github/Dvar/tests/sim/20210909-sim"
nsim <- 1
# ---- Load Results ----
simLevels <- c(0:4, 6:10)
R <- matrix(NA, 7224, length(simLevels))
colnames(R) <- letters[1:length(simLevels)]
for(jj in seq_along(simLevels)){
simNum <- simLevels[jj]
print(simNum)
fileName <- sprintf("results%d.RData", simNum)
print(fileName)
load(file.path(simDir, fileName))
R[, jj] <- coefEsts
colnames(R)[jj] <- paste0("sim", simNum)
}
head(R)
# ---- True Data ----
load(file.path(simDir, "true-table.Rdata")) # Loads array A
Av <- c(A)
# ---- Absolue Error ----
absErr <- abs(R - Av)
par(mfrow = c(1, 2), mar = c(3, 3, 3, .1))
boxplot(absErr, main = "absolute error")
boxplot(absErr, ylim = c(0, 1), main = "zoom in to (0, 1)")
Rsort <- apply(absErr, 2, sort)
hist(R[, "sim0"], breaks = 40, ylim = c(0, 100))
hist(R[, "sim1"], breaks = 40, ylim = c(0, 100))
|
4bd4055383e8fb732f2666cdd59c987f547248cd
|
bf825ce5fcbda03c083caf45b549f4ae350e27af
|
/Projekty/problem sąsiedztwa.R
|
951ebbefeaa39ce7ca0710a062053d5cb8d4b82a
|
[] |
no_license
|
ajandria/AWD
|
a48cbafe3a6e0eb2b6075bda5cf0060582d79ec1
|
0f7f4ef1dc569d2eecb36e2b9d471f1b0770588e
|
refs/heads/master
| 2023-04-06T15:26:32.827834
| 2020-11-26T15:49:08
| 2020-11-26T15:49:08
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,353
|
r
|
problem sąsiedztwa.R
|
# -------------------------------------------------------------------------
#
library(tidyverse)
obliczDystans <- function(df1, df2) {
x1 <- df1$x
y1 <- df1$y
z1 <- df1$z
x2 <- df2$x
y2 <- df2$y
z2 <- df2$z
dystans <- sqrt((x2-x1)^2+(y2-y1)^2+(z2-z1)^2)
return(dystans)
}
set.seed(424242)
# 1. - wylosuj 10k punktów z rozkładu normalnego (0, I).
ilosc_punktow <- 10000
myList <- list()
for (i in 1:ilosc_punktow) {
myList[[i]] <- runif(3, 0, 1)
names(myList[[i]]) <- c('x', 'y', 'z')
}
merged_data_frame <- data.frame(do.call(rbind, myList))
# 2. - Dla n=20 punktów policz mediana+-mad(policz
# stosunek odległości (1, 10, 100, 1000) najbliższego sąsiada do najdalszego sąsiada)
# https://www.calculatorsoup.com/calculators/geometry-solids/distance-two-points.php
# wylosuj 20 punktów z 10k punktów
set.seed(424242)
wybrane20pkt <- round(runif(20, 1, 10000))
lista20pkt <- list()
index <- 1
for (i in wybrane20pkt) {
lista20pkt[[index]] <- myList[[i]]
index = index + 1
}
merged_data_frame_20pkt <- data.frame(do.call(rbind, lista20pkt)) %>%
mutate(id_punktu = (1:20)) %>%
select(id_punktu, everything())
#
library(tidyverse)
obliczDystans <- function(df1, df2) {
x1 <- df1$x
y1 <- df1$y
z1 <- df1$z
x2 <- df2$x
y2 <- df2$y
z2 <- df2$z
dystans <- sqrt((x2-x1)^2+(y2-y1)^2+(z2-z1)^2)
return(dystans)
}
set.seed(424242)
# 1. - wylosuj 10k punktów z rozkładu normalnego (0, I).
ilosc_punktow <- 10000
myList <- list()
for (i in 1:ilosc_punktow) {
myList[[i]] <- runif(3, 0, 1)
names(myList[[i]]) <- c('x', 'y', 'z')
}
merged_data_frame <- data.frame(do.call(rbind, myList))
# 2. - Dla n=20 punktów policz mediana+-mad(policz
# stosunek odległości (1, 10, 100, 1000) najbliższego sąsiada do najdalszego sąsiada)
# https://www.calculatorsoup.com/calculators/geometry-solids/distance-two-points.php
# wylosuj 20 punktów z 10k punktów
set.seed(424242)
wybrane20pkt <- round(runif(20, 1, 10000))
lista20pkt <- list()
index <- 1
for (i in wybrane20pkt) {
lista20pkt[[index]] <- myList[[i]]
index = index + 1
}
merged_data_frame_20pkt <- data.frame(do.call(rbind, lista20pkt)) %>%
mutate(id_punktu = (1:20)) %>%
select(id_punktu, everything())
# Final -------------------------------------------------------------------
library(philentropy)
problem_sasiedztwa <- function(d) {
nazwy_wspolrzednych_dla_punktu <- paste(c("x"), 1:d, sep="")
set.seed(424242)
n = 100
myList <- list()
for (i in 1:n) {
myList[[i]] <- runif(d, 0, 1)
names(myList[[i]]) <- nazwy_wspolrzednych_dla_punktu
}
merged_data_frame <- do.call(rbind, myList)
merged_data_frame <- as.data.frame(merged_data_frame)
rownames(merged_data_frame) <- paste(c("p"), 1:n, sep="")
set.seed(424242)
wybrane20pkt <- round(runif(5, 1, 100))
lista20pkt <- list()
i = 1
for (value in wybrane20pkt) {
lista20pkt[[i]] <- merged_data_frame[value, ]
i = i + 1
}
merged_data_frame_20pkt <- do.call(rbind, lista20pkt)
merged_data_frame_20pkt_setdiff <- setdiff(merged_data_frame, merged_data_frame_20pkt)
macierz_odleglosci <- distance(rbind(merged_data_frame_20pkt, merged_data_frame_20pkt_setdiff),
method = 'euclidean', use.row.names = TRUE)
list_distances <- list()
i2 = 1
for (value in rownames(merged_data_frame_20pkt)) {
wybrane_punkty <- data.frame(macierz_odleglosci) %>%
select(rownames(merged_data_frame_20pkt)[i2])
wybrane_punkty2 <- wybrane_punkty %>%
slice(-i2)
wybrane_punkty2_max <- wybrane_punkty2 %>%
max()
wybrane_punkty2_min <- wybrane_punkty2 %>%
min()
stosunek_odleglosci <- wybrane_punkty2_min/wybrane_punkty2_max
zwracana_tabela <- data.frame(punkt = value,
stosunek_odleglosci = stosunek_odleglosci)
list_distances[[i2]] <- zwracana_tabela
i2 = i2 + 1
}
merged_list_distances <- do.call(rbind, list_distances)
merged_list_distances <- as.data.frame(merged_list_distances)
mad_ps <- mad(merged_list_distances$stosunek_odleglosci)
median_ps <- median(merged_list_distances$stosunek_odleglosci)
return(data.frame(mad_ps, median_ps, median_ps-mad_ps, median_ps+mad_ps))
}
|
dc0681c9d6f438e25f84e380965e9828ed1cd851
|
f9c3eb0584c42c25db3e9f719d03c92d38a607c2
|
/app.R
|
ce0c5e9cae9fcb7fabc234618a72d105a51f5a22
|
[
"MIT"
] |
permissive
|
strojank/loan-vs-savings
|
264c154e15549a7f751ddc9e93c20f48737c3552
|
24edf54d03eb78b35d8d33d36ae0cd4340a9526a
|
refs/heads/master
| 2020-08-04T23:03:37.518693
| 2019-10-23T06:08:17
| 2019-10-23T06:08:17
| 212,307,226
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,022
|
r
|
app.R
|
# Shiny app
library(shiny)
library(plotly)
library(tidyverse)
source('pomozneFunkcije.R')
options(scipen = 999)
library(DT)
# Define UI for application that draws a histogram
ui <- fluidPage(
titlePanel("When to take a loan?"),
# Sidebar with inputs.
sidebarLayout(
sidebarPanel(
helpText(
"This Shiny app can help answer the following question: When in the next few years is the best time to take a loan for an investment? Calculation is based on current funds, yearly savings, interest rates and more."
),
sliderInput(
"leta",
"Loan maturity:",
min = 2,
max = 30,
value = 10
),
numericInput(
"eur",
"Estimated EURIBOR interest rate (%):",
0,
min = -1,
max = 100
),
numericInput(
"bank",
"Bank interest rate (%):",
3,
min = 0,
max = 100
),
numericInput(
"exp",
"Estimated yearly price increase of investment (%):",
4,
min = 0,
max = 100
),
numericInput(
"inv",
"Current investment estimate (€):",
200000,
min = 0,
max = 10000000
),
sliderInput(
"funds",
"Current funds (€):",
value = 10000,
min = 0,
max = 200000,
step = 1000
),
sliderInput(
"savings",
"Average yearly savings (€):",
value = 10000,
min = 0,
max = 100000,
step = 1000
)
),
mainPanel(tabsetPanel(
# Loans and interests
tabPanel(
"Loan and interests",
h4("Funds, loan and interests"),
p(
"The plot shows the cost of total investment separated to funds, loan and the cost of that loan (interests) depending on the year you decide to invest. Hoover on top of the chart to display values"
),
plotlyOutput("loanPlot"),
tags$hr()
),
# Total investment
tabPanel(
"Total investment",
h4("Total investment cost"),
p(
"The plot shows total cost of investment depending on the year you decide to invest."
),
plotlyOutput("investmentPlot")
),
# Payments
tabPanel(
"Payments",
h4("Payments"),
p(
"In the table below you can see how much money you need to borrow, what is the cost of that loan and the value of monthly payment depending on the year you decide to invest."
),
DT::dataTableOutput("loanTable")
),
# Credits
tabPanel(
"Credits",
tags$hr(),
HTML(
"Loan and payments calculations are based on <a href='https://github.com/lcolladotor/mortgage'>Mortage Calculator</a> by <a href='http://bit.ly/LColladoTorres'>L. Collado Torres</a>."
),
tags$hr(),
HTML(
"Powered by <a href='http://www.rstudio.com/shiny/'>Shiny</a> and hosted by <a href='http://www.rstudio.com/'>RStudio</a>."
),
tags$hr(),
HTML(
"Developed by <a href='https://twitter.com/strojanklemen'>Klemen Strojan.</a>"
),
tags$hr(),
HTML(
"Code hosted by <a href='https://github.com/strojank/loan-vs-savings'>GitHub</a>."
),
tags$hr()
)
))
))
# Define server logic required to draw a histogram
server <- function(input, output) {
# loan data calcualtion
investmentData <-
reactive({
cenaInvesticije(
loanDuration = input$leta,
euribor = input$eur,
bankInterest = input$bank,
investmentEstimate = input$inv,
priceIncrease = input$exp / 100,
currentFunds = input$funds,
yearlySavings = input$savings
)
})
# subseting table data, formating
tableData <- reactive({
investmentData() %>%
select(startYear,
loan,
interests,
payment) %>%
datatable() %>%
formatCurrency(
c('loan',
'interests',
'payment'),
currency = "€",
interval = 3,
mark = ".",
dec.mark = ',',
digits = 2
)
})
# subseting main results data
loanData <- reactive({
investmentData() %>%
pivot_longer(cols = -c(startYear, totalInvestment)) %>%
filter(name %in% c('funds', 'loan', 'interests'))
})
# subseting investment plot data
invPlotData <- reactive({
investmentData() %>%
pivot_longer(cols = -startYear) %>%
filter(name %in% c('totalInvestment'))
})
# Funds, loan and interests plot
output$investmentPlot <- renderPlotly({
ggplot(invPlotData(),
aes(
x = startYear,
y = value,
color = name,
fill = name,
label = value
)) +
geom_bar(stat = 'identity') +
labs(x = 'Starting year',
y = 'EUR',
title = '') +
scale_y_continuous(labels = scales::dollar_format(suffix = "", prefix = "€")) +
scale_color_manual(values = c("#000000")) +
scale_fill_manual(values = c("#000000")) +
theme_bw()
})
# investment plot
output$loanPlot <- renderPlotly({
ggplot(
loanData(),
aes(
x = startYear,
y = value,
color = name,
fill = name,
label = totalInvestment
)
) +
geom_bar(stat = 'identity') +
labs(x = 'Starting year',
y = 'EUR') +
scale_y_continuous(labels = scales::dollar_format(suffix = "", prefix = "€")) +
scale_fill_manual(values = c("#740001", "#eeba30", "#ae0001")) +
scale_color_manual(values = c("#000000", "#000000", "#000000")) +
theme_bw()
})
# Payments table
output$loanTable <- DT::renderDataTable({
tableData()
})
}
# Run the application
shinyApp(ui = ui, server = server)
|
9b2b013ea4c30bfac34ef26c666f936ca3702371
|
106a8e26bd103fa96745a9e94f3b2ca40dd33d23
|
/tests/testthat/test-worm_download.R
|
e4c2b37c7d841f897134a1b845f2d4afb632b617
|
[
"MIT"
] |
permissive
|
rikenbit/WormTensor
|
babece93b314999d076694058f59444c73b646bc
|
3b08a92cf4da2f33221aa1c7a122803dee06177b
|
refs/heads/master
| 2023-04-10T08:09:59.990702
| 2022-10-04T07:25:20
| 2022-10-04T07:25:20
| 470,419,278
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,160
|
r
|
test-worm_download.R
|
.dist2mat <- function(x) {
as.matrix(x)
}
############ Euclid ############
## dist object
Ds_Euclid <- worm_download("Euclid")$Ds
expect_true(object.size(Ds_Euclid) != 0)
expect_equal(length(Ds_Euclid), 24)
expect_true(all(lapply(Ds_Euclid, is) == "dist"))
## matrix object
Ms_Euclid <- lapply(Ds_Euclid, .dist2mat)
expect_true(all(unlist(lapply(Ms_Euclid, isSymmetric))))
############# mSBD #############
## dist object
Ds_mSBD <- worm_download("mSBD")$Ds
expect_true(object.size(Ds_mSBD) != 0)
expect_equal(length(Ds_mSBD), 24)
expect_true(all(lapply(Ds_mSBD, is) == "dist"))
## matrix object
Ms_mSBD <- lapply(Ds_mSBD, .dist2mat)
expect_true(all(unlist(lapply(Ms_mSBD, isSymmetric))))
############ WARN ############
Ds_Euclid_warn <- worm_download("Euclid", qc = "WARN")$Ds
expect_true(object.size(Ds_Euclid_warn) != 0)
expect_equal(length(Ds_Euclid_warn), 27)
expect_true(all(lapply(Ds_Euclid_warn, is) == "dist"))
############ FAIL ############
Ds_Euclid_fail <- worm_download("Euclid", qc = "FAIL")$Ds
expect_true(object.size(Ds_Euclid_fail) != 0)
expect_equal(length(Ds_Euclid_fail), 28)
expect_true(all(lapply(Ds_Euclid_fail, is) == "dist"))
|
451dfbb7aae1c7ea3227357e07c54fd91caec39c
|
9f57e0ad44b78d809c262fa0ffb659232fdb8d5e
|
/Art of R Programming Code/Ch2/findud.R
|
4203569aa81ec773044cbe79378b199e915463b5
|
[] |
no_license
|
abhi8893/Intensive-R
|
c3439c177776f63705546c6666960fbc020c47e8
|
e340ad775bf25d5a17435f8ea18300013195e2c7
|
refs/heads/master
| 2020-09-22T00:57:36.118504
| 2020-08-31T09:23:57
| 2020-08-31T09:23:57
| 224,994,015
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 376
|
r
|
findud.R
|
# findud() converts vector v to 1s, 0s, representing an element
# increasing or not, relative to the previous one; output length is 1
# less than input
findud <- function(v) {
vud <- v[-1] - v[-length(v)] _label~findvud1@
return(ifelse(vud > 0,1,-1)) _label~findvud2@
}
udcorr <- function(x,y) {
ud <- lapply(list(x,y),findud)
return(mean(ud[[1]] == ud[[2]]))
}
|
7e8851c91d5c262f02ac127bc0cddc8d5a9e6abf
|
f4fffe026383f8f681c8b2ef2e7b2ec0f8143688
|
/man/brauer_2008.Rd
|
02d4082228ee4148ebcf4ae3b8a674873c13d255
|
[] |
no_license
|
DavisVaughan/romic
|
436da67b077937d1c13af9701d39a00f083e6694
|
f3470f5cd42b6ee8322db3f10a1c254766a5dc3e
|
refs/heads/master
| 2023-05-08T05:44:49.460728
| 2021-05-18T13:10:03
| 2021-05-18T13:10:03
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,486
|
rd
|
brauer_2008.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{brauer_2008}
\alias{brauer_2008}
\alias{brauer_2008_tidy}
\alias{brauer_2008_triple}
\title{Brauer 2008}
\format{
A tibble with 18,000 rows and 8 columns:
\describe{
\item{name}{Common gene name}
\item{BP}{Gene ontology biological process of the gene}
\item{MF}{Gene ontology molecular function of the gene}
\item{sample}{Sample name}
\item{nutrient}{Which nutrient limits growth (Glucose, Nitrogen,
Phosphorous, Sulfur, Uracil, Leucine}
\item{DR}{Dilution rate of the culture - basically how fast the cells
are growing}
\item{expression}{Expression level of the gene, log2 observation relative
to a replicate of G0.3}
}
An object of class \code{tidy_omic} (inherits from \code{tomic}, \code{general}) of length 2.
An object of class \code{triple_omic} (inherits from \code{tomic}, \code{general}) of length 4.
}
\source{
\url{https://pubmed.ncbi.nlm.nih.gov/17959824/}
}
\usage{
brauer_2008
brauer_2008_tidy
brauer_2008_triple
}
\description{
An RNA expression (microarray) dataset looking at how yeast gene expression
changes as nutrient sources and nutrient richness changes.
\code{\link{brauer_2008}} formatted as a tidy_omic object
\code{\link{brauer_2008}} formatted as a triple_omic object
}
\details{
This version of the dataset contains only 500 genes randomly
selected from the ~6K genes in the complete dataset.
}
\keyword{datasets}
|
f4013e94761da12a48533e3114038c8585749d93
|
799f724f939763c26c4c94497b8632bad380e8f3
|
/tests/testthat/test-tokens_ngrams.R
|
833cdf6164089c6d86d660cd019da1c83c71aef2
|
[] |
no_license
|
chmue/quanteda
|
89133a7196b1617f599e5bba57fe1f6e59b5c579
|
aed5cce6778150be790b66c031ac8a40431ec712
|
refs/heads/master
| 2020-12-01T02:59:48.346832
| 2017-11-22T18:35:37
| 2017-11-22T18:35:37
| 50,363,453
| 2
| 0
| null | 2016-01-25T16:18:50
| 2016-01-25T16:18:50
| null |
UTF-8
|
R
| false
| false
| 4,036
|
r
|
test-tokens_ngrams.R
|
library(quanteda)
library(testthat)
context('test ngrams.R')
test_that("test that ngrams produces the results from Guthrie 2006", {
toks <- tokens(c('insurgents killed in ongoing fighting'))
bi_grams <- c('insurgents_killed', 'killed_in', 'in_ongoing',
'ongoing_fighting')
two_skip_bi_grams <- c('insurgents_killed', 'insurgents_in',
'insurgents_ongoing', 'killed_in', 'killed_ongoing', 'killed_fighting',
'in_ongoing', 'in_fighting', 'ongoing_fighting')
tri_grams <- c('insurgents_killed_in', 'killed_in_ongoing',
'in_ongoing_fighting')
two_skip_tri_grams <- c('insurgents_killed_in',
'insurgents_killed_ongoing', 'insurgents_killed_fighting',
'insurgents_in_ongoing', 'insurgents_in_fighting',
'insurgents_ongoing_fighting', 'killed_in_ongoing',
'killed_in_fighting', 'killed_ongoing_fighting',
'in_ongoing_fighting')
expect_equivalent(setdiff(
as.list(tokens_ngrams(toks, n=2, skip=0))[[1]],
bi_grams
), character(0)
)
expect_equivalent(setdiff(
as.list(tokens_ngrams(toks, n=2, skip=0:2))[[1]],
two_skip_bi_grams
), character(0)
)
expect_equivalent(setdiff(
as.list(tokens_ngrams(toks, n=3, skip=0))[[1]],
tri_grams
), character(0)
)
expect_equivalent(setdiff(
as.list(tokens_ngrams(toks, n=3, skip=0:2))[[1]],
two_skip_tri_grams
), character(0)
)
expect_equivalent(setdiff(
as.list(tokens_ngrams(toks, n = 2:3))[[1]],
c(bi_grams, tri_grams)
), character(0)
)
expect_equivalent(setdiff(
as.list(suppressWarnings(tokens_ngrams(toks, n = 2:3)))[[1]],
c(bi_grams, tri_grams)
), character(0)
)
})
test_that("test `tokens_ngrams` on characters", {
ngms <- tokens_ngrams(c('insurgents','killed', 'in', 'ongoing', 'fighting'))
charNgms <- char_ngrams(c('insurgents','killed', 'in', 'ongoing', 'fighting'))
expect_equivalent(
ngms,
c('insurgents_killed', 'killed_in', 'in_ongoing', 'ongoing_fighting')
)
expect_equivalent(
charNgms,
c('insurgents_killed', 'killed_in', 'in_ongoing', 'ongoing_fighting')
)
expect_warning(tokens_ngrams(c('insurgents killed', 'in', 'ongoing', 'fighting')),
"whitespace detected: you may need to run tokens\\(\\) first")
expect_warning(tokens_ngrams(c('insurgents killed', 'in', 'ongoing', 'fighting'), n = 1, skip = 1),
"skip argument ignored for n = 1")
})
test_that("test `tokens_ngrams` on skipgrams", {
toks <- tokens("insurgents killed in ongoing fighting")
ngms <- tokens_skipgrams(toks, n = 2, skip = 0:1, concatenator = " ")
expect_equivalent(
as.list(ngms)[[1]],
c('insurgents killed', "insurgents in", "killed in" , "killed ongoing" ,
"in ongoing", "in fighting", "ongoing fighting")
)
})
# FAILLING (issue #469)
# test_that("test there is not competition between the thread", {
# txt <- c(one = char_tolower("Insurgents killed in ongoing fighting."),
# two = "A B C D E")
# toks <- tokens(txt, remove_punct = TRUE)
#
# for(k in 1:1000) {
# out <- tokens_ngrams(toks, n = 2:3)
# expect_equivalent(as.list(out),
# list(c("insurgents_killed", "killed_in", "in_ongoing", "ongoing_fighting",
# "insurgents_killed_in", "killed_in_ongoing", "in_ongoing_fighting"),
# c("A_B", "B_C", "C_D", "D_E", "A_B_C", "B_C_D", "C_D_E")))
# }
# })
# FAILLING
# test_that("tokens_ngrams(x, n = ...) works when ntokens(x) < n", {
# ## issue #392
# expect_equivalent(unclass(tokens_ngrams(tokens("a"), n = 2))[[1]],
# char_ngrams("a", n = 2))
# })
|
b2a89df694f8d1df3a0c7b01e0e7fc8d5549753a
|
93730e6fee0ae4e3bb028714d6e8a5e335632121
|
/man/search_subset_node.Rd
|
aec08f6a72d9ce66ac774a6b0c740886af2e4fa3
|
[] |
no_license
|
jeevanyue/PIVOT
|
56ef7c2d2eb30a197b1c0387ae79e73605ab7eae
|
46fa6af11f19c320ee338452ccff745aa93a1f6d
|
refs/heads/master
| 2021-01-01T06:30:45.988532
| 2017-07-17T02:51:20
| 2017-07-17T02:51:20
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 352
|
rd
|
search_subset_node.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_map.R
\name{search_subset_node}
\alias{search_subset_node}
\title{PIVOT data management function}
\usage{
search_subset_node(tbl, node)
}
\description{
This function is for determining which sample subset the filter operation is performed on, if not keep filtering.
}
|
e9b8d5de340a9ffd1ffa3dc68f37ef28642e5d30
|
58f007d9b5414c747998bf0bc655ed2428ac06dc
|
/autres_exemples/gh.R
|
f7aeaf21739da5124a778c37b9a1db17dba12ea6
|
[] |
no_license
|
GuillaumePressiat/formation_pmsi_R
|
9e9cfc76532c2e97ae9e4d64850c0b3d959f3f3a
|
78b9db64b79bc8ad179bdd90e60ebcc7db63d335
|
refs/heads/master
| 2020-03-25T03:00:28.155493
| 2018-09-07T13:19:49
| 2018-09-07T13:19:49
| 143,318,013
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,865
|
r
|
gh.R
|
# Faire une base par GH
liste_hop <- c('750100125', '940100035')
library(pmeasyr)
library(dplyr, warn.conflicts = FALSE)
p <- noyau_pmeasyr(
annee = 2018,
mois = 7,
path = '~/Documents/data/mco',
progress = FALSE,
tolower_names = TRUE
)
rsa_gh <- liste_hop %>%
purrr::map(function(i){
p$finess <- i;
adezip(p, type = "out", liste = 'rsa')
irsa(p) %>%
purrr::map(function(table)
{mutate(table, f = i)})})
rsa_gh_fin <- list()
rsa_gh_fin$rsa <- rsa_gh %>% purrr::map('rsa') %>% bind_rows()
rsa_gh_fin$rsa_um <- rsa_gh %>% purrr::map('rsa_um') %>% bind_rows()
rsa_gh_fin$das <- rsa_gh %>% purrr::map('das') %>% bind_rows()
rsa_gh_fin$actes <- rsa_gh %>% purrr::map('actes') %>% bind_rows()
rsa_gh_fin
ano_gh <- liste_hop %>%
purrr::map(function(i){
p$finess <- i;
adezip(p, type = "out", liste = 'ano')
iano_mco(p) %>% mutate( f = i)}) %>%
bind_rows()
# Chainer en intra GH
get_hop_from_gh <- function(table, fin){
table %>% filter(f == fin)
}
rsa_psl <- get_hop_from_gh(rsa_gh_fin$rsa, '750100125')
rsa_cfx <- get_hop_from_gh(rsa_gh_fin$rsa, '940100035')
ano_psl <- get_hop_from_gh(ano_gh, '750100125')
ano_cfx <- get_hop_from_gh(ano_gh, '940100035')
rsa_psl %>%
select(cle_rsa, ghm, duree) %>%
inner_join(ano_psl %>% filter(cok) %>% select(cle_rsa, noanon, nosej), by = 'cle_rsa') %>%
left_join(
rsa_cfx %>%
select(cle_rsa, ghm, duree) %>%
inner_join(ano_cfx %>% filter(cok) %>% select(cle_rsa, noanon, nosej), by = 'cle_rsa'),
by = 'noanon',
suffix = c('_psl', '_cfx')
) %>%
filter(!is.na(ghm_cfx)) %>%
mutate(delta = nosej_cfx - (nosej_psl + duree_psl)) %>%
filter(delta >= 0) %>%
arrange(noanon, delta) %>%
distinct(noanon, .keep_all = TRUE) %>%
filter(delta < 5) %>% # View
count(ghm_psl, ghm_cfx) %>%
View
|
380cc47cc82a1ebe838b4f4f910e74e9dfbd62b2
|
2fa33aeef712fa0a1b8043b40261d218a37cafa2
|
/R/pbino.R
|
8b61c9a1eea0b48736ef9e7afce03156a8636299
|
[] |
no_license
|
cran/bayess
|
778e3cd961acecec0ccbf0de66048543af82c98c
|
30208f8c4b61bc73e5885875b8134f05a963719c
|
refs/heads/master
| 2022-09-03T00:53:47.483683
| 2022-08-11T09:30:08
| 2022-08-11T09:30:08
| 17,694,647
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 190
|
r
|
pbino.R
|
pbino=function(nplus)
{
# POSTERIOR PROBABILITIES OF N FOR THE BINOMIAL CAPTURE MODEL UNDER UNIFORM PRIOR (N_MAX=400)
prob=c(rep(0,max(nplus,1)-1),1/(max(nplus,1):400+1))
prob/sum(prob)
}
|
c45f0c3e1ffce75a5583c42a5b3d5c2ee633d671
|
5a1e2c2f2d8ab9668acc3cbfe61b3251ea8e3fc6
|
/R/split_dat.R
|
81badbba8dc2376855f210930dab93851570fae1
|
[] |
no_license
|
helixcn/phylotools
|
1fc5162f5dc5d1c9483a25f4ac162c5bf27c695b
|
758d338b2d2004c5b94f931802d269251f4fd7a1
|
refs/heads/master
| 2021-06-08T22:55:57.141365
| 2021-03-24T15:55:30
| 2021-03-24T15:55:30
| 35,886,086
| 10
| 11
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,176
|
r
|
split_dat.R
|
#### author: Jinlong Zhang <jinlongzhang01@gmail.com>
#### institution: Kadoorie Farm and Botanic Garden, Hong Kong
#### package: phylotools
#### URL: http://github.com/helixcn/phylotools
#### date: 26 MAY 2015
#### modified: 8 DEC 2017
### Create fasta files based on the groups specified
split_dat <- function(dat, ref_table){
colnames(ref_table) <- c("seq.name", "group")
dat.merged <- merge(dat, ref_table, by = "seq.name", all.x = TRUE)
### save the ungrouped sequences first
group.dat_i <- dat.merged[is.na(dat.merged$group), ][, -3]
dat2fasta(group.dat_i, outfile = "ungrouped.fasta")
### deleted ungrouped sequences from the merged
dat.merged <- na.omit(dat.merged)
group.name <- as.character(unique(dat.merged$group))
### generate fasta files according to the groups
for(i in 1:length(group.name)){
group.name_i <- group.name[i]
group.dat_i <- dat.merged[as.character(dat.merged$group) == group.name_i, ][, -3]
dat2fasta(group.dat_i, outfile = paste(group.name_i, ".fasta", sep = ""))
}
cat(paste("splitted fasta files have been saved to: \n", getwd(),"\n"))
}
|
043c9cfc8c94d1978ae1224447e4e7d5d0eaabd4
|
c8533654476eeec28d52b2dd47f471009a12c593
|
/man/predict.ConsRegArima.Rd
|
4bb1162e7bf516fb90c20b19d36042fb478b287e
|
[] |
no_license
|
puigjos/ConsReg
|
e64d03eca6b6b37d116682e7cdda10da73decb63
|
50b7df1faaccc888036bed3622c4651e669be672
|
refs/heads/master
| 2021-05-20T01:42:51.925681
| 2020-04-03T13:29:22
| 2020-04-03T13:29:22
| 252,132,981
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,357
|
rd
|
predict.ConsRegArima.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict_ConsRegArima.R
\name{predict.ConsRegArima}
\alias{predict.ConsRegArima}
\alias{print.predict.ConsRegArima}
\alias{plot.predict.ConsRegArima}
\title{Predict function for ConsRegArima object}
\usage{
\method{predict}{ConsRegArima}(object, h = ifelse(is.null(newdata), 1,
nrow(newdata)), newdata = NULL, intervals = 90, origdata = NULL,
...)
\method{print}{predict.ConsRegArima}(x, ...)
\method{plot}{predict.ConsRegArima}(x, ...)
}
\arguments{
\item{object}{ConsRegArima object}
\item{h}{horizont to predict}
\item{newdata}{data frame in which to look for variables with which to predict.
In case there is no regression part, this parameter could be set NULL}
\item{intervals}{Confidence level for prediction intervals (default 90)}
\item{origdata}{Original data (default NULL). Useful if lagged predictive
variables are used in the formula}
\item{...}{Additional params passed to the function ggplot2::labs}
\item{x}{object of class predict.ConsRegArima}
}
\value{
Returns an object of class predict.ConsRegArima
\item{predict}{dataframe with the predictions}
\item{table}{dataframe with the predictions as well as the fitted values}
\item{intervals}{Interval level}
\item{object}{original object}
}
\description{
Obtains predictions of ConsRegArima object
}
|
059664936fc9ac4daf462b89ae7ffb9db92d09b6
|
e189d2945876e7b372d3081f4c3b4195cf443982
|
/man/icevision_Flip.Rd
|
61b983dd40b7a6cbd34f8f311607fda990763446
|
[
"Apache-2.0"
] |
permissive
|
Cdk29/fastai
|
1f7a50662ed6204846975395927fce750ff65198
|
974677ad9d63fd4fa642a62583a5ae8b1610947b
|
refs/heads/master
| 2023-04-14T09:00:08.682659
| 2021-04-30T12:18:58
| 2021-04-30T12:18:58
| 324,944,638
| 0
| 1
|
Apache-2.0
| 2021-04-21T08:59:47
| 2020-12-28T07:38:23
| null |
UTF-8
|
R
| false
| true
| 491
|
rd
|
icevision_Flip.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/icevision_albumentations.R
\name{icevision_Flip}
\alias{icevision_Flip}
\title{Flip}
\usage{
icevision_Flip(always_apply = FALSE, p = 0.5)
}
\arguments{
\item{always_apply}{always_apply}
\item{p}{p}
}
\value{
None
}
\description{
Flip the input either horizontally, vertically or both horizontally and vertically.
}
\section{Targets}{
image, mask, bboxes, keypoints
}
\section{Image types}{
uint8, float32
}
|
63e8b8ce55346dbbbbbce18321f8ec59c5f873c4
|
ac40ac14de2765386f86ad7a6c4d44293226ce1f
|
/Machine Learning II/Lab II/ISLR 7-8 Lab.R
|
15afe1f70a65a3e4a4ef838cf5e760a4ca09e29d
|
[] |
no_license
|
King-AK/MSBA
|
846974427be025769294a6ed8e3cc65ab55e1e31
|
ece0a24e4ed8da11b95317de35287a75280a1a3e
|
refs/heads/master
| 2020-01-23T21:24:17.933797
| 2017-06-06T15:34:13
| 2017-06-06T15:34:13
| 67,090,677
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,287
|
r
|
ISLR 7-8 Lab.R
|
#VJ Davey
#ISLR 7.8 Lab
#non-linear modeling
rm(list = ls())
library(ISLR)
attach(Wage)
#Going to analyze the Wage data in order to illustrate that nonlinear fitting procedures can be easly implemented in R
### 7.8.1 Polynomial Regression and Step Functions
fit = lm(wage~poly(age,4),data=Wage)
summary(fit)
coef(summary(fit))
#above: a linear model has been fit using the lm() function in order to predict wage using a 4th degree polynomial.
#poly() allows us to avoid writing out a long formula with powers of age.
#the poly() function returns a matrix whose columns are a basis of orthogonal polynomials, which essentially means that each column is a linear combination of the variables age, age^2, age^3, and age^4
#we can use poly() to obtain age, age2, age^3, and age^4 directly by using the raw=TRUE argument
fit2=lm(wage~poly(age,4,raw=TRUE), data=Wage)
coef(summary(fit2))
#there are several other equivalent ways of fitting this model, which showcase the flexibility of the formula language
#for example:
fit2a=lm(wage~age+I(age^2)+I(age^3)+I(age^4), data=Wage)
coef(fit2a)
#the above simiply creates the polynomial basis functions on the fly, taking care to protect terms like age^2 via the wrapper function I()
#the following will do the same, though more compactly, by using the cbind() function for building a matrix from a collection of vectors
fit2b=lm(wage~cbind(age,age^2,age^3,age^4),data=Wage)
#we now create a grid of values for age at which we want predcitions for wage, and then call predict()
agelims=range(age)
age.grid=seq(from=agelims[1],to=agelims[2])
preds=predict(fit,newdata=list(age=age.grid),se=TRUE)
se.bands=cbind(preds$fit+2*preds$se.fit,preds$fit-2*preds$se.fit)
#finally, we plot the data and add the fit from the degree-4 polynomial
par(mfrow=c(1,2),mar=c(4.5,4.5,1,1), oma=c(0,0,4,0))
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Degree-4 Polynomial", outer=T)
lines(age.grid,preds$fit,lwd=2, col="blue")
matlines(age.grid,se.bands,lwd=1,col="blue",lty=3)
#mar and oma arguments allow us to control the margins of the plot
#title function creates a figure title that spans both subplots
#In performing a polynomial regression, we must decide on the degree of the polynomial to use. We can do this using hypothesis tests. We can fit models ranging from linear to degree-p and seek to determine te simplest model which is sufficient to explain the relationship between Wage and Age.
#we use the anova() function to perform an analysis of variance (ANOVA using an f-test) in order to test the null hypothesis that a model M1 is sufficient to explain the data against the hypothesis that a more complex model M2 is required
#in order to use anova() M1 and M2 must be nested models: the predeictors in M1 must be a subset of the predictors in M2
#in this case, we fit five different models and sequentially compare the simpler model to the more complex model
fit.1=lm(wage~age,data=Wage)
fit.2=lm(wage~poly(age,2),data=Wage)
fit.3=lm(wage~poly(age,3),data=Wage)
fit.4=lm(wage~poly(age,4),data=Wage)
fit.5=lm(wage~poly(age,5),data=Wage)
anova(fit.1,fit.2,fit.3,fit.4,fit.5)
#the p=value comparing the linear model 1 to the quadratic model 2 is essentially 0, indicating that a linear fit is not sufficient
#the p-value comparing the quadratic model 2 to the cubic model 3 is also very low, so the quadratic fit is insufficient
#quartic is around 5%
#quintic is way too much
#So either a cubic or quartic model give the most reasonable fit to the data, but higher or lower order models are not justified via ANOVA
#as an alternative to using hypothesis tests and ANOVA, we could choose polynomial degree using Cross Validation
#we can also do classification much in the same way. We make sure we have an appropriate response vector and then apply the glm() function using the appropriate family (binomial, multinomial) in order to fit a polynomial logistic regression model
fit=glm(I(wage>250)~poly(age,4), data=Wage,family=binomial)
preds=predict(fit,newdata=list(age=age.grid),se=TRUE)
#calculating the confidence intervals is more involved than in the linear regression case.
pfit=exp(preds$fit)/(1+exp(preds$fit))
se.bands.logit = cbind(preds$fit +2* preds$se.fit , preds$fit-2*preds$se.fit)
se.bands = exp(se.bands.logit)/(1+exp(se.bands.logit))
#plot the fit
plot(age,I(wage>250),xlim=agelims,type="n",ylim=c(0,0.2))
points(jitter(age), I((wage>250)/5), cex=.5, pch="|", col="darkgrey")
lines(age.grid,pfit,lwd=2,col="blue")
matlines(age.grid, se.bands,lwd=1,col="blue",lty=3)
#step functions
###7.8.2 Splines
#regression splines can be fit by constructing an appropriate matrix of basis functions
#the bs() function generates the entire matrix of basis functions for splines with the specified set of knots. By default, cubic splines are produced.
library(splines)
fit <- lm(wage ~ bs(age, knots = c(25, 40, 60)), data = Wage)
pred <- predict(fit, newdata = list(age = age.grid), se = TRUE)
plot(age, wage, col = "gray")
lines(age.grid, pred$fit, lwd = 2)
lines(age.grid, pred$fit + 2*pred$se, lty = "dashed")
lines(age.grid, pred$fit - 2*pred$se, lty = "dashed")
#we specified knots at ages 25, 40 and 60. This produces a spline with six basis functions.
#we could also use the df() function to produce a spline with knots at uniform quantiles of the data
dim(bs(age,knots=c(25,40,60)))
dim(bs(age,df=6))
attr(bs(age,df=6),"knots")
#In this case, R would choose knots at 33.8, 42.0 and 51.0, which correspond to the 25th, 50th and 75th percentiles of age
#the bs() function also has a degree argument, so we can fit splines of any degree, rather than the default of 3 for cubic splines
#in order to fit a natural spline, we use the ns() function
fit2=lm(wage~ns(age,df=4),data=Wage)
pred2=predict(fit2,newdata=list(age=age.grid),se=TRUE)
lines(age.grid,pred2$fit,col="red",lwd=2)
#as with the bs() function we could instead specify the knots directly using the knots option
#in order to fit a smoothing spline, we use the smooth.spline() function
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Smoothing Spline")
fit=smooth.spline(age,wage,df=16)
fit2=smooth.spline(age,wage,cv=TRUE)
fit2$df
lines(fit,col="red",lwd=2)
lines(fit2,col="blue",lwd=2)
legend("topright",legend=c("16 DF", "6.8 DF"), col=c("red","blue"),lty=1,lwd=2,cex=.8)
#in the first call of smooth.spline() we specified the df as being 16
#in the second call to smooth.spline() we selected the smoothness level with cross validation, yielding a lambda value that yields 6.8 degrees of freedom
#in order to perform local regression we use the loess() function
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Local Regression")
fit=loess(wage~age,span=.2,data=Wage)
fit2=loess(wage~age,span=.5,data=Wage)
lines(age.grid,predict(fit,data.frame(age=age.grid)),col="red",lwd=2)
lines(age.grid,predict(fit2,data.frame(age=age.grid)),col="blue",lwd=2)
legend("topright",legend=c("Span=0.2","Span=0.5"),col=c("red","blue"),lty=1,lwd=2,cex=.8)
#we havev performed local regression using spans of 0.2 and 0.5: that is, each neighborhood consists of 20 or 50% of the observations. The larger the span, the smoother the fit.
|
c1fa80208403498f786701b8a9d379dbdb077a5d
|
85641490bc20f312161d6ababd651e2503bab20c
|
/Metabolomics stats course.R
|
64d786edc5a409d38e89b33cb05944dffd6bafb1
|
[] |
no_license
|
JoeRothwell/MOOCs
|
159236fcbf2baf7e0382d73c1e2222a5d262f8bb
|
70f8583d376ee779fe25b4427cad36f5b2aafa75
|
refs/heads/master
| 2023-04-29T22:41:56.455539
| 2023-04-24T06:28:18
| 2023-04-24T06:28:18
| 180,340,799
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,065
|
r
|
Metabolomics stats course.R
|
# Functions 1 -----------------------------------
describe <- function(object, ...) {
UseMethod("describe")
}
describe.default <- function(object, var.name, na.rm=TRUE) {
if (!is.numeric(object)) {
stop(deparse(substitute(object)), " must be numeric")
}
if (missing(var.name)) {
var.name <- deparse(substitute(object))
}
par(mfrow=c(2, 3), pty="s")
plot(density(object, na.rm=na.rm), main="Density")
hist(object, main="Histogram", xlab=var.name)
qqnorm(object, pch=20, main="Normal Q-Q plot")
qqline(object)
plot(density(log(object), na.rm=na.rm), main="Density")
hist(log(object), main="Histogram", xlab=sprintf("log(%s)", var.name))
qqnorm(log(object), pch=20, main="Normal Q-Q plot")
qqline(log(object))
}
describe.data.frame <- function(object, na.rm=TRUE) {
devAskNewPage(ask=TRUE)
invisible(lapply(object, function(x) {
var.name <- names(eval(sys.call(1)[[2]]))[substitute(x)[[3]]]
describe(x, var.name, na.rm)
}))
}
describe.matrix <- function(object, na.rm=TRUE) {
describe(as.data.frame(object), na.rm)
}
# Functions 2 --------------------------------------------
library(lme4)
library(pheatmap)
pqq <- function(pvals, alpha=0.05, add=FALSE, ...) {
pvals <- na.omit(pvals)
n <- length(pvals)
exp <- -log10(1:n / n)
obs <- -log10(sort(pvals))
if (!add) {
ci <- -log10(cbind(
qbeta(alpha/2, 1:n, n - 1:n + 1),
qbeta(1 - alpha/2, 1:n, n - 1:n + 1)
))
xlim <- c(0, exp[1])
ylim <- c(0, max(obs[1], ci[1,]))
par(pty="s")
matplot(exp, ci, type="l", lty=2, col="gray50",
xlim=xlim, ylim=ylim,
xlab=expression(Expected~~-log[10]~italic(p)),
ylab=expression(Observed~~-log[10]~italic(p)), ...)
abline(0, 1, col="gray25")
}
points(exp, obs, pch=20, ...)
invisible(NULL)
}
response.name <- function(formula) {
tt <- terms(formula)
vars <- as.character(attr(tt, "variables"))[-1]
vars[attr(tt, "response")]
}
mlm <- function(formula, data=NULL, vars) {
Y <- get(response.name(formula))
formula <- update(formula, "NULL ~ .")
mf <- model.frame(formula, data, drop.unused.levels=TRUE)
mm <- model.matrix(formula, mf)
labs <- labels(terms(formula))
if (missing(vars)) {
vars <- labs
}
colnames(mm) <- sprintf("V%d", 1:ncol(mm))
new.vars <- colnames(mm)[attr(mm, "assign") %in% match(vars, labs)]
mm <- as.data.frame(mm)
formula <- as.formula(sprintf("y ~ %s - 1",
paste0(colnames(mm), collapse=" + ")
))
coefs <- array(NA, c(ncol(Y), length(vars), 3),
dimnames=list(colnames(Y), vars,
c("coef", "coef.se", "pval")))
options(warn=2)
for (i in 1:ncol(Y)) {
mm$y <- Y[,i]
model <- try(lm(formula, data=mm), silent=TRUE)
if (inherits(model, "try-error")) {
next
}
tmp <- try(coef(summary(model)), silent=TRUE)
if (inherits(tmp, "try-error")) {
next
}
for (j in 1:length(vars)) if (new.vars[j] %in% rownames(tmp)) {
coefs[i,vars[j],"coef"] <- tmp[new.vars[j],"Estimate"]
coefs[i,vars[j],"coef.se"] <- tmp[new.vars[j],"Std. Error"]
coefs[i,vars[j],"pval"] <- tmp[new.vars[j],"Pr(>|t|)"]
}
}
if (length(vars) == 1) {
coefs <- as.data.frame(coefs[,1,])
}
coefs
}
setGeneric("VarComp", function(object) {
standardGeneric("VarComp")
})
setMethod("VarComp", signature(object="merMod"),
function(object) {
vc <- VarCorr(object)
sds <- lapply(vc, attr, which="stddev")
data.frame(
group=c(rep(names(sds), unlist(lapply(sds, length))), "Residual"),
var.name=c(c(lapply(sds, names), recursive=TRUE), NA),
var=c(c(sds, recursive=TRUE), attr(vc, "sc"))**2,
row.names=NULL,
stringsAsFactors=FALSE
)
})
setGeneric("re.rank", function(object, whichel) {
standardGeneric("re.rank")
})
setMethod("re.rank", signature(object="merMod", whichel="missing"),
function(object, whichel) {
re.rank(object, names(getME(object, "flist")))
})
setMethod("re.rank", signature(object="merMod", whichel="character"),
function(object, whichel) {
vc <- subset(VarComp(object),
group %in% whichel & var.name == "(Intercept)")
groups <- vc$group[which(vc$var > .Machine$double.eps)]
lapply(ranef(object)[groups], function(x) {
structure(rank(x[,"(Intercept)"]), names=rownames(x))
})
})
mlmer <- function(formula, data=NULL, vars, lrt=TRUE) {
Y <- get(response.name(formula))
lf <- lFormula(update(formula, "NULL ~ ."), data, REML=FALSE,
control=lmerControl(check.formula.LHS="ignore"))
labs <- as.character(attr(terms(lf$fr), "predvars.fixed")[-1])
if (missing(vars)) {
vars <- labs
}
mm <- lf$X
colnames(mm) <- sprintf("V%d", 1:ncol(mm))
new.vars <- colnames(mm)[which(attr(lf$X, "assign") %in% match(vars, labs))]
formula <- as.formula(sprintf("y ~ %s - 1",
paste0(c(
sprintf("(%s)", findbars(formula)), colnames(mm)
), collapse=" + ")
))
model.data <- cbind(mm, lf$reTrms$flist)
coefs <- array(NA, c(ncol(Y), length(vars), 3),
dimnames=list(colnames(Y), vars, c("coef", "coef.se", "pval")))
re.ranks <- lapply(lf$reTrms$flist, function(x) {
tmp <- matrix(0, nlevels(x), nlevels(x),
dimnames=list(levels(x), 1:nlevels(x)))
attr(tmp, "count") <- 0
tmp
})
options(warn=2)
for (i in 1:ncol(Y)) {
model.data$y <- Y[,i]
data.subset <- subset(model.data, !is.na(y))
model <- try(lmer(formula, data=data.subset, REML=FALSE), silent=TRUE)
if (inherits(model, "try-error")) {
next
}
tmp <- try(coef(summary(model)), silent=TRUE)
if (inherits(tmp, "try-error")) {
next
}
for (j in 1:length(vars)) if (new.vars[j] %in% rownames(tmp)) {
coefs[i,vars[j],"coef"] <- tmp[new.vars[j],"Estimate"]
coefs[i,vars[j],"coef.se"] <- tmp[new.vars[j],"Std. Error"]
if (!lrt) {
coefs[i,vars[j],"pval"] <-
2 * pt(abs(tmp[new.vars[j],"t value"]),
df=df.residual(model), lower.tail=FALSE)
} else {
lrt.formula <- as.formula(sprintf(". ~ . - %s", new.vars[j]))
model0 <- try(update(model, lrt.formula), silent=TRUE)
if (inherits(model0, "try-error")) {
next
}
coefs[i,vars[j],"pval"] <-
anova(model0, model)["model","Pr(>Chisq)"]
}
}
ranks <- lapply(re.rank(model), function(x) {
diag(length(x))[x,]
})
for (g in names(ranks)) {
re.ranks[[g]] <- re.ranks[[g]] + ranks[[g]]
attr(re.ranks[[g]], "count") <- attr(re.ranks[[g]], "count") + 1
}
}
if (length(vars) == 1) {
coefs <- as.data.frame(coefs[,1,])
}
list(coefs=coefs, re.ranks=re.ranks)
}
plot.ranks <- function(x, col="red") {
x <- x / attr(x, "count")
breaks <- seq(0, max(x), length.out=101)
cols <- c(
rep("white", sum(breaks <= 1 / ncol(x)) - 1),
colorRampPalette(c("white", col))(sum(breaks > 1 / ncol(x)))
)
pheatmap(x, color=cols, breaks=breaks,
cluster_rows=FALSE, cluster_cols=FALSE)
invisible(NULL)
}
# Functions 3----------------------------------------------------------------
library(glmnet)
best.coefs <- function(model) {
stopifnot(inherits(model, "cv.glmnet"))
best.model <- which.min(model$cvm)
if (!is.list(model$glmnet.fit$beta)) {
model$glmnet.fit$beta <- list(model$glmnet.fit$beta)
}
tmp <- lapply(model$glmnet.fit$beta, function(x) {
x <- x[,best.model]
x[abs(x) > sqrt(.Machine$double.eps)]
})
with(model$glmnet.fit, cbind(
`(Intercept)`=a0[best.model],
do.call(rbind, tmp)
))
}
|
bf0ec8e2590c30856bd0ea5c6769d999a24ff03d
|
21327fc1d5030fc4bb69e6505b66b471290fae5e
|
/commands.R
|
f12df4b8259aadf00b92842c5346d677a430a767
|
[] |
no_license
|
sportebois/Rbash
|
995f6c6aac874cde109c7ae661d76cc49d005421
|
de809e643055f4856c5175bb44e71bdb4f4d53f3
|
refs/heads/master
| 2021-01-10T03:42:28.372645
| 2016-03-14T01:23:53
| 2016-03-14T01:23:53
| 53,816,427
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 290
|
r
|
commands.R
|
#commands.R
print("sourcing 'commands.R'")
source("defaultTestValue.R")
testVal <- getOption("test", default = "defaultTestInCommands")
print(paste0("test value is ", testVal))
testVal2 <- getOption("test2", default = "defaultTest2InCommands")
print(paste0("test2 value is ", testVal2))
|
2f26cb4c59202ac86d289aadb0a1c2efca0d2bd3
|
c3bcb4169d97579d67e14198b7ed827562f7c7bb
|
/Supervised_Learning/project_1.R
|
3fbcc8c83b83cda25cce266725438b14ae39f679
|
[] |
no_license
|
xiaomizhou616/Machine-Learning
|
c11c5cfa582231abd6438ca2c5fe937129a37887
|
66287a275a62305c340933041c981b7661badbe6
|
refs/heads/master
| 2020-04-11T06:46:37.706765
| 2018-12-13T08:20:40
| 2018-12-13T08:20:40
| 161,591,458
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 32,848
|
r
|
project_1.R
|
#protein solubility dataset
#preprocessing data
total=read.csv("solubility2.csv")
total[,1]<-NULL
total[,1]<-NULL
total[ ,1][total[ ,1]>100]<-100
total[ ,1]<-total[ ,1]/100
colnames(total)[1] <- "solubility"
total[,3]<-total[,1]
total[,1]<-NULL
x<-na.omit(total)
x[ ,1]<-as.character(x[ ,1])
length(x[ ,1])
library("protr")
x[ ,1] = x[ ,1][(sapply(x[ ,1], protcheck))]
length(x[ ,1])
x1 = t(sapply(x[ ,1], extractAAC))
x[ ,3:22] <- x1[ ,1:20]
x[ ,1]<-NULL
x[ ,22]<-x[ ,1]
x[ ,1]<-NULL
colnames(x)[21] <- "solubility"
write.csv(x,"cleandata_1.csv")
clean7=read.csv("cleandata_1.csv")
clean7[ ,1]<-NULL
clean7$solubility[clean7$solubility >= 0.5] <-1
clean7$solubility[clean7$solubility < 0.5] <- 0
# 70% training data, 10 fold cross validation, 30% test data
library('caret')
set.seed(5)
TrainingDataIndex <- createDataPartition(clean7[ ,21], p=0.7, list = FALSE)
train7 <- clean7[TrainingDataIndex,]
test7<- clean7[-TrainingDataIndex,]
#decison tree
# train accuracy
testdata<-train7
testdata[ ,21]<-NULL
install.packages("party")
library(party)
#output.tree <- ctree(solubility ~ ., data=train7)
output.tree <- ctree(solubility ~ ., data=train7,controls = ctree_control(mincriterion = 0.1))
pred <- predict(output.tree, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = train7$solubility, Predicted = prediction[,1]))
sum(diag(cm))/length(train7$solubility)
plot(output.tree)
#prune tree
#0.1 249 0.8357532
#0.2 189 0.8212341
#0.3 147 0.8030853
#0.4 141 0.7999093
#0.5 121 0.7881125
#0.6 97 0.7767695
#0.7 59 0.7617967
#0.8 45 0.7459165
#0.9 39 0.7436479
#default mincriterion 0.95 29 nodes accuracy 0.7345735
#mincriterion 0.98 25 nodes accuracy 0.7214156
#0.99 25 0.7214156
#1 1 0.5494555
# cv accuracy
d1 = NULL
for (x in seq(0,1,0.1)) {
d2 = NULL
yourData<-train7[sample(nrow(train7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7 <- yourData[testIndexes, ]
train_7 <- yourData[-testIndexes, ]
testdata_7<-test_7
test_7[ ,21]<-NULL
output.tree <- ctree(solubility ~ ., data=train_7,controls = ctree_control(mincriterion = x))
pred <- predict(output.tree, newdata = test_7)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = testdata_7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(testdata_7$solubility)
d2 = rbind(d2, data.frame(i,accuracy))
}
average_accuracy=sum(d2[1:10,2])/10
d1 = rbind(d1, data.frame(x,average_accuracy))
}
write.csv(d1,"DT_1.csv")
#test data
testdata<-test7
testdata[ ,21]<-NULL
d1 = NULL
for (x in seq(0,1,0.1)) {
output.tree <- ctree(solubility ~ ., data=train7,controls = ctree_control(mincriterion = x))
pred <- predict(output.tree, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = test7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(test7$solubility)
d1 = rbind(d1, data.frame(x,accuracy))
}
write.csv(d1,"DT_2.csv")
# choose mincriterion 0.8
# change training data ratio
#calculate train accuracy
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
output.tree <- ctree(solubility ~ ., data=train_7,controls = ctree_control(mincriterion = 0.8))
pred <- predict(output.tree, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = train_7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(train_7$solubility)
d1 = rbind(d1, data.frame(x,accuracy))
}
write.csv(d1,"DT_3.csv")
#cv accuracy
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
d3 = NULL
yourData<-train_7[sample(nrow(train_7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7data <- yourData[testIndexes, ]
train_7data <- yourData[-testIndexes, ]
testdata_7data<-test_7data
test_7data[ ,21]<-NULL
output.tree <- ctree(solubility ~ ., data=train_7data,controls = ctree_control(mincriterion = 0.8))
pred <- predict(output.tree, newdata = test_7data)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = testdata_7data$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(testdata_7data$solubility)
d3 = rbind(d3, data.frame(i,accuracy))
}
average_accuracy=sum(d3[1:10,2])/10
d2 = rbind(d2, data.frame(x,average_accuracy))
}
d1$accuracy2=d2[,2]
write.csv(d1,"DT_3.csv")
# test data
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
output.tree <- ctree(solubility ~ ., data=train_7,controls = ctree_control(mincriterion = 0.8))
pred <- predict(output.tree, newdata = test7)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = test7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(test7$solubility)
d1 = rbind(d1, data.frame(x,accuracy))
}
# svm
# kernel
#train accuracy
library("e1071")
testdata<-train7
testdata[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train7,kernel ="radial")
pred <- predict(svm_model, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = train7$solubility, Predicted = prediction[,1]))
sum(diag(cm))/length(train7$solubility)
#radial 0.8393829 cv 0.7363739
#linear 0.7068966 0.6996154
#sigmoid 0.4664247 0.5131201
#polynomial 0.7654265 0.647427
# kernel cv accuracy
#cv accuracy
d2 = NULL
yourData<-train7[sample(nrow(train7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7 <- yourData[testIndexes, ]
train_7 <- yourData[-testIndexes, ]
testdata_7<-test_7
test_7[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train_7,kernel ="polynomial")
pred <- predict(svm_model, newdata = test_7)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = testdata_7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(testdata_7$solubility)
d2 = rbind(d2, data.frame(i,accuracy))
}
average_accuracy=sum(d2[1:10,2])/10
average_accuracy
# training data ratio
# train accuracy
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train_7,kernel ="radial")
pred <- predict(svm_model, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = train_7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(train_7$solubility)
d1 = rbind(d1, data.frame(x,accuracy))
}
# cv accuracy
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
d3 = NULL
yourData<-train_7[sample(nrow(train_7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7data <- yourData[testIndexes, ]
train_7data <- yourData[-testIndexes, ]
testdata_7data<-test_7data
test_7data[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train_7data,kernel ="radial")
pred <- predict(svm_model, newdata = test_7data)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = testdata_7data$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(testdata_7data$solubility)
d3 = rbind(d3, data.frame(i,accuracy))
}
average_accuracy=sum(d3[1:10,2])/10
d2 = rbind(d2, data.frame(x,average_accuracy))
}
d1$accuracy2=d2[,2]
write.csv(d1,"DT_4.csv")
#test data
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train_7,kernel ="radial")
pred <- predict(svm_model, newdata = test7)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = test7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(test7$solubility)
d2 = rbind(d2, data.frame(x,accuracy))
}
d1$SVM=d2[,2]
#boosted
install.packages("adabag")
library("adabag")
train7f=train7
train7f$solubility<-as.factor(train7f$solubility)
testdata<-train7f
testdata[ ,21]<-NULL
control=rpart.control(maxdepth=8)
adaboost = boosting(solubility~., data=train7f, mfinal=100, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=testdata)
cm = as.matrix(table(Actual = train7f$solubility, Predicted = adaboost.pred$class))
sum(diag(cm))/length(train7f$solubility)
#8 100 0.9909256 0.7150494
#8 50 0.9314882 0.7173427
#8 10 0.8203267 0.7286508
# 8 5 0.7912886 0.7164562
# 10 5 0.7899274 0.7037392
# 6 5 0.7885662 0.7027849
# 4 5 0.7513612 0.7200555
# maxdepth 5 iteration 5 0.7749546 0.70645
#no control 0.7935572
# cv accuracy
d2 = NULL
yourData<-train7f[sample(nrow(train7f)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7 <- yourData[testIndexes, ]
train_7 <- yourData[-testIndexes, ]
testdata_7<-test_7
test_7[ ,21]<-NULL
control=rpart.control(maxdepth=8)
adaboost = boosting(solubility~., data=train_7, mfinal=100, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=test_7)
cm = as.matrix(table(Actual = testdata_7$solubility, Predicted = adaboost.pred$class))
accuracy=sum(diag(cm))/length(testdata_7$solubility)
d2 = rbind(d2, data.frame(i,accuracy))
}
average_accuracy=sum(d2[1:10,2])/10
average_accuracy
# traning data ratio
# train accuracy
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7f[ ,21], p=x, list = FALSE)
train_7 <- train7f[TrainingDataIndex,]
test_7<- train7f[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
control=rpart.control(maxdepth=8)
adaboost = boosting(solubility~., data=train_7, mfinal=10, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=testdata)
cm = as.matrix(table(Actual = train_7$solubility, Predicted = adaboost.pred$class))
accuracy=sum(diag(cm))/length(train_7$solubility)
d1 = rbind(d1, data.frame(x,accuracy))
}
# cv accuracy
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7f[ ,21], p=x, list = FALSE)
train_7 <- train7f[TrainingDataIndex,]
test_7<- train7f[-TrainingDataIndex,]
d3 = NULL
yourData<-train_7[sample(nrow(train_7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7data <- yourData[testIndexes, ]
train_7data <- yourData[-testIndexes, ]
testdata_7data<-test_7data
test_7data[ ,21]<-NULL
control=rpart.control(maxdepth=8)
adaboost = boosting(solubility~., data=train_7data, mfinal=10, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=test_7data)
cm = as.matrix(table(Actual = testdata_7data$solubility, Predicted = adaboost.pred$class))
accuracy=sum(diag(cm))/length(testdata_7data$solubility)
d3 = rbind(d3, data.frame(i,accuracy))
}
average_accuracy=sum(d3[1:10,2])/10
d2 = rbind(d2, data.frame(x,average_accuracy))
}
d1$accuracy2=d2[,2]
write.csv(d1,"DT_5.csv")
#test data
test7f=test7
test7f$solubility<-as.factor(test7f$solubility)
d3 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7f[ ,21], p=x, list = FALSE)
train_7 <- train7f[TrainingDataIndex,]
test_7<- train7f[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
control=rpart.control(maxdepth=8)
adaboost = boosting(solubility~., data=train_7, mfinal=10, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=test7f)
cm = as.matrix(table(Actual = test7f$solubility, Predicted = adaboost.pred$class))
accuracy=sum(diag(cm))/length(test7f$solubility)
d3 = rbind(d3, data.frame(x,accuracy))
}
d1$Boosting=d3[,2]
# knn
# change neigbor n
# train accuracy
install.packages("class")
library("class")
knn.model=knn(train=train7[,1:20], test=train7[,1:20], cl=train7$solubility, k =30)
d=table(train7$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
accuracy
# k 100 accuracy 0.6515426 0.6465405
# 50 0.6705989 0.6565076
# 30 0.6873866 0.6601687
# 10 0.7295826 0.6660736
# 5 0.7717786 0.6655944
# 1 0.996824 0.62297
# cv accuracy
d2 = NULL
yourData<-train7[sample(nrow(train7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7 <- yourData[testIndexes, ]
train_7 <- yourData[-testIndexes, ]
testdata_7<-test_7
test_7[ ,21]<-NULL
knn.model=knn(train=train_7[,1:20], test=testdata_7[,1:20], cl=train_7$solubility, k =1)
d=table(testdata_7$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
d2 = rbind(d2, data.frame(i,accuracy))
}
average_accuracy=sum(d2[1:10,2])/10
average_accuracy
# traning data ratio
#train accuracy
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
knn.model=knn(train=train_7[,1:20], test=train_7[,1:20], cl=train_7$solubility, k =10)
d=table(train_7$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
d1 = rbind(d1, data.frame(x,accuracy))
}
# cv accuracy
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
d3 = NULL
yourData<-train_7[sample(nrow(train_7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7data <- yourData[testIndexes, ]
train_7data <- yourData[-testIndexes, ]
testdata_7data<-test_7data
test_7data[ ,21]<-NULL
knn.model=knn(train=train_7data[,1:20], test=testdata_7data[,1:20], cl=train_7data$solubility, k =10)
d=table(testdata_7data$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
d3 = rbind(d3, data.frame(i,accuracy))
}
average_accuracy=sum(d3[1:10,2])/10
d2 = rbind(d2, data.frame(x,average_accuracy))
}
d1$accuracy2=d2[,2]
write.csv(d1,"DT_6.csv")
# test data
d4 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
knn.model=knn(train=train_7[,1:20], test=test7[,1:20], cl=train_7$solubility, k =10)
d=table(test7$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
d4 = rbind(d4, data.frame(x,accuracy))
}
d1$KNN=d4[,2]
write.csv(d1,"DT_7.csv")
# bank dataset
# preprocessing
clean7=read.csv("bank.csv")
clean7[ ,1]<-NULL
colnames(clean7)[21]="solubility"
clean7$solubility=as.numeric(as.factor(clean7$solubility))
clean7$solubility[clean7$solubility == 1] <-0
clean7$solubility[clean7$solubility ==2] <- 1
# 70% training data, 10 fold cross validation, 30% test data
library('caret')
set.seed(5)
TrainingDataIndex <- createDataPartition(clean7[ ,21], p=0.7, list = FALSE)
train7 <- clean7[TrainingDataIndex,]
test7<- clean7[-TrainingDataIndex,]
# Decision tree
# train accuracy
testdata<-train7
testdata[ ,21]<-NULL
install.packages("party")
library(party)
#output.tree <- ctree(solubility ~ ., data=train7)
output.tree <- ctree(solubility ~ ., data=train7,controls = ctree_control(mincriterion = 1))
pred <- predict(output.tree, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = train7$solubility, Predicted = prediction[,1]))
sum(diag(cm))/length(train7$solubility)
# cv accuracy
d1 = NULL
for (x in seq(0,1,0.1)) {
d2 = NULL
yourData<-train7[sample(nrow(train7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7 <- yourData[testIndexes, ]
train_7 <- yourData[-testIndexes, ]
testdata_7<-test_7
test_7[ ,21]<-NULL
output.tree <- ctree(solubility ~ ., data=train_7,controls = ctree_control(mincriterion = x))
pred <- predict(output.tree, newdata = test_7)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = testdata_7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(testdata_7$solubility)
d2 = rbind(d2, data.frame(i,accuracy))
}
average_accuracy=sum(d2[1:10,2])/10
d1 = rbind(d1, data.frame(x,average_accuracy))
}
write.csv(d1,"DT_8.csv")
# training data ratio
# parameter 0.7
# train accuracy
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
output.tree <- ctree(solubility ~ ., data=train_7,controls = ctree_control(mincriterion = 0.7))
pred <- predict(output.tree, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = train_7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(train_7$solubility)
d1 = rbind(d1, data.frame(x,accuracy))
}
#cv accuracy
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
d3 = NULL
yourData<-train_7[sample(nrow(train_7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7data <- yourData[testIndexes, ]
train_7data <- yourData[-testIndexes, ]
testdata_7data<-test_7data
test_7data[ ,21]<-NULL
output.tree <- ctree(solubility ~ ., data=train_7data,controls = ctree_control(mincriterion = 0.7))
pred <- predict(output.tree, newdata = test_7data)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = testdata_7data$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(testdata_7data$solubility)
d3 = rbind(d3, data.frame(i,accuracy))
}
average_accuracy=sum(d3[1:10,2])/10
d2 = rbind(d2, data.frame(x,average_accuracy))
}
d1$accuracy2=d2[,2]
write.csv(d1,"DT_9.csv")
# test data
d10 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
output.tree <- ctree(solubility ~ ., data=train_7,controls = ctree_control(mincriterion = 0.7))
pred <- predict(output.tree, newdata = test7)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = test7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(test7$solubility)
d10 = rbind(d10, data.frame(x,accuracy))
}
#SVM
#kernel
#train accuracy
testdata<-train7
testdata[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train7,kernel ="radial")
pred <- predict(svm_model, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = train7$solubility, Predicted = prediction[,1]))
sum(diag(cm))/length(train7$solubility)
# kernel cv accuracy
#radial 0.9072789 0.9057521
#linear 0.8993711 0.8991396
#sigmoid 0.8061413 0.810627
#polynomial 0.9034406 0.9017752
#cv accuracy
d2 = NULL
yourData<-train7[sample(nrow(train7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7 <- yourData[testIndexes, ]
train_7 <- yourData[-testIndexes, ]
testdata_7<-test_7
test_7[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train_7,kernel ="radial")
pred <- predict(svm_model, newdata = test_7)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = testdata_7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(testdata_7$solubility)
d2 = rbind(d2, data.frame(i,accuracy))
}
average_accuracy=sum(d2[1:10,2])/10
average_accuracy
# training data ratio
# train accuracy
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train_7,kernel ="radial")
pred <- predict(svm_model, newdata = testdata)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = train_7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(train_7$solubility)
d1 = rbind(d1, data.frame(x,accuracy))
}
# cv accuracy
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
d3 = NULL
yourData<-train_7[sample(nrow(train_7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7data <- yourData[testIndexes, ]
train_7data <- yourData[-testIndexes, ]
testdata_7data<-test_7data
test_7data[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train_7data,kernel ="radial")
pred <- predict(svm_model, newdata = test_7data)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = testdata_7data$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(testdata_7data$solubility)
d3 = rbind(d3, data.frame(i,accuracy))
}
average_accuracy=sum(d3[1:10,2])/10
d2 = rbind(d2, data.frame(x,average_accuracy))
}
d1$accuracy2=d2[,2]
write.csv(d1,"DT_10.csv")
#test data
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
svm_model <- svm(solubility ~ ., train_7,kernel ="radial")
pred <- predict(svm_model, newdata = test7)
prediction<-data.frame(pred)
prediction[,1][prediction[,1] >= 0.5] <-1
prediction[,1][prediction[,1] < 0.5] <- 0
cm = as.matrix(table(Actual = test7$solubility, Predicted = prediction[,1]))
accuracy=sum(diag(cm))/length(test7$solubility)
d2 = rbind(d2, data.frame(x,accuracy))
}
d10$SVM=d2[,2]
#boosted
library("adabag")
library("rpart")
train7f=train7
train7f$solubility<-as.factor(train7f$solubility)
testdata<-train7f
testdata[ ,21]<-NULL
control=rpart.control(maxdepth=5)
adaboost = boosting(solubility~., data=train7f, mfinal=5, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=testdata)
cm = as.matrix(table(Actual = train7f$solubility, Predicted = adaboost.pred$class))
sum(diag(cm))/length(train7f$solubility)
#8 100 0.9216611 0.9125034
#8 50 0.9202275 0.9107935
#8 10 0.9164354 0.9110244
# 8 5 0.9145856 0.9078809
# 10 5 0.9083888 0.9088974
# 6 5 0.9153718 0.90802
# 4 5 0.913707 0.9086193
# maxdepth 5 iteration 5 0.913707 0.9097298
# cv accuracy
d2 = NULL
yourData<-train7f[sample(nrow(train7f)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7 <- yourData[testIndexes, ]
train_7 <- yourData[-testIndexes, ]
testdata_7<-test_7
test_7[ ,21]<-NULL
control=rpart.control(maxdepth=5)
adaboost = boosting(solubility~., data=train_7, mfinal=5, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=test_7)
cm = as.matrix(table(Actual = testdata_7$solubility, Predicted = adaboost.pred$class))
accuracy=sum(diag(cm))/length(testdata_7$solubility)
d2 = rbind(d2, data.frame(i,accuracy))
}
average_accuracy=sum(d2[1:10,2])/10
average_accuracy
# traning data ratio
# train accuracy
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7f[ ,21], p=x, list = FALSE)
train_7 <- train7f[TrainingDataIndex,]
test_7<- train7f[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
control=rpart.control(maxdepth=8)
adaboost = boosting(solubility~., data=train_7, mfinal=100, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=testdata)
cm = as.matrix(table(Actual = train_7$solubility, Predicted = adaboost.pred$class))
accuracy=sum(diag(cm))/length(train_7$solubility)
d1 = rbind(d1, data.frame(x,accuracy))
}
# cv accuracy
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7f[ ,21], p=x, list = FALSE)
train_7 <- train7f[TrainingDataIndex,]
test_7<- train7f[-TrainingDataIndex,]
d3 = NULL
yourData<-train_7[sample(nrow(train_7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7data <- yourData[testIndexes, ]
train_7data <- yourData[-testIndexes, ]
testdata_7data<-test_7data
test_7data[ ,21]<-NULL
control=rpart.control(maxdepth=8)
adaboost = boosting(solubility~., data=train_7data, mfinal=100, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=test_7data)
cm = as.matrix(table(Actual = testdata_7data$solubility, Predicted = adaboost.pred$class))
accuracy=sum(diag(cm))/length(testdata_7data$solubility)
d3 = rbind(d3, data.frame(i,accuracy))
}
average_accuracy=sum(d3[1:10,2])/10
d2 = rbind(d2, data.frame(x,average_accuracy))
}
d1$accuracy2=d2[,2]
write.csv(d1,"DT_12.csv")
#test data
test7f=test7
test7f$solubility<-as.factor(test7f$solubility)
d3 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7f[ ,21], p=x, list = FALSE)
train_7 <- train7f[TrainingDataIndex,]
test_7<- train7f[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
control=rpart.control(maxdepth=8)
adaboost = boosting(solubility~., data=train_7, mfinal=100, control=control)
adaboost.pred <- predict.boosting(adaboost,newdata=test7f)
cm = as.matrix(table(Actual = test7f$solubility, Predicted = adaboost.pred$class))
accuracy=sum(diag(cm))/length(test7f$solubility)
d3 = rbind(d3, data.frame(x,accuracy))
}
d10$Boosting=d3[,2]
write.csv(d10,"DT_13.csv")
write.csv(d10,"DT_13.csv")
# KNN
clean7$job=as.numeric(as.factor(clean7$job))
clean7$marital=as.numeric(as.factor(clean7$marital))
clean7$education=as.numeric(as.factor(clean7$education))
clean7$default=as.numeric(as.factor(clean7$default))
clean7$housing=as.numeric(as.factor(clean7$housing))
clean7$loan=as.numeric(as.factor(clean7$loan))
clean7$contact=as.numeric(as.factor(clean7$contact))
clean7$month=as.numeric(as.factor(clean7$month))
clean7$day_of_week=as.numeric(as.factor(clean7$day_of_week))
clean7$poutcome=as.numeric(as.factor(clean7$poutcome))
str(clean7)
set.seed(5)
TrainingDataIndex <- createDataPartition(clean7[ ,21], p=0.7, list = FALSE)
train7 <- clean7[TrainingDataIndex,]
test7<- clean7[-TrainingDataIndex,]
# change neigbor n
# train accuracy
install.packages("class")
library("class")
knn.model=knn(train=train7[,1:20], test=train7[,1:20], cl=train7$solubility, k =1)
d=table(train7$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
accuracy
# k 100 accuracy 0.9132908 0.9117189
# 50 0.9143082 0.9119495
# 30 0.916343 0.9113026
# 10 0.922401 0.9083892
# 5 0.9304477 0.9033943
# 1 1 0.8877167
# cv accuracy
d2 = NULL
yourData<-train7[sample(nrow(train7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7 <- yourData[testIndexes, ]
train_7 <- yourData[-testIndexes, ]
testdata_7<-test_7
test_7[ ,21]<-NULL
knn.model=knn(train=train_7[,1:20], test=testdata_7[,1:20], cl=train_7$solubility, k =100)
d=table(testdata_7$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
d2 = rbind(d2, data.frame(i,accuracy))
}
average_accuracy=sum(d2[1:10,2])/10
average_accuracy
# traning data ratio
#train accuracy
d1 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
knn.model=knn(train=train_7[,1:20], test=train_7[,1:20], cl=train_7$solubility, k =50)
d=table(train_7$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
d1 = rbind(d1, data.frame(x,accuracy))
}
# cv accuracy
d2 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
d3 = NULL
yourData<-train_7[sample(nrow(train_7)),]
folds <- cut(seq(1,nrow(yourData)),breaks=10,labels=FALSE)
for (i in seq(1,10,1)) {
testIndexes <- which(folds==i,arr.ind=TRUE)
test_7data <- yourData[testIndexes, ]
train_7data <- yourData[-testIndexes, ]
testdata_7data<-test_7data
test_7data[ ,21]<-NULL
knn.model=knn(train=train_7data[,1:20], test=testdata_7data[,1:20], cl=train_7data$solubility, k =50)
d=table(testdata_7data$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
d3 = rbind(d3, data.frame(i,accuracy))
}
average_accuracy=sum(d3[1:10,2])/10
d2 = rbind(d2, data.frame(x,average_accuracy))
}
d1$accuracy2=d2[,2]
write.csv(d1,"DT_11.csv")
# test data
d4 = NULL
for (x in seq(0.1,1,0.1)) {
set.seed(5)
TrainingDataIndex <- createDataPartition(train7[ ,21], p=x, list = FALSE)
train_7 <- train7[TrainingDataIndex,]
test_7<- train7[-TrainingDataIndex,]
testdata<-train_7
testdata[ ,21]<-NULL
knn.model=knn(train=train_7[,1:20], test=test7[,1:20], cl=train_7$solubility, k =50)
d=table(test7$solubility,knn.model)
accuracy=sum(diag(d))/sum(d)
d4 = rbind(d4, data.frame(x,accuracy))
}
d10$KNN=d4[,2]
write.csv(d10,"DT_13.csv")
|
3b5dc22392ec65738fe393c30f4d2a671c0e78fd
|
b73f1bbdfad964a17c9225f2d3ef41d28f15cc69
|
/cachematrix.R
|
54a50ee734fbbd0415eb96cb9177ec10e05a45c9
|
[] |
no_license
|
ankitsharma222/ProgrammingAssignment2
|
b08d80cf018fdcc2c3e29532281b4821c7f65b05
|
cd47e3d97c935fdd4aa78c624fc91c4c1cf66cc4
|
refs/heads/master
| 2021-01-17T17:50:08.324287
| 2015-02-19T20:17:18
| 2015-02-19T20:17:18
| 30,935,524
| 0
| 0
| null | 2015-02-17T20:13:05
| 2015-02-17T20:13:05
| null |
UTF-8
|
R
| false
| false
| 1,168
|
r
|
cachematrix.R
|
## these functions help in caching the inverse of a matrix rather than computing it repeatedly
##to set or get the value of matrix or its inverse
makeCacheMatrix<-function(x=matrix()){
m<-NULL
set<-function(y=matrix()){
x<<-y
m<<-NULL
##to assign new data to x and discard the old inverse stored in m.
}
get<-function()x ## to get the value of x
setinv<-function(inv) m<<-inv ##takes inv as an argument and save its value in the m variable in global env
getinv<-function() m ##returns the value of mean
list(set=set,get=get,setinv=setinv,getinv=getinv)## makecachemeans returns a list of functions
}
##2nd function to calculate the inverse or to get a cached value
cacheSolve<-function(x=matrix(),...){
m<-z$getinv() ##z=makeCacheMatrix(...)
if(!is.null(m)) {
print("printing cached inverse") ##to check if m is already calculated
return(m)
}
mat<-z$get()
m<-solve(mat) ## to calculate inv if it ws not previously calculated
z$setinv(m) ##to set new value of inverse just calculated
m
}
|
5f98a1ca38df5101d8a801995b76fa9edeb2daad
|
1cb7c242633970d93fd874dddd140b4461e35278
|
/Rscripts/ensemble/compute_ensemble.R
|
8fd322b9cd874019bdfb79b1ba6a0a612f16ffe2
|
[
"MIT"
] |
permissive
|
chenxofhit/scRNAseq_clustering_comparison
|
5c2e1e79ea200dde38edeaa7dad5057541d2af82
|
de91456d411f97a45b871d55490d1e14b09031f5
|
refs/heads/master
| 2020-03-24T06:05:20.625297
| 2019-08-20T15:23:45
| 2019-08-20T15:23:45
| 142,515,237
| 0
| 0
|
MIT
| 2018-07-27T02:02:45
| 2018-07-27T02:02:45
| null |
UTF-8
|
R
| false
| false
| 4,998
|
r
|
compute_ensemble.R
|
## -------------------------------------------------------------------------- ##
## Ensemble between methods, per run, all k
## -------------------------------------------------------------------------- ##
args <- (commandArgs(trailingOnly = TRUE))
for (i in 1:length(args)) {
eval(parse(text = args[[i]]))
}
print(clusteringsummary)
print(ncores)
print(outrds)
suppressPackageStartupMessages({
library(plyr)
library(dplyr)
library(tidyr)
library(purrr)
library(clue)
library(reshape2)
library(parallel)
})
## Load clustering summary
df <- readRDS(file = clusteringsummary)
## Sample Seurat results, keep only one resolution per k
set.seed(42)
df_sub <- df %>% dplyr::filter(!(method %in% c("Seurat")))
df_seurat <- df %>% dplyr::filter(method == "Seurat") %>%
dplyr::group_by(dataset, method, k) %>%
dplyr::filter(resolution == sample(unique(resolution), 1)) %>%
dplyr::ungroup()
df_sub <- plyr::rbind.fill(df_sub, df_seurat)
## Help function for computing ensemble clusters
## df: dataframe with variables method, k, dataset, cell, trueclass, run
## methods: character vector with method names for ensemble
## output: tibble longformat with method cluster, ensemble cluster, trueclass, dataset
## ------------------------------------------------------------------
helper_ensemble <- function(methods, df) {
## Process each dataset separately
l <- vector("list", length(unique(df$dataset)))
names(l) <- unique(df$dataset)
for (i in unique(df$dataset)) {
print(paste(i, paste(methods, collapse = "+")))
## Extract only results for the current dataset
res <- df %>% dplyr::filter(dataset == i)
## Process each k separately
combined_l <- vector("list", length(unique(res$k)))
names(combined_l) <- unique(res$k)
for (u in unique(res$k)) {
## Extract only results for the current k
res.k <- res %>% dplyr::filter(k == u)
## Skip if results for one method not exist
if (sum(unique(res.k$method) %in% methods) != length(methods)) {
next
} else {
## Wide format (each method/run combination in a separate column)
res.w <- dcast(res.k %>% filter(method %in% methods),
trueclass + cell ~ method + run,
value.var = "cluster")
res2 <- res.w %>% dplyr::select(-trueclass) %>%
tibble::column_to_rownames("cell") %>% as.matrix
## If all values are NA, replace them with 0
if (all(is.na(res2))) {
res2[] <- 0
}
## Process each run separately
runs <- unique(res.k$run)
m <- matrix(NA, nrow = nrow(res2), ncol = length(runs))
rownames(m) <- rownames(res2)
colnames(m) <- runs
for (j in seq_along(runs)) {
## Extract only results for the current run
run <- grep(paste0("_", runs[j], "$"), colnames(res2))
res3 <- res2[, run]
re <- res3 %>% plyr::alply(2, clue::as.cl_partition) # partition
## Replace NA cluster assignment by zeros
re <- lapply(re, function(x) {
x <- as.matrix(x$.Data)
x[is.na(x)] <- 0
x
})
## Make ensemble and generate consensus clustering
re <- clue::as.cl_ensemble(re)
if (all(sapply(re, length) == 0)) {
m[, runs[j]] <- NA
} else {
re <- clue::cl_consensus(re, method = "SE", control = list(nruns = 50)) # no NAS!
clusters <- clue::cl_class_ids(re)
m[, runs[j]] <- clusters[rownames(m)]
}
}
out <- data.frame(res.w, stringsAsFactors = FALSE) %>%
dplyr::mutate(dataset = i, k = u, method = paste(methods, collapse = ".")) %>%
dplyr::left_join(data.frame(m, stringsAsFactors = FALSE, check.names = FALSE) %>%
tibble::rownames_to_column("cell"), by = "cell")
combined_l[[as.character(u)]] <- out
}
}
l[[i]] <- plyr::rbind.fill(combined_l)
}
res.df <- plyr::rbind.fill(l)
res.df <- reshape2::melt(data = res.df,
id.vars = c("dataset", "trueclass", "cell", "method", "k"),
measure.vars = as.character(runs),
value.name = "cons_cluster", variable.name = "run")
return(res.df)
}
## Construct all combinations of two methods
comb.ensmbl <- list(l1 = unique(df_sub$method),
l2 = unique(df_sub$method)) %>%
purrr::cross() %>% purrr::map((paste))
names(comb.ensmbl) <- sapply(comb.ensmbl, function(x) paste0(x, collapse = "."))
## Remove pairings of a method with itself
comb.ensmbl <- comb.ensmbl %>% purrr::discard(function(x) x[1] == x[2])
## Run
out <- mclapply(comb.ensmbl, helper_ensemble, df = df_sub,
mc.preschedule = FALSE, mc.cores = ncores)
out <- plyr::rbind.fill(out)
## Save
saveRDS(out, file = outrds)
date()
sessionInfo()
|
990dce1a7e47e3340c5517439776c4c3167a0714
|
02210921d469ae6c7daf9e159ea985aab6347517
|
/R/app_server.R
|
46cea8775fb3f6af3f8c468a04bf649aa2edc3ea
|
[
"MIT"
] |
permissive
|
frdanconia/shiny_etf_portfolio
|
e6e94380ac482da53fb1322a1197ca968cad1490
|
580d9d4c1c53f94e7c77a78058c78794f3b4940a
|
refs/heads/main
| 2023-03-03T22:25:16.162724
| 2021-01-23T13:26:32
| 2021-01-23T13:26:32
| 332,216,492
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 301
|
r
|
app_server.R
|
#' @import shiny
app_server <- function(input, output,session) {
# List the first level callModules here
library(tidyquant) # due to issue: https://github.com/business-science/tidyquant/issues/116
callModule(mod_universe_server, "universe_ui_1")
callModule(mod_finder_server, "finder_ui_1")
}
|
6ecfe9b7168449c56e5e9945f4a64131ec604c4e
|
8ab151cc5bfb154cc4ae4b1d97ddd6b2bedc95fa
|
/man/solar.long.Rd
|
cc205edf43403c5b7c1edc1524173c37d5038ea2
|
[] |
no_license
|
arturochian/MetFns
|
5eafd4bc404edbbdefd27223c5b8a99d32cd048d
|
5ce9fc52efdac3c2a12aa18282ab71e53aacf115
|
refs/heads/master
| 2020-04-06T04:20:15.871591
| 2014-09-16T00:00:00
| 2014-09-16T00:00:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 939
|
rd
|
solar.long.Rd
|
\name{solar.long}
\alias{solar.long}
\title{
Calculation of solar longitude
}
\description{
Calculates solar longitude with respect to the equinox of 2000.0 for given year, month, day and time.
}
\usage{
solar.long(year, month, day, time)
}
\arguments{
\item{year}{
numeric vector of length 4 specifying year.
}
\item{month}{
numeric vector specifying month of the year.
}
\item{day}{
numeric vector specifying day.
}
\item{time}{
numeric vector specifying time in hours.
}
}
\value{
\code{solar.long} returns numeric vector with a value between 0 and 359.999.
}
\references{
Steyaert C. (1991). Calculating the Solar Longitude 2000.0, \emph{WGN, Journal of the IMO}, 19:2, 31-34.
}
\author{
Kristina Veljkovic
}
\seealso{
\code{\link{filter.sol}}
}
\examples{
## calculate solar longitude for June 22, 2006, at 4h UT.
require(astroFns)
solar.long(year=2006,month=6,day=22,time=4)
}
|
7ff68bee61ea077074444adfff8499f12195c7a0
|
34382754d6fe101536f2a5755801845849a3e1d3
|
/Covid19/ui.R
|
de7a9d29dee0dd6f5edc7396355a512911c70cea
|
[] |
no_license
|
nohturfft/covid19_a
|
11284c018cb2e742e35bb6eacb0e5af3efe31e91
|
189eb2f9374c9f9cf3448b407220552b17994109
|
refs/heads/master
| 2021-03-16T17:17:07.652394
| 2020-03-14T11:43:45
| 2020-03-14T11:43:45
| 246,926,050
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,214
|
r
|
ui.R
|
#
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(dygraphs)
# Define UI for application that draws a histogram
shinyUI(fluidPage(
# App title ----
titlePanel("COVID19"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(width = 2,
selectInput(inputId='country', label='Country', choices=c("United Kingdom")),
# br() element to introduce extra vertical spacing ----
br()
),
# Main panel for displaying outputs ----
mainPanel(
# Output: Tabset w/ plot, summary, and table ----
tabsetPanel(type = "tabs",
tabPanel("Daily Cases", dygraphOutput("plot_daily")),
tabPanel("Cummulative", dygraphOutput("plot_cumm")),
tabPanel("Table", tableOutput("table"))
)
)
)
))
|
74b1a91941b836e1131c9a1252add411121b2b80
|
a206f33c8cbd90abf2f400f79b233b4e56c89f23
|
/clusterizacion/clusterizacion_steepest.R
|
3501d9d296e643dbec62d59c4e5f76338a549c85
|
[] |
no_license
|
andresrabinovich/algoritmos-geneticos
|
60a403860fcad3932e5f18bad23a6ac9312c12f1
|
6b3923981c2f51ed735451f735dd12e1c63a0d75
|
refs/heads/master
| 2021-01-10T13:46:18.419293
| 2015-07-15T12:53:51
| 2015-07-15T12:53:51
| 36,511,094
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,776
|
r
|
clusterizacion_steepest.R
|
#///////////////
#CONFIGURACIONES
#///////////////
poblacion = 1;
pm = 1; #probabilidad de mutacion
pc = 0.1; #probabilidad de single-point crossover
generaciones = 1000;
corridas = 1;
dim_red = 2; #los puntos en la red no son reales, son solo los lugares alrededor de los cuales se van a armar los clusters
puntos_por_cluster = 3;
parametro_de_red = 1;
ancho_del_cluster = 0.1; #lo que mide el cluster en x
alto_del_cluster = 0.1; #lo que mide el cluster en y
k = dim_red^2; #TO DO - Ver de adaptar el algoritmo para no tener que decirle cuantos clusters buscar
#////////////////////
#CARGADO DE LIBRERIAS
#////////////////////
library(fpc);
#/////////////////////////////////
#COMIENZAN LAS FUNCIONES GENERICAS
#/////////////////////////////////
#------------------
#Funcion de fitness
#------------------
calcular_fitness <- function(cromosoma, matriz_de_distancias){
#return (binario_a_decimal(cromosoma));
#return (sum(cromosoma));
#Vamos a probar con silhouette como funcion de fitness
#s = matrix(0, ncol=1, nrow=ncol(cromosoma));
#for(observacion in 1:ncol(cromosoma)){
# a = matrix(0, ncol=2, nrow=ncol(matriz_de_distancias));
# puntos <- c(1:ncol(cromosoma));
# puntos <- puntos[-observacion]; #Sacamos el punto que estamos viendo de los puntos a siluetear
# for(punto in puntos)){
# #Sumamos distinto
# a[cromosoma[observacion], 1]
# }
#}
#s<-silhouette(cromosoma, matriz_de_distancias);
#si<-summary(s);
#return (si$si.summary["Mean"]); #Le sumamos 1 para que sea positiva
return (cluster.stats(matriz_de_distancias, cromosoma)$ch);
}
#-------------------
#Funcion de mutacion
#-------------------
mutar <- function(cromosoma, pm, k){
#Muta un bit del cromosoma con probabilidad pm
if(runif(1) <= pm){
for(i in 1:5){
#Elije un locus al azar y lo cambia
posicion = sample(1:length(cromosoma), 1);
red<-c(1:k);
red<-red[-cromosoma[posicion]]; #Sacamos como posibilidad que la mutacion lo deje en el mismo lugar
cromosoma[posicion] = sample(red, 1);
}
}
return (cromosoma);
}
#--------------------
#Funcion de crossover
#--------------------
cruzar <- function(cromosomas_padres, pc){
#Creamos los hijos
cromosomas_hijos = cromosomas_padres;
#Hace crossover entre dos cromosomas con probabilidad pc
if(runif(1) <= pc){
#Elije un locus desde donde empezar a cruzar
posicion = sample(1:length(cromosomas_padres[1,]), 1);
cromosomas_hijos[1, 1:posicion] = cromosomas_padres[2, 1:posicion];
cromosomas_hijos[2, 1:posicion] = cromosomas_padres[1, 1:posicion];
}
return (cromosomas_hijos);
}
#-----------------------------------------------------
#Funcion que elige una pareja en funcion de su fitness
#-----------------------------------------------------
elegir_pareja <- function(fitness){
#VER COMO IMPLEMENTAR LA FUNCION SAMPLE EN C!
return (sample(1:length(fitness), 2, replace=FALSE, prob=fitness));
}
#------------------------------------
#Genera la matriz de distancias entre
#los puntos y cada elemento de la red
#------------------------------------
#generar_matriz_de_distancias <- function(puntos, redes){
# matriz_distancia = matrix(0, nrow=nrow(puntos), ncol=nrow(redes));
# for(punto in 1:nrow(puntos)){
# for(red in 1:nrow(redes)){
# matriz_distancia[punto, red] = sqrt((puntos[punto, 1]-redes[red, 1])^2 + (puntos[punto, 2]-redes[red, 2])^2);
# }
# }
# return (matriz_distancia);
#}
#////////////////////
#COMIENZA EL PROGRAMA
#////////////////////
#genero la red equiespaciada
a <- seq(1, dim_red*parametro_de_red, parametro_de_red);
red <- matrix(data=a, nrow=dim_red^2, ncol=2);
red[, 1] <- rep(a, each=dim_red);
#genero los puntos de datos alrededor de la red
puntos_en_la_red <- dim_red^2;
total_de_puntos <- puntos_en_la_red * puntos_por_cluster;
tamano_del_cromosoma = total_de_puntos;
#Los puntos de ruido que vamos a tener
cantidad_de_puntos_de_ruido <- total_de_puntos * nivel_de_ruido;
#Genero los puntos de los clusters
puntos <- matrix(0, nrow=total_de_puntos, ncol=2);
puntos[, 1] <- runif(total_de_puntos, -ancho_del_cluster, ancho_del_cluster) + rep(red[, 1], each=puntos_por_cluster);
puntos[, 2] <- runif(total_de_puntos, -alto_del_cluster, alto_del_cluster) + rep(red[, 2], each=puntos_por_cluster);
#Matriz de distancias entre los puntos y la red
#matriz_de_distancias = generar_matriz_de_distancias(puntos, red);
matriz_de_disimilaridad = dist(puntos);
#Matriz en blanco que va a guardar los cromosomas de la poblacion nueva despues de cada corrida
#Cada cromosoma es una tira ordenada que asigna a cada posicion (cada punto) uno de los clusters posibles
#de la red
cromosomas_nuevos = matrix(0, ncol=total_de_puntos, nrow=poblacion);
#Matriz que guarda el fitness de cada cromosoma
fitness = matrix(0, ncol=1, nrow=poblacion);
#Comienzan las corridas
for(corrida in 1:corridas){
#Genera los cromosomas al azar de la corrida, entre 1 y la cantidad de puntos de la red
cromosomas = matrix(sample(1:puntos_en_la_red, poblacion*tamano_del_cromosoma, replace=TRUE), ncol=tamano_del_cromosoma);
#Calcula el fitness de los cromosomas
for(cromosoma in 1:poblacion){
fitness[cromosoma] = calcular_fitness(cromosomas[cromosoma, ], matriz_de_disimilaridad);
}
#Generando las generaciones
for(generacion in 1:generaciones){
if(generacion%%100 == 0){
print(mean(fitness));
print(generacion);
}
#Cruza los cromosomas de acuerdo a su fitness. Cuanto mas fitness mas probabilidad de cruza.
#Elige poblacion/2 parejas
if(poblacion > 1){
pareja_actual = 1; #Indice de la nueva pareja en cada cruza
for(cruza in cruzas){
#Elige la pareja a cruzar
pareja = elegir_pareja(fitness);
#La cruza y genera dos hijos
hijos = cruzar(cromosomas[pareja, ], pc);
#Asigna a la nueva poblacion los dos hijos
cromosomas_nuevos[pareja_actual, ] = hijos[1, ];
cromosomas_nuevos[pareja_actual+1, ] = hijos[2, ];
#Agrega dos al indice de nuevas parejas
pareja_actual = pareja_actual + 2;
}
#Asignamos la nueva poblacion como la poblacion actual
cromosomas = cromosomas_nuevos;
}
#Mutamos los nuevos cromosomas
for(cromosoma in 1:poblacion){
cromosoma_nuevo = mutar(cromosomas[cromosoma, ], pm, k);
fitness_nuevo = calcular_fitness(cromosoma_nuevo, matriz_de_disimilaridad);
#if(fitness_nuevo > fitness[cromosoma] || runif(1) > exp(-(fitness_nuevo-fitness[cromosoma]))) {
if(fitness_nuevo > fitness[cromosoma]) {
cromosomas[cromosoma, ] = cromosoma_nuevo;
fitness[cromosoma] = fitness_nuevo;
}
}
}
}
#Muestra los mejores fitness
graphics.off();
print(cromosomas);
plot(puntos[,1],puntos[,2]);
points(puntos[,1],puntos[,2],col=rainbow(k)[cromosomas[1, ]],pch=20);
dev.new();
plot(silhouette(cromosomas[which.max(fitness), ], matriz_de_disimilaridad));
|
79859c428fb939c528c8a114845ced2d61eeb64a
|
e58cb0a3ce95401501f0f0441a492529632b41f7
|
/submitJobs.R
|
f4f6aa06672ff707953199a0aefe1b847fd36c5f
|
[] |
no_license
|
larsgr/GRewdPipeline
|
ea451c75b5f4d91d4f92a941e3b2f3461566ee98
|
77a7d5b17373a2139d34327942fcec300b62fb40
|
refs/heads/master
| 2020-12-29T02:19:46.034273
| 2019-01-15T10:24:33
| 2019-01-15T10:24:33
| 30,870,561
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,834
|
r
|
submitJobs.R
|
#
# NOTE: system("source /etc/profile.d/modules.sh && sbatch jobscript.sh") does not work!
#
# The module command is not available to "jobscript.sh".. why not?
# apparently the job script does inherit the environment where sbatch is executed.
#
# Need to add #!/bin/bash –l at the start of each job script...
submitJob <- function(jobScript, deps=NULL){
if(is.null(deps)){
cmd <- paste("sbatch",jobScript)
} else {
cmd <- paste0("sbatch -d afterok:",paste(deps,collapse=":")," ",jobScript)
}
cat("Submit job command:",cmd,"\n")
jobIDstr <- system(paste("source /etc/profile.d/modules.sh && module load slurm &&",cmd),intern=T)
cat(jobIDstr,"\n")
# Output from sbatch: "Submitted batch job 1284123"
if( !( length(jobIDstr) == 1 & grepl("^Submitted batch job [0-9]+$",jobIDstr))){
stop("Job submit failed!")
}
jobID <- stringr::str_extract(jobIDstr,"[0-9]+")
return(jobID)
}
#
# orthoMCL.prepOrthoMCL.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/orthoMCL/prepOrthoMCL.job.sh",
# deps=NULL)
#
# orthoMCL.blastOrthoMCL.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/orthoMCL/blastOrthoMCL.job.sh",
# deps=orthoMCL.prepOrthoMCL.jobID)
#
#
# orthoMCL.orthoMCL.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/orthoMCL/orthoMCL.job.sh",
# deps=orthoMCL.blastOrthoMCL.jobID)
#
#
# ExTbls.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/exprTbls/ExTbls.job.sh",
# deps=orthoMCL.orthoMCL.jobID)
#
# runDESeq.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/DESeq/runDESeq.job.sh",
# deps=ExTbls.jobID)
#
# grpFastas.RJob.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/grpFastas/RJob.job.sh",
# deps=orthoMCL.orthoMCL.jobID)
#
# grpCDSFastas.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/grpCDSFastas/grpCDSFastas.job.sh",
# deps=orthoMCL.orthoMCL.jobID)
#
# prepMAFFT.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/grpAligned/prepMAFFT.job.sh",
# deps=grpFastas.RJob.jobID)
#
# MAFFT.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/grpAligned/MAFFT.job.sh",
# deps=prepMAFFT.jobID)
#
# pal2nal.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/pal2nal/pal2nal.job.sh",
# deps=c(MAFFT.jobID,grpCDSFastas.jobID))
#
# treesNuc.jobID <- submitJob(
# jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/treesNuc/nucTree.job.sh",
# deps=pal2nal.jobID)
splitGroups.jobID <- submitJob(
jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/splitGroups/splitGroups.job.sh",
deps=treesNuc.jobID)
PAML.jobID <- submitJob(
jobScript = "/mnt/NOBACKUP/mariansc/share/orthos/PAML/codeml.job.sh",
deps=splitGroups.jobID)
|
b14f34c23409a8bd39f03d6a41867ddbdae7712a
|
0420fa9f0a499af5edc75c7ddbe606a7ea684e6c
|
/data-raw/deprecated/cairo-header-old.R
|
d83a469983c17ad9e9faa3ff0ca64a6ca8f7ee90
|
[
"MIT"
] |
permissive
|
coolbutuseless/cairocore
|
ca8a2b82f594b086c7de38149fb3d3a5f150b455
|
5bf9f2f170f3c7ee81e0b5c709946745950115d4
|
refs/heads/master
| 2022-12-03T18:36:56.909944
| 2020-08-30T04:04:22
| 2020-08-30T04:04:22
| 288,104,420
| 12
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 21,190
|
r
|
cairo-header-old.R
|
cairo_header <- list(
cairo_create <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_create:
* @target: target surface for the context
*
* Creates a new #cairo_t with all graphics state parameters set to
* default values and with @target as a target surface. The target
* surface should be constructed with a backend-specific function such
* as cairo_image_surface_create() (or any other
* <function>cairo_<emphasis>backend</emphasis>_surface_create(<!-- -->)</function>
* variant).
*
* This function references @target, so you can immediately
* call cairo_surface_destroy() on it if you don't need to
* maintain a separate reference to it.
*
* Return value: a newly allocated #cairo_t with a reference
* count of 1. The initial reference count should be released
* with cairo_destroy() when you are done using the #cairo_t.
* This function never returns %NULL. If memory cannot be
* allocated, a special #cairo_t object will be returned on
* which cairo_status() returns %CAIRO_STATUS_NO_MEMORY. If
* you attempt to target a surface which does not support
* writing (such as #cairo_mime_surface_t) then a
* %CAIRO_STATUS_WRITE_ERROR will be raised. You can use this
* object normally, but no drawing will be done.
*
* Since: 1.0
**/}",
proto_text = "cairo_t * cairo_create (cairo_surface_t *target)"
),
cairo_move_to <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_move_to:
* @cr: a cairo context
* @x: the X coordinate of the new position
* @y: the Y coordinate of the new position
*
* Begin a new sub-path. After this call the current point will be (@x,
* @y).
*
* Since: 1.0
**/}",
proto_text = "void cairo_move_to (cairo_t *cr, double x, double y)"
),
cairo_line_to <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_line_to:
* @cr: a cairo context
* @x: the X coordinate of the end of the new line
* @y: the Y coordinate of the end of the new line
*
* Adds a line to the path from the current point to position (@x, @y)
* in user-space coordinates. After this call the current point
* will be (@x, @y).
*
* If there is no current point before the call to cairo_line_to()
* this function will behave as cairo_move_to(@cr, @x, @y).
*
* Since: 1.0
**/}",
proto_text = "void cairo_line_to (cairo_t *cr, double x, double y)"
),
cairo_rectangle <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_rectangle:
* @cr: a cairo context
* @x: the X coordinate of the top left corner of the rectangle
* @y: the Y coordinate to the top left corner of the rectangle
* @width: the width of the rectangle
* @height: the height of the rectangle
*
* Adds a closed sub-path rectangle of the given size to the current
* path at position (@x, @y) in user-space coordinates.
*
* This function is logically equivalent to:
* <informalexample><programlisting>
* cairo_move_to (cr, x, y);
* cairo_rel_line_to (cr, width, 0);
* cairo_rel_line_to (cr, 0, height);
* cairo_rel_line_to (cr, -width, 0);
* cairo_close_path (cr);
* </programlisting></informalexample>
*
* Since: 1.0
**/}",
proto_text = "void
cairo_rectangle (cairo_t *cr,
double x, double y,
double width, double height)"
),
cairo_paint <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_paint:
* @cr: a cairo context
*
* A drawing operator that paints the current source everywhere within
* the current clip region.
*
* Since: 1.0
**/}",
proto_text = "void
cairo_paint (cairo_t *cr)"
),
cairo_select_font_face <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_select_font_face:
* @cr: a #cairo_t
* @family: a font family name, encoded in UTF-8
* @slant: the slant for the font
* @weight: the weight for the font
*
* Note: The cairo_select_font_face() function call is part of what
* the cairo designers call the "toy" text API. It is convenient for
* short demos and simple programs, but it is not expected to be
* adequate for serious text-using applications.
*
* Selects a family and style of font from a simplified description as
* a family name, slant and weight. Cairo provides no operation to
* list available family names on the system (this is a "toy",
* remember), but the standard CSS2 generic family names, ("serif",
* "sans-serif", "cursive", "fantasy", "monospace"), are likely to
* work as expected.
*
* If @family starts with the string "@cairo:", or if no native font
* backends are compiled in, cairo will use an internal font family.
* The internal font family recognizes many modifiers in the @family
* string, most notably, it recognizes the string "monospace". That is,
* the family name "@cairo:monospace" will use the monospace version of
* the internal font family.
*
* For "real" font selection, see the font-backend-specific
* font_face_create functions for the font backend you are using. (For
* example, if you are using the freetype-based cairo-ft font backend,
* see cairo_ft_font_face_create_for_ft_face() or
* cairo_ft_font_face_create_for_pattern().) The resulting font face
* could then be used with cairo_scaled_font_create() and
* cairo_set_scaled_font().
*
* Similarly, when using the "real" font support, you can call
* directly into the underlying font system, (such as fontconfig or
* freetype), for operations such as listing available fonts, etc.
*
* It is expected that most applications will need to use a more
* comprehensive font handling and text layout library, (for example,
* pango), in conjunction with cairo.
*
* If text is drawn without a call to cairo_select_font_face(), (nor
* cairo_set_font_face() nor cairo_set_scaled_font()), the default
* family is platform-specific, but is essentially "sans-serif".
* Default slant is %CAIRO_FONT_SLANT_NORMAL, and default weight is
* %CAIRO_FONT_WEIGHT_NORMAL.
*
* This function is equivalent to a call to cairo_toy_font_face_create()
* followed by cairo_set_font_face().
*
* Since: 1.0
**/}",
proto_text = "void
cairo_select_font_face (cairo_t *cr,
const char *family,
cairo_font_slant_t slant,
cairo_font_weight_t weight)"
),
cairo_set_font_size <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_set_font_size:
* @cr: a #cairo_t
* @size: the new font size, in user space units
*
* Sets the current font matrix to a scale by a factor of @size, replacing
* any font matrix previously set with cairo_set_font_size() or
* cairo_set_font_matrix(). This results in a font size of @size user space
* units. (More precisely, this matrix will result in the font's
* em-square being a @size by @size square in user space.)
*
* If text is drawn without a call to cairo_set_font_size(), (nor
* cairo_set_font_matrix() nor cairo_set_scaled_font()), the default
* font size is 10.0.
*
* Since: 1.0
**/}",
proto_text = "void
cairo_set_font_size (cairo_t *cr, double size)"
),
cairo_show_text <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_show_text:
* @cr: a cairo context
* @utf8: a NUL-terminated string of text encoded in UTF-8, or %NULL
*
* A drawing operator that generates the shape from a string of UTF-8
* characters, rendered according to the current font_face, font_size
* (font_matrix), and font_options.
*
* This function first computes a set of glyphs for the string of
* text. The first glyph is placed so that its origin is at the
* current point. The origin of each subsequent glyph is offset from
* that of the previous glyph by the advance values of the previous
* glyph.
*
* After this call the current point is moved to the origin of where
* the next glyph would be placed in this same progression. That is,
* the current point will be at the origin of the final glyph offset
* by its advance values. This allows for easy display of a single
* logical string with multiple calls to cairo_show_text().
*
* Note: The cairo_show_text() function call is part of what the cairo
* designers call the "toy" text API. It is convenient for short demos
* and simple programs, but it is not expected to be adequate for
* serious text-using applications. See cairo_show_glyphs() for the
* "real" text display API in cairo.
*
* Since: 1.0
**/}",
proto_text = "void cairo_show_text (cairo_t *cr, const char *utf8)"
),
cairo_set_source_rgb <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_set_source_rgb:
* @cr: a cairo context
* @red: red component of color
* @green: green component of color
* @blue: blue component of color
*
* Sets the source pattern within @cr to an opaque color. This opaque
* color will then be used for any subsequent drawing operation until
* a new source pattern is set.
*
* The color components are floating point numbers in the range 0 to
* 1. If the values passed in are outside that range, they will be
* clamped.
*
* The default source pattern is opaque black, (that is, it is
* equivalent to cairo_set_source_rgb(cr, 0.0, 0.0, 0.0)).
*
* Since: 1.0
**/}",
proto_text = "void cairo_set_source_rgb (cairo_t *cr, double red, double green, double blue)"
),
cairo_set_antialias <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_set_antialias:
* @cr: a #cairo_t
* @antialias: the new antialiasing mode
*
* Set the antialiasing mode of the rasterizer used for drawing shapes.
* This value is a hint, and a particular backend may or may not support
* a particular value. At the current time, no backend supports
* %CAIRO_ANTIALIAS_SUBPIXEL when drawing shapes.
*
* Note that this option does not affect text rendering, instead see
* cairo_font_options_set_antialias().
*
* Since: 1.0
**/}",
proto_text = "void cairo_set_antialias (cairo_t *cr, cairo_antialias_t antialias)"
),
cairo_set_fill_rule <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_fill_rule_t:
* @CAIRO_FILL_RULE_WINDING: If the path crosses the ray from
* left-to-right, counts +1. If the path crosses the ray
* from right to left, counts -1. (Left and right are determined
* from the perspective of looking along the ray from the starting
* point.) If the total count is non-zero, the point will be filled. (Since 1.0)
* @CAIRO_FILL_RULE_EVEN_ODD: Counts the total number of
* intersections, without regard to the orientation of the contour. If
* the total number of intersections is odd, the point will be
* filled. (Since 1.0)
*
* #cairo_fill_rule_t is used to select how paths are filled. For both
* fill rules, whether or not a point is included in the fill is
* determined by taking a ray from that point to infinity and looking
* at intersections with the path. The ray can be in any direction,
* as long as it doesn't pass through the end point of a segment
* or have a tricky intersection such as intersecting tangent to the path.
* (Note that filling is not actually implemented in this way. This
* is just a description of the rule that is applied.)
*
* The default fill rule is %CAIRO_FILL_RULE_WINDING.
*
* New entries may be added in future versions.
*
* Since: 1.0
**/}",
proto_text = "void cairo_set_fill_rule (cairo_t *cr, cairo_fill_rule_t fill_rule);"
),
cairo_set_line_width <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_fill_rule_t:
* @CAIRO_FILL_RULE_WINDING: If the path crosses the ray from
* left-to-right, counts +1. If the path crosses the ray
* from right to left, counts -1. (Left and right are determined
* from the perspective of looking along the ray from the starting
* point.) If the total count is non-zero, the point will be filled. (Since 1.0)
* @CAIRO_FILL_RULE_EVEN_ODD: Counts the total number of
* intersections, without regard to the orientation of the contour. If
* the total number of intersections is odd, the point will be
* filled. (Since 1.0)
*
* #cairo_fill_rule_t is used to select how paths are filled. For both
* fill rules, whether or not a point is included in the fill is
* determined by taking a ray from that point to infinity and looking
* at intersections with the path. The ray can be in any direction,
* as long as it doesn't pass through the end point of a segment
* or have a tricky intersection such as intersecting tangent to the path.
* (Note that filling is not actually implemented in this way. This
* is just a description of the rule that is applied.)
*
* The default fill rule is %CAIRO_FILL_RULE_WINDING.
*
* New entries may be added in future versions.
*
* Since: 1.0
**/}",
proto_text = "void cairo_set_line_width (cairo_t *cr, double width)"
),
cairo_stroke_preserve <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_stroke_preserve:
* @cr: a cairo context
*
* A drawing operator that strokes the current path according to the
* current line width, line join, line cap, and dash settings. Unlike
* cairo_stroke(), cairo_stroke_preserve() preserves the path within the
* cairo context.
*
* See cairo_set_line_width(), cairo_set_line_join(),
* cairo_set_line_cap(), cairo_set_dash(), and
* cairo_stroke_preserve().
*
* Since: 1.0
**/}",
proto_text = "void cairo_stroke_preserve (cairo_t *cr)"
),
cairo_fill <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_fill:
* @cr: a cairo context
*
* A drawing operator that fills the current path according to the
* current fill rule, (each sub-path is implicitly closed before being
* filled). After cairo_fill(), the current path will be cleared from
* the cairo context. See cairo_set_fill_rule() and
* cairo_fill_preserve().
*
* Since: 1.0
**/}",
proto_text = "void cairo_fill (cairo_t *cr)"
),
cairo_arc <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_arc:
* @cr: a cairo context
* @xc: X position of the center of the arc
* @yc: Y position of the center of the arc
* @radius: the radius of the arc
* @angle1: the start angle, in radians
* @angle2: the end angle, in radians
*
* Adds a circular arc of the given @radius to the current path. The
* arc is centered at (@xc, @yc), begins at @angle1 and proceeds in
* the direction of increasing angles to end at @angle2. If @angle2 is
* less than @angle1 it will be progressively increased by
* <literal>2*M_PI</literal> until it is greater than @angle1.
*
* If there is a current point, an initial line segment will be added
* to the path to connect the current point to the beginning of the
* arc. If this initial line is undesired, it can be avoided by
* calling cairo_new_sub_path() before calling cairo_arc().
*
* Angles are measured in radians. An angle of 0.0 is in the direction
* of the positive X axis (in user space). An angle of
* <literal>M_PI/2.0</literal> radians (90 degrees) is in the
* direction of the positive Y axis (in user space). Angles increase
* in the direction from the positive X axis toward the positive Y
* axis. So with the default transformation matrix, angles increase in
* a clockwise direction.
*
* (To convert from degrees to radians, use <literal>degrees * (M_PI /
* 180.)</literal>.)
*
* This function gives the arc in the direction of increasing angles;
* see cairo_arc_negative() to get the arc in the direction of
* decreasing angles.
*
* The arc is circular in user space. To achieve an elliptical arc,
* you can scale the current transformation matrix by different
* amounts in the X and Y directions. For example, to draw an ellipse
* in the box given by @x, @y, @width, @height:
*
* <informalexample><programlisting>
* cairo_save (cr);
* cairo_translate (cr, x + width / 2., y + height / 2.);
* cairo_scale (cr, width / 2., height / 2.);
* cairo_arc (cr, 0., 0., 1., 0., 2 * M_PI);
* cairo_restore (cr);
* </programlisting></informalexample>
*
* Since: 1.0
**/}",
proto_text = "void
cairo_arc (cairo_t *cr,
double xc, double yc,
double radius,
double angle1, double angle2)"
),
cairo_close_path <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_close_path:
* @cr: a cairo context
*
* Adds a line segment to the path from the current point to the
* beginning of the current sub-path, (the most recent point passed to
* cairo_move_to()), and closes this sub-path. After this call the
* current point will be at the joined endpoint of the sub-path.
*
* The behavior of cairo_close_path() is distinct from simply calling
* cairo_line_to() with the equivalent coordinate in the case of
* stroking. When a closed sub-path is stroked, there are no caps on
* the ends of the sub-path. Instead, there is a line join connecting
* the final and initial segments of the sub-path.
*
* If there is no current point before the call to cairo_close_path(),
* this function will have no effect.
*
* Note: As of cairo version 1.2.4 any call to cairo_close_path() will
* place an explicit MOVE_TO element into the path immediately after
* the CLOSE_PATH element, (which can be seen in cairo_copy_path() for
* example). This can simplify path processing in some cases as it may
* not be necessary to save the "last move_to point" during processing
* as the MOVE_TO immediately after the CLOSE_PATH will provide that
* point.
*
* Since: 1.0
**/}",
proto_text = "void cairo_close_path (cairo_t *cr)"
),
cairo_translate <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_translate:
* @cr: a cairo context
* @tx: amount to translate in the X direction
* @ty: amount to translate in the Y direction
*
* Modifies the current transformation matrix (CTM) by translating the
* user-space origin by (@tx, @ty). This offset is interpreted as a
* user-space coordinate according to the CTM in place before the new
* call to cairo_translate(). In other words, the translation of the
* user-space origin takes place after any existing transformation.
*
* Since: 1.0
**/}",
proto_text = "void cairo_translate (cairo_t *cr, double tx, double ty)"
),
cairo_scale <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_scale:
* @cr: a cairo context
* @sx: scale factor for the X dimension
* @sy: scale factor for the Y dimension
*
* Modifies the current transformation matrix (CTM) by scaling the X
* and Y user-space axes by @sx and @sy respectively. The scaling of
* the axes takes place after any existing transformation of user
* space.
*
* Since: 1.0
**/}",
proto_text = "void cairo_scale (cairo_t *cr, double sx, double sy)"
),
cairo_set_source_rgba <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_set_source_rgba:
* @cr: a cairo context
* @red: red component of color
* @green: green component of color
* @blue: blue component of color
* @alpha: alpha component of color
*
* Sets the source pattern within @cr to a translucent color. This
* color will then be used for any subsequent drawing operation until
* a new source pattern is set.
*
* The color and alpha components are floating point numbers in the
* range 0 to 1. If the values passed in are outside that range, they
* will be clamped.
*
* The default source pattern is opaque black, (that is, it is
* equivalent to cairo_set_source_rgba(cr, 0.0, 0.0, 0.0, 1.0)).
*
* Since: 1.0
**/}",
proto_text = "void
cairo_set_source_rgba (cairo_t *cr,
double red, double green, double blue,
double alpha)"
),
cairo_text_extents <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_text_extents:
* @cr: a #cairo_t
* @utf8: a NUL-terminated string of text encoded in UTF-8, or %NULL
* @extents: a #cairo_text_extents_t object into which the results
* will be stored
*
* Gets the extents for a string of text. The extents describe a
* user-space rectangle that encloses the "inked" portion of the text,
* (as it would be drawn by cairo_show_text()). Additionally, the
* x_advance and y_advance values indicate the amount by which the
* current point would be advanced by cairo_show_text().
*
* Note that whitespace characters do not directly contribute to the
* size of the rectangle (extents.width and extents.height). They do
* contribute indirectly by changing the position of non-whitespace
* characters. In particular, trailing whitespace characters are
* likely to not affect the size of the rectangle, though they will
* affect the x_advance and y_advance values.
*
* Since: 1.0
**/}",
proto_text = "void
cairo_text_extents (cairo_t *cr,
const char *utf8,
cairo_text_extents_t *extents)"
),
cairo_paint_with_alpha <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_paint_with_alpha:
* @cr: a cairo context
* @alpha: alpha value, between 0 (transparent) and 1 (opaque)
*
* A drawing operator that paints the current source everywhere within
* the current clip region using a mask of constant alpha value
* @alpha. The effect is similar to cairo_paint(), but the drawing
* is faded out using the alpha value.
*
* Since: 1.0
**/}",
proto_text = "void
cairo_paint_with_alpha (cairo_t *cr,
double alpha)"
)
)
xxx <- list(
include = TRUE,
gtkdoc = r"{}",
proto_text = ""
)
|
374d1f963849c37a2ba655db2c50ab19675f63dd
|
9bd0a8ccaad50f716a942192aa31bddc1644bfbd
|
/SableSurface_2022.R
|
b01e9ea5c86919c1d0d5582549137632c8ed23b6
|
[] |
no_license
|
arcticecology/Sable
|
4ab0df525ae8238cfd7c451431ac090906d4e26d
|
5e6ca0269b2fc59f7a3b5c234ed020aff82c1aff
|
refs/heads/main
| 2023-04-11T03:48:00.876984
| 2022-08-30T11:57:01
| 2022-08-30T11:57:01
| 530,626,321
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,920
|
r
|
SableSurface_2022.R
|
#The script to add norway to the conservative model because only RA is available
library(rioja)
library(analogue)
library(vegan)
library(labdsv)
setwd("C:\\Users\\Andrew\\Desktop\\Sable\\R\\surface")
species3<-read.csv(file="dataset_merge.csv", row.names=1) #read the csv file
species1<-species3[, -cbind(118:119) ] #this removes the taxa that are not good for modelling, undiff tanytarsini and such
species2 <- species1 / rowSums(species1) * 100
speciesX <- species2[, which(colSums(species2) != 0)]
species<-speciesX[, -cbind(116:117) ] #this removes the taxa that are not good for modelling, undiff tanytarsini and such
N <- 2
M <- 2
i <- colSums(speciesX >= M) >= N ## L1
species <- speciesX[, i, drop = FALSE]
tspecies <- decostand(species, method="hellinger") #transform data
#dca (b-diversity)
vare.dca <- decorana(tspecies)
vare.dca
summary(vare.dca)
plot(vare.dca)
plot(vare.dca, type = "n", xlim=c(-3,3), ylim=c(3,-3))
points(vare.dca, display = "sites", col = "black", cex = 1, pch = 21, bg = "red")
text(vare.dca, display="species", pos=2)
## load the data
coreinput_PD03<-read.csv(file="PD03m.csv", row.names=1)
coresp<-coreinput_PD03[, -cbind(1:3) ] #use this for cores
coresp1 <- coresp / rowSums(coresp) * 100
coresp2<-coresp1[, -cbind(117:118) ]
cols_to_keep <- intersect(colnames(species),colnames(coresp2))
PD03 <- coresp2[,cols_to_keep, drop=FALSE]
coreinput_PD67<-read.csv(file="PD67m.csv", row.names=1)
cores67<-coreinput_PD67[, -cbind(1:3) ] #use this for cores
coresp67 <- cores67 / rowSums(cores67) * 100
coresp677<-coresp67[, -cbind(117:118) ]
cols_to_keep <- intersect(colnames(species),colnames(coresp677))
PD67 <- coresp677[,cols_to_keep, drop=FALSE]
coreinput_PD37<-read.csv(file="PD37m.csv", row.names=1)
cores37<-coreinput_PD37[, -cbind(1:2) ] #use this for cores
coresp37 <- cores37 / rowSums(cores37) * 100
cols_to_keep <- intersect(colnames(PD67),colnames(coresp37))
PD37a <- coresp37[,cols_to_keep, drop=TRUE]
coreinput_PD38<-read.csv(file="PD38m.csv", row.names=1)
cores38<-coreinput_PD38[, -cbind(1:2) ] #use this for cores
coresp38 <- cores38 / rowSums(cores38) * 100
cols_to_keep <- intersect(colnames(PD67),colnames(coresp38))
PD38 <- coresp38[,cols_to_keep, drop=TRUE]
PD37 <-PD37a[ -cbind(9:15), ]
## Fit the timetrack ordination
core<-PD67
mod <- timetrack(species, core, transform = "hellinger",
method = "rda")
mod
## Plot the timetrack
plot(mod, ptype = "b", col = c("black", "red"), lwd = 2)
## Other options (reorder the time track)
ord <- rev(seq_len(nrow(core)))
plot(mod, choices = 2:3, order = ord, ptype = "b",
col = c("forestgreen", "orange"), lwd = 2)
## scores and fitted methods
## IGNORE_RDIFF_BEGIN
head(fitted(mod, type = "passive"))
head(scores(mod, type = "passive"))
## IGNORE_RDIFF_END
my.pca <- rda(tspecies, scale=FALSE)
biplot(my.pca, scaling = 3, type = c("text", "points"))
text(my.pca, display = "sites", scaling = 1, cex = 0.8, col = "darkcyan")
## predict locations in timetrack for new observations
mod3 <- timetrack(species, PD03, transform = "hellinger",
method = "rda")
mod4 <- timetrack(species, PD67, transform = "hellinger",
method = "rda", scaling=3)
mod5 <- timetrack(species, PD37, transform = "hellinger",
method = "rda", scaling=3)
plot(mod5)
mod6 <- timetrack(species, PD38, transform = "hellinger",
method = "rda", scaling=3)
plot(mod6)
mod <- timetrack(species, core, transform = "hellinger",
method = "rda", labels=species)
mod
plot(mod)
## Plot the timetrack
plot(mod, ptype = "b", col = c("black", "red"), lwd = 2)
plot(mod3, ptype = "b", col = c("black", "red"), lwd = 2)
plot(mod4, ptype = "b", col = c("black", "red"), lwd = 2)
plot(mod5, ptype = "b", col = c("black", "red"), lwd = 2)
library(survival)
library(grid)
library(gridGraphics)
plot(mod, type = "n", ptype = "b", xlim=c(-0.6,0.5), ylim=c(-0.6,0.5))
# capture the plotted output as a grob of PD67
grid.echo()
grid.grab() -> k
# pull out the data from the grob..
k$children$`graphics-plot-1-points-1`$x -> x
k$children$`graphics-plot-1-points-1`$y -> y
plot(mod5, type = "n", ptype = "b", xlim=c(-0.6,0.5), ylim=c(-0.6,0.5))
# capture the plotted output as a grob of PD37
grid.echo()
grid.grab() -> k2
# pull out the data from the grob..
k2$children$`graphics-plot-1-points-1`$x -> x2
k2$children$`graphics-plot-1-points-1`$y -> y2
plot(mod6, type = "n", ptype = "b", xlim=c(-0.6,0.5), ylim=c(-0.6,0.5))
# capture the plotted output as a grob of PD38
grid.echo()
grid.grab() -> k3
# pull out the data from the grob..
k3$children$`graphics-plot-1-points-1`$x -> x3
k3$children$`graphics-plot-1-points-1`$y -> y3
#plot(mod, type = "n", ptype = "b")
#points(mod, which = "ordination", col = "grey", pch = 19, cex = 0.7)
#lines(x,y, col=1)
#points(x,y)
plot(mod3, xlim=c(-0.6,0.5), ylim=c(-0.6,0.5))
points(mod3, which = "passive", col = "red")
points(mod4, which = "passive", col = "blue")
points(mod, which = "ordination", col = "grey", pch = 19, cex = 0.7)
lines(x,y, col="blue", lty=2)
points(mod5, which = "passive", col = "orange", pch = 21, cex = 1)
lines(x2,y2, col="orange", lty=3)
points(mod6, which = "passive", col = "black", pch = 21, cex = 1)
lines(x3,y3, col="black", lty=4)
text(my.pca, display = "sites", scaling=3, pos=4, cex = 0.8, col = "darkcyan")
text(my.pca, display="species", scaling=3)
legend("bottomleft", inset =0.05,legend=c("PD03", "PD67", "PD37", "PD38"),
col=c("red", "blue", "orange", "black"), lty=1:4, cex=0.8,
box.lty=0)
tiff("SableSurface_b.tiff", width = 7, height = 7, units = 'in', res = 300)
plot(mod3, xlim=c(-0.6,0.5), ylim=c(-0.6,0.5), lwd=2)
points(mod3, which = "passive", col = "red")
points(mod4, which = "passive", col = "blue")
points(mod, which = "ordination", col = "grey", pch = 19, cex = 0.7)
lines(x,y, col="blue", lty=2, lwd=2)
points(mod5, which = "passive", col = "brown", pch = 21, cex = 1)
lines(x2,y2, col="brown", lty=3, lwd=3)
points(mod6, which = "passive", col = "black", pch = 21, cex = 1)
lines(x3,y3, col="black", lty=4, lwd=2)
text(my.pca, display = "sites", scaling=3, pos=4, cex = 0.8, col = "darkcyan")
#text(my.pca, display="species", scaling=3)
legend("bottomleft", inset =0.05,legend=c("PD03", "PD67", "PD37", "PD38"),
col=c("red", "blue", "brown", "black"), lty=1:4, cex=0.8, lwd=2,
box.lty=0)
dev.off()
#stratigraphy
#fos<-PD03
fos<-PD67
#fos<-PD37
#fos<-PD38
yPD03<-coreinput_PD03[1:2]
colnames(yPD03) <- c("Depth","Year")
yPD67<-coreinput_PD67[1:2]
colnames(yPD67) <- c("Depth","Year")
yPD37<-coreinput_PD37[1:2]
colnames(yPD37) <- c("Depth","Year")
yPD38<-coreinput_PD38[1:2]
colnames(yPD38) <- c("Depth","Year")
yDepth<-yPD67$Depth
yYear<- yPD67$Year
#yDepth<-yPD03$Depth
#yYear<- yPD03$Year
#yDepth<-yPD37$Depth
#yYear<- yPD37$Year
#yDepth<-yPD38$Depth
#yYear<- yPD38$Year
nms <- colnames(fos)
nms3<-gsub("\\.", " ", nms)
diss <- dist(sqrt(fos/100)^2)
clust <- chclust(diss, method="coniss")
diss <- dist(sqrt(fos/100)^2)
clust <- chclust(diss)
bstick(clust, 5)
library(NbClust)
library(ggpalaeo)
sp <- strat.plot(fos, scale.percent=TRUE, yvar=yDepth,
title="PG Lake", ylabel="", srt.xlabel=45, col.bar=1,
plot.line=FALSE, plot.bar = TRUE, lwd.bar=4, y.rev=TRUE,
xLeft=0.18, xRight=0.75, y.axis=0.1, x.names=nms3, yTop=0.68, cex.yaxis=1,
cex.ylabel=1, cex.xlabel=1.0, cex.axis=1, clust=clust)
secondary_scale(sp, yvar = yDepth, yvar2 = yYear, n = 15, ylabel2 = "age (CE)")
addClustZone(sp, clust, 3, col="black", lty=2, lwd=2)
|
956bdb76bff4acf2e5546d3d3465c2f415ac3725
|
ac82cd0243a121e3749f7dcfb3f31d2fa33b2a9f
|
/R_Programming/exploring_linux_evolution.R
|
244e28a8d820f877c19f428a0c405ec05975ee14
|
[
"MIT"
] |
permissive
|
arpithaupd/Data-Science-Projects
|
63aa24db88b85f82d2b3e79510fce77f6b596cbf
|
423695c130a92db1a188b3d3a13871f0f76f6f5b
|
refs/heads/master
| 2023-04-29T08:54:42.834503
| 2021-04-21T16:12:23
| 2021-04-21T16:12:23
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,446
|
r
|
exploring_linux_evolution.R
|
# R script to perform the data analysis and visualization of the Linux evolution
# Tasks:
# 1. Identifying the TOP 10 contributors to Linux repository
# 2. Visualizing the commits over the years to Linux repository
#----------------- Reading the dataset
log = read.csv('git_log.csv', header = TRUE, sep = ",")
head(log) # Display first 6 rows of dataset
summary(log) # Summary of the dataset
str(log) # Structure of dataset
#-----------------Part 1: Data Analysis
# 1. calculating number of commits
num.commits <- nrow(log)
# 2. calculating number of authors
num.authors <- length(unique(log$author))
paste(num.authors, ' authors committed ', num.commits,' code changes.')
# 3. TOP 10 contributors that changed the Linux kernel very often.
freq.data <- table(log$author)
top10 <- as.data.frame(head(sort(freq.data, decreasing = T), 10))
colnames(top10) <- c('Authors', 'Commits')
top10
#----------------Part 2: Data Visualization
# use the information in the timestamp column to create a time series-based column
if (!require("lubridate")) install.packages("lubridate")
library(lubridate)
log$corrected_timestamp <- as.POSIXlt(log$timestamp, origin = "1970-01-01 00:00:00", tz = "UTC")
log$timestamp <- NULL
summary(log)
# Determining first real commit timestamp
first.commit <- tail(log, 1)$corrected_timestamp
# determining the last sensible commit timestamp
last.commit <- today(tzone = "UTC")
# filtering out wrong timestamps
corrected.log <- log[(log$corrected_timestamp >= first.commit) & (log$corrected_timestamp <= last.commit), ]
head(corrected.log)
summary(corrected.log)
# group the commits by year
corrected.log$Year <- year(ymd(as.Date(corrected.log$corrected_timestamp)))
corrected.log
if (!require("dplyr")) install.packages("dplyr")
library(dplyr)
visual.log <- corrected.log %>% group_by(Year) %>% summarise(author = n())
colnames(visual.log) <- c('Year', 'Commits')
visual.log
if (!require("ggplot2")) install.packages("ggplot2")
library(ggplot2)
q <- ggplot(visual.log, aes(x = Year, y = Commits)) +
geom_line(color="blue", size=1.2) + geom_point()
q + xlab('Year') + ylab('Commits') +
ggtitle('Commits to Linux repo over the years ') +
theme(axis.title.x = element_text(size = 15),
axis.title.y = element_text(size = 15),
plot.title = element_text(hjust = 0.5, size = 20))
# removing all the unnecessary data from environment
rm(corrected.log, first.commit, last.commit, freq.data)
|
a464ab7bce49653298e3a4b3238148f8b15ee36a
|
5cdd72710fe3a519859bac1a40fd68723330b2ef
|
/man/latlon_to_km.Rd
|
ea9483dc6e92eddddbfaa3ea2ec01192abdb3a3e
|
[] |
no_license
|
cran/stormwindmodel
|
75b81aa790bfbe30afc16d1c0ebe8bf8251b4b24
|
59a439b1592cef52ea2d03cb192fa70b033322c0
|
refs/heads/master
| 2021-01-12T01:56:16.406086
| 2020-07-27T06:10:02
| 2020-07-27T06:10:02
| 78,445,768
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,118
|
rd
|
latlon_to_km.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/latlon_helpers.R
\name{latlon_to_km}
\alias{latlon_to_km}
\title{Calculate distance between two locations}
\usage{
latlon_to_km(tclat_1, tclon_1, tclat_2, tclon_2, Rearth = 6378.14)
}
\arguments{
\item{tclat_1}{A numeric vector giving latitude of the first location
(degrees)}
\item{tclon_1}{A numeric vector giving longitude of the first location
(degrees). This value should be expressed as a positive value for Western
hemisphere longitudes.}
\item{tclat_2}{A numeric vector giving latitude of the second location
(degrees)}
\item{tclon_2}{A numeric vector giving longitude of the second location
(degrees). This value should be expressed as a positive value for Western
hemisphere longitudes.}
\item{Rearth}{Radius of the earth (km). Default is 6378.14 km.}
}
\value{
A vector with the distance between the two locations, in kilometers.
}
\description{
This function takes latitudes and longitudes for two locations and
calculates the distance (in meters) between the locations using the
Haversine method.
}
\details{
This function uses the Haversine method with great circle distance
to calculate this distance. It is applying the following equations to
determine distance (in kilometers) between two latitude-longitude pairs:
\deqn{hav(\gamma) = hav(\phi_1 - \phi_2) + cos(\phi_1)*cos(\phi_2)*hav(L_1 - L_2)}{
hav(\gamma) = hav(\phi1 - \phi2) + cos(\phi1)*cos(\phi2)*hav(L1 - L2)}
where:
\itemize{
\item{\eqn{\phi_1}{\phi1}: Latitude of first location, in radians}
\item{\eqn{\phi_2}{\phi2}: Latitude of second location, in radians}
\item{\eqn{L_1}{L1}: Longitude of first location, in radians}
\item{\eqn{L_2}{L2}: Longitude of second location, in radians}
\item{\eqn{hav(\gamma)}: The haversine function,
\eqn{hav(\gamma) = sin^2 \left(\frac{\gamma}{2}\right)}{
hav(\gamma) = sin^2 (\gamma / 2)}}
\item{\eqn{R_earth}{Rearth}: Radius of the earth, here assumed to be 6378.14 kilometers}
\item{\eqn{D}}: Distance between the two locations, in kilometers
}
}
|
82f4f5ffd021ebb6e6428b2c70dacd759452c508
|
c3ed0eea77de3338cc4820ca27dc04384676b29b
|
/man/rescale_capped.Rd
|
9a07036b85ac58b6630e3109368c3e25258f0701
|
[] |
no_license
|
jrboyd/seqtsne
|
f3bd94ee23140dc71373af4f1e7ce9ffe60d702f
|
5a67cbe5af281ec42906689d1a9961d8fe9de68d
|
refs/heads/master
| 2022-10-30T18:33:13.005047
| 2022-10-28T20:22:58
| 2022-10-28T20:22:58
| 177,857,441
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,070
|
rd
|
rescale_capped.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions_util.R
\name{rescale_capped}
\alias{rescale_capped}
\title{wraps scales::rescale to enforce range of \code{to} on output}
\usage{
rescale_capped(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE))
}
\arguments{
\item{x}{continuous vector of values to manipulate.}
\item{to}{output range (numeric vector of length two)}
\item{from}{input range (vector of length two). If not given, is calculated from the range of x}
}
\value{
x rescaled from \code{from} domain to \code{to} domain, within bounds of \code{to}
}
\description{
wraps scales::rescale to enforce range of \code{to} on output
}
\examples{
#behaves identically to scales::rescale when x is within 'from' domain
chiptsne:::rescale_capped(0:10, to = c(0, 1), c(0, 10))
scales::rescale(0:10, to = c(0, 1), c(0, 10))
#when x exceeds 'from' domain, results are still within 'to' domain
chiptsne:::rescale_capped(0:10, to = c(0,1), c(0,5))
#not true for scales::rescale
scales::rescale(0:10, to = c(0,1), c(0,5))
}
|
ec26e819680a50353f148d5713b36799de04bf51
|
3de27dccf8fdf1855fdacf6c71b138e1b0c48f0e
|
/L-13.R
|
ba53c039298aa506a261a2a99bd588488417127b
|
[] |
no_license
|
tongramt/Forecasting
|
2a909478af578eb5a9c64ae9779362f33320b0ec
|
f9c1478bcbe0a83befadddf03b64ccbaad9efd28
|
refs/heads/main
| 2023-01-23T17:27:26.631733
| 2020-11-30T09:46:03
| 2020-11-30T09:46:03
| 311,314,162
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 243
|
r
|
L-13.R
|
# Title : Lecture 13
# Objective : Write and annotate code for lecture 13
# Created by: ThomasGrant
# Created on: 29/10/2020
require("fma")
tslm.airpass<-tslm(airpass~ season)
tslm.airpass
Arima(dowjones,order=c(0,1,0),include.mean=FALSE)
|
8a871a8738acbf0b92ced79bbc62327c321ff211
|
e6a9d96db33e6cd819e4f1dbd60c80b0eb53a615
|
/man/fit_agTrend_ssl_mgcv.Rd
|
c528b310e40ad08314e5a3c11609a5ea11002fe2
|
[] |
no_license
|
dsjohnson/agTrendTMB
|
42384a16831a7f21b4bfe61ad61075b6555cac8f
|
403c55da56e891c8126b912ab8958346d7f1d3c8
|
refs/heads/master
| 2023-01-10T19:30:07.323254
| 2020-11-10T01:19:36
| 2020-11-10T01:19:36
| 307,476,720
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,472
|
rd
|
fit_agTrend_ssl_mgcv.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fit_agTrend_ssl_mgcv.R
\name{fit_agTrend_ssl_mgcv}
\alias{fit_agTrend_ssl_mgcv}
\title{Site-level fitting function}
\usage{
fit_agTrend_ssl_mgcv(.x, obl.corr = FALSE, debug = FALSE)
}
\arguments{
\item{.x}{Data frame for an individual site. Must have been passed through \cite{prep_for_fit} first!}
\item{obl.corr}{Logical. Should correction be made for oblique photos.}
\item{debug}{Logical. Step into fitting function for interactive evaluation}
\item{model}{Model for interpolation: 'const', 'lin', or 'gp' (see Details)}
}
\value{
A list with the following elements:
\itemize{
\item summary Parameter and abundance estimate information?
\item raw_data Original data plus oblique photo correction estimates
\item q_output quality control diagnostic data
}
}
\description{
Fit separate discrete-normal distribution models to an Steller sea lion counts at a single site
}
\details{
There are 3 models which can be used for within site interpolation:
\itemize{
\item \code{const} This interpolates using a simple mean and variance for the site
\item \code{lin} A linear fit with time is used for interpolation
\item \code{gp} i.e., Gaussian Process. This model used a time trend plus a random walk of order 2 (RW(2)) to interpolate
counts. Further, the RW(2) process can be further restrained to minimize 'wiggliness' of the curve when necessary. This
prevents overfitting of the curve.
}
}
|
1483c90d8326c4cc9daf0ad99b2a70f10357dad5
|
0b7e9f17d32d84558b95321d92177b7e4532ffdb
|
/R/match_and_append.R
|
919e0205abb2b8a9c83540c0057c702ce854ed9a
|
[] |
no_license
|
agualtieri/mcla_cleaneR
|
0de5d5a40334e1f2e74b028dd9dff8bb402c97fb
|
397225a064b486bc48020f4f0d3286a266c862b0
|
refs/heads/master
| 2020-05-23T13:52:37.834105
| 2019-09-19T12:03:08
| 2019-09-19T12:03:08
| 186,787,354
| 0
| 6
| null | 2019-07-10T09:25:23
| 2019-05-15T08:50:52
|
R
|
UTF-8
|
R
| false
| false
| 689
|
r
|
match_and_append.R
|
match_and_append <- function(df, source_df, df_variable_for_match, source_df_variable_for_match, variable_to_append){
#setNames(variable_for_match, "variable")
#setNames(variable_to_append, "variable")
df$appended_var <- source_df$variable_to_append[match(df$variable_for_match, source_df$variable_for_match)]
df
}
test <- match_and_append(cleaning_log_melt,
conditions_list,
"variable",
"check_names",
"conditions")
cleaning_log_melt$quality_checks <- conditions_list$conditions[match(cleaning_log_melt$variable, conditions_list$check_names)]
rm(test)
|
3b5787ea7aa266e4797e7427fb628b7c085c2943
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/phonTools/examples/spectralslice.Rd.R
|
a206ae99f4098cf19998b64f6640c02242aeb335
|
[] |
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
| 450
|
r
|
spectralslice.Rd.R
|
library(phonTools)
### Name: spectralslice
### Title: Spectral Slice
### Aliases: spectralslice slice
### ** Examples
## synthesize schwa-like vowel
vowel = vowelsynth (ffs = c(500,1500,2500,3500,4500))$sound
## compare window lengths
par (mfrow = c(3,1))
spectralslice (vowel[500:550], fs = 10000)
spectralslice (vowel[500:1000], fs = 10000)
## line spectrum
spectralslice (vowel[500:600], padding = 0, line = TRUE, fs = 10000)
|
753442eb1321d43c3c14163c8a2f94ffb846db08
|
ffcc3072f9a5190b068f296a6d22404bee24eeb0
|
/man/ocrTesseract.Rd
|
474a174e2742b72bd532a55725d0ab337cac500d
|
[
"Apache-2.0"
] |
permissive
|
greenore/ocR
|
213661192761fc5fdc9ab7f376a03663737c6e66
|
d47d0b5db1d487f42399be361e11ef98c305fce2
|
refs/heads/master
| 2021-01-20T12:04:29.617030
| 2014-12-18T14:51:32
| 2014-12-18T14:51:32
| 26,242,115
| 6
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,377
|
rd
|
ocrTesseract.Rd
|
% Generated by roxygen2 (4.0.2): do not edit by hand
\name{ocrTesseract}
\alias{ocrTesseract}
\title{OCR with tesseract}
\usage{
ocrTesseract(dir_path, image_name, output_base, lang = "eng", psm = 5)
}
\arguments{
\item{dir_path}{Path to the directory where the input and output files are.}
\item{image_name}{The name of the input image. Most image file formats are supported.}
\item{output_base}{The basename of the output file (to which the appropriate extension will be appended).}
\item{lang}{The language to use. If none is specified, English is assumed. Multiple languages may be specified, separated by plus characters. Tesseract uses 3-character ISO 639-2 language codes:
\describe{
\item{}{ara (Arabic), aze (Azerbauijani), bul (Bulgarian), cat (Catalan), ces (Czech), chi_sim (Simplified Chinese), chi_tra (Traditional Chinese), chr (Cherokee), dan (Danish), dan-frak (Danish (Fraktur)), deu (German), ell (Greek), eng (English), enm (Old English), epo (Esperanto), est (Estonian), fin (Finnish), fra (French), frm (Old French), glg (Galician), heb (Hebrew), hin (Hindi), hrv (Croation), hun (Hungarian), ind (Indonesian), ita (Italian), jpn (Japanese), kor (Korean), lav (Latvian), lit (Lithuanian), nld (Dutch), nor (Norwegian), pol (Polish), por (Portuguese), ron (Romanian), rus (Russian), slk (Slovakian), slv (Slovenian), sqi (Albanian), spa (Spanish), srp (Serbian), swe (Swedish), tam (Tamil), tel (Telugu), tgl (Tagalog), tha (Thai), tur (Turkish), ukr (Ukrainian), vie (Vietnamese)}
}}
\item{psm}{Set Tesseract to only run a subset of layout analysis and assume a certain form of image. The options for N are:
\itemize{
\item{0}{ = Orientation and script detection (OSD) only.}
\item{1}{ = Automatic page segmentation with OSD.}
\item{2}{ = Automatic page segmentation, but no OSD, or OCR.}
\item{3}{ = Fully automatic page segmentation, but no OSD. (Default)}
\item{4}{ = Assume a single column of text of variable sizes.}
\item{5}{ = Assume a single uniform block of vertically aligned text.}
\item{6}{ = Assume a single uniform block of text.}
\item{7}{ = Treat the image as a single text line.}
\item{8}{ = Treat the image as a single word.}
\item{9}{ = Treat the image as a single word in a circle.}
\item{10}{ = Treat the image as a single character.}
}}
}
\description{
\code{Optical character recognition (OCR) trough tesseract}
}
|
33e20493de0140eead90b391909145f0165283df
|
37fcfce951487d3ba45d2ba3dbc22b6360939b77
|
/man/scale_per_minute.Rd
|
84b16e1346a30f652a7bef40237b058b096f5ad7
|
[] |
no_license
|
abresler/nbastatR
|
576155fb58378c03010590c81806515161c03eb5
|
aba9179ef644f263387c1536d6ddd26104d79cf4
|
refs/heads/master
| 2023-08-08T08:52:05.149224
| 2023-07-19T13:09:21
| 2023-07-19T13:09:21
| 43,844,510
| 307
| 94
| null | 2023-02-01T16:59:22
| 2015-10-07T21:00:59
|
R
|
UTF-8
|
R
| false
| true
| 356
|
rd
|
scale_per_minute.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{scale_per_minute}
\alias{scale_per_minute}
\title{Summarise data per minute}
\usage{
scale_per_minute(data, scale_columns = NULL)
}
\arguments{
\item{data}{a data frame}
\item{scale_columns}{vector of columns to scale}
}
\description{
Summarise data per minute
}
|
797c1c10cf0533b58b8e5e79f531d89a51834d09
|
a48797beca55474d7b39676389f77f8f1af76875
|
/man/graph_to_pcs.Rd
|
7b994805ac850e0f349c91fcc0e3a56f07803723
|
[] |
no_license
|
uqrmaie1/admixtools
|
1efd48d8ad431f4a325a4ac5b160b2eea9411829
|
26759d87349a3b14495a7ef4ef3a593ee4d0e670
|
refs/heads/master
| 2023-09-04T02:56:48.052802
| 2023-08-21T21:15:27
| 2023-08-21T21:15:27
| 229,330,187
| 62
| 11
| null | 2023-01-23T12:19:57
| 2019-12-20T20:15:32
|
R
|
UTF-8
|
R
| false
| true
| 1,076
|
rd
|
graph_to_pcs.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/toposearch.R
\name{graph_to_pcs}
\alias{graph_to_pcs}
\title{Simulate PCs under an admixture graph}
\usage{
graph_to_pcs(
graph,
nsnps = 10000,
drift_default = 0.02,
admix_default = 0.5,
leaves_only = TRUE
)
}
\arguments{
\item{graph}{An admixture graph as igraph object, or as edge list data frame
with a column \code{weight}, as returned by \code{qpgraph()$edges}}
\item{nsnps}{Number of SNPs to simulate}
\item{drift_default}{Default branch lengths. Ignored if \code{graph} is a data frame with weights}
\item{admix_default}{Default admixture weights. Ignored if \code{graph} is a data frame with weights}
\item{leaves_only}{Return PCs for leaf nodes only}
}
\value{
A data frame with PCs for each population
}
\description{
This function simulates PCA of allele frequencies under an admixture graph model
}
\examples{
\dontrun{
pcs = graph_to_pcs(example_igraph)
pcs \%>\% ggplot(aes(PC1, PC2, label = pop)) + geom_text() + geom_point()
}
}
\seealso{
\code{\link{graph_to_afs}}
}
|
afe6a88b05f53284f158ff0ed7e6d011aa29dda2
|
40261deb3548e47216204512da1012d927bad4aa
|
/man/fpl_player_hist.Rd
|
f9192299d305a838809b0da8c6b121658ee9af12
|
[] |
no_license
|
jphelps13/fpl.analytics
|
05195c122036e083cfc01dbf5329020ca527e4b8
|
4f21706e42cb083125aec50b252efd151994ff80
|
refs/heads/master
| 2020-03-25T11:02:51.322784
| 2018-08-14T15:36:20
| 2018-08-14T15:36:20
| 143,717,104
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 631
|
rd
|
fpl_player_hist.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fpl_player_hist.R
\docType{data}
\name{fpl_player_hist}
\alias{fpl_player_hist}
\title{Player historical FTP data}
\format{An object of class \code{data.table} (inherits from \code{data.frame}) with 1696 rows and 26 columns.}
\source{
\url{https://fantasy.premierleague.com/drf/element-summary}
}
\usage{
fpl_player_hist
}
\description{
input from a \code{\link{fpl_season_json}} list object.
Loops through the ids, and collects all the historical season data for
these players
}
\examples{
fpl_season_json <- pullFplSeasonJson()
}
\keyword{datasets}
|
b76e43bc4035110a71ca5ebd0bbd5d821fac321e
|
781aef24c9fae0d1f275f9ac0eae0f7164b79a69
|
/R/fitted.sca.R
|
d8336026a25e95d114f617f5f492ef9d6d142471
|
[] |
no_license
|
cran/multiway
|
e3326bc34a72c7a74037c68b053428102fab5cf2
|
f2e6377ccb5207ddc6d7d8910a0f3293f87185c3
|
refs/heads/master
| 2021-01-22T08:53:07.193287
| 2019-03-13T07:20:03
| 2019-03-13T07:20:03
| 34,022,312
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 249
|
r
|
fitted.sca.R
|
fitted.sca <-
function(object,...){
# Calculates Fitted Values (lists) for fit SCA Models
# Nathaniel E. Helwig (helwig@umn.edu)
# last updated: April 9, 2015
sapply(object$D,function(d,b){tcrossprod(d,b)},b=object$B)
}
|
0cda1358539a4ce14d089eeb196010cc2b612e26
|
bf7507dfab13cf917d2be97fdd2abee5c21ebf04
|
/man/ovarianSubsampling.Rd
|
bc3935ce645ac5553f76652dbc8ff1fef5988c41
|
[] |
no_license
|
cbg-ethz/TiMEx
|
acac638214cd97e7ec10e09f74854f13aa351c7e
|
f0d0f0f5fd8c58bb5da9d01ffa49bbe6ffc09c19
|
refs/heads/master
| 2021-01-15T14:50:31.880579
| 2018-11-12T17:22:15
| 2018-11-12T17:22:15
| 38,927,358
| 4
| 3
| null | 2018-11-12T17:22:16
| 2015-07-11T13:53:33
|
R
|
UTF-8
|
R
| false
| true
| 1,468
|
rd
|
ovarianSubsampling.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/documentDatasets.R
\name{ovarianSubsampling}
\alias{ovarianSubsampling}
\title{Stability of mutually exclusive groups in ovarian cancer}
\format{\code{ovarianSubsampling} is a list with as many elements as
subsampling frequencies provided (3 in this case). Each element is further
a list with as many elements as number of sizes of the significantly
mutually exclusive groups identified. Additionally, \code{bonf} and
\code{fdr} are two lists corresponding to each of these elements,
representing different multiple correction methods. Finally, each element
is a vector of relative counts of the significantly mutually exclusive
groups identified. For example, \code{ovarianSubsampling[[1]][[3]]}
represents the relative counts of the identified mutually exclusive groups
of size 3 for a subsampling frequency of 30\%, for both \code{fdr} and
\code{bonf} (bonferroni) multiple correction methods.}
\source{
Produced with the function \code{\link{subsampleAnalysis}}, ran with
the inputs
\code{subsampl<-c(0.3,0.5,0.8)}
\code{noReps<-100}
and the mutually exclusive groups from \code{\link{ovarianOutput}}.
}
\description{
Dataset containing the stability of the mutually exclusive groups identified
by TiMEx in the ovarian cancer dataset \code{\link{ovarian}}, after
subsampling the set of patients at frequencies of \code{30\%},
\code{50\%}, and \code{80\%}, 100 times.
}
|
bd8339cf91e43c371d56badb0a059090c4ac0724
|
d2d18a40e768ce045c82639e16bf541dd36c660c
|
/03_diagnostics/hh_size_distro.R
|
817e9a6b87501c1bdd8431d5c15996dd0d001658
|
[] |
no_license
|
manzorz/wash_mapping
|
a607dd7f158217b2628cdf8fc75957abe1a64812
|
821fa49b66847d256adfb9e0b6fdbd7fef15a2d5
|
refs/heads/master
| 2021-07-09T00:14:19.171006
| 2018-01-31T20:53:32
| 2018-01-31T20:53:32
| 95,460,682
| 0
| 0
| null | 2017-06-26T15:21:39
| 2017-06-26T15:21:38
| null |
UTF-8
|
R
| false
| false
| 3,562
|
r
|
hh_size_distro.R
|
library(tidyverse)
library(ggpubr)
library(grid)
library(gridExtra)
setwd('J:/LIMITED_USE/LU_GEOSPATIAL/geo_matched/wash')
# read in extraction
load('points_collapsed_2017_07_31.Rdata')
load('polys_collapsed_2017_07_31.Rdata')
# bind rows to create master dataset
alldat <- bind_rows(pt_collapse, poly_collapse)
alldat$point <- ifelse(is.na(alldat$lat)|is.na(alldat$long), 'poly','point')
# read in post-collapse data
mydat <- read_csv(file = 'J:/WORK/11_geospatial/10_mbg/input_data/w_imp.csv', col_types = 'ddddcddcdcddddddd')
# define regions
cssa <- c('CAR','COD','GNQ','COG', 'GAB','STP','AGO')
sssa_hi <- c('NAM','ZAF','BWA')
name_hi <- c('MAR','DZA','TUN','LBY','EGY')
essa_hilo <- c('SDN','SSD','ETH','ERI','DJI','SOM','KEN','UGA','RWA',
'BDI','TZA','MWI','ZMB','ZWE','LSO','SWZ','MDG')
wssa <- c('MRT','MLI','NER','TCD','SEN','GMB','GIN','SLE','LBR','CIV',
'GHA','BFA','TGO','BEN','NGA','CMR', 'CPV')
reg_list <- list(cssa, sssa_hi, name_hi, essa_hilo)
reg_vec <- unlist(reg_list)
# plot for 5 countries at a time violin plots of hh_size by point vs. poly
# for pre-collapse data
setwd('C:/Users/adesh/Desktop')
pdf('pre_collapse_hhsize_dx.pdf')
for (i in seq(1, length(reg_vec), by = 5)) {
sub_vec <- reg_vec[i:ifelse(i+4>length(reg_vec),length(reg_vec),i+4)]
plot_dat <- filter(alldat, iso3 %in% sub_vec)
print(
ggplot(plot_dat) + geom_violin(fill = 'red', aes(x = iso3, y = hh_size)) + theme_bw() +
facet_grid(point ~ ., scales = 'free_y')
)
}
dev.off()
# plot for 5 countries at a time violin plots of hh_size by point vs. poly
# for post-collapse data
setwd('C:/Users/adesh/Desktop')
pdf('post_collapse_hhsize_dx.pdf')
for (i in seq(1, length(reg_vec), by = 5)) {
sub_vec <- reg_vec[i:ifelse(i+4>length(reg_vec),length(reg_vec),i+4)]
plot_dat <- filter(mydat, country %in% sub_vec)
print(
ggplot(plot_dat) + geom_violin(fill = 'red', aes(x = country, y = N)) + theme_bw() +
facet_grid(point ~ ., scales = 'free_y')
)
}
dev.off()
# ggplot scatter of indicator vs. N after running collapse script
setwd('C:/Users/adesh/Desktop')
pdf('post_collapse_hhsize_dx_indi_pt.pdf')
for (i in 1:length(reg_vec)) {
sub_vec <- reg_vec[i]
plot_dat <- filter(ptdat, iso3 %in% sub_vec)
if (nrow(plot_dat) > 0) {
g1 <- ggplot(plot_dat, aes(total_hh)) + geom_freqpoly() + ggtitle(sub_vec) + theme_bw()
g2 <- ggplot(plot_dat, aes(piped)) + geom_freqpoly() + theme_bw()
g3 <- ggplot(plot_dat) + geom_point(aes(x = total_hh, y = piped, col = survey_series)) + theme_bw()
g4 <- ggplot(plot_dat, aes(imp)) + geom_freqpoly() + theme_bw()
g5 <- ggplot(plot_dat) + geom_point(aes(x = total_hh, y = imp, col = survey_series)) + theme_bw()
print(g1, labels = sub_vec)
print(ggarrange(g2,g3, labels = sub_vec, ncol = 1, nrow = 2))
print(ggarrange(g4,g5, labels = sub_vec, ncol = 1, nrow = 2))
grid.newpage()
print(grid.table(plot_dat %>% group_by(nid, survey_series, year_start) %>%
summarize(total_people = sum(total_hh, na.rm = T))))
}
}
dev.off()
plot_dat <- filter(alldat, iso3 %in% c('LSO','MWI','TZA'))
ggplot(plot_dat) + geom_boxplot(fill = 'red', aes(x = iso3, y = hh_size)) + theme_bw() +
facet_grid(point ~ ., scales = 'free_y')
test <- filter(alldat, point == 'poly') %>% group_by(iso3) %>% summarize(hh_75 = quantile(hh_size, 0.75, na.rm = T))
# repeat for peru data
peru_dat <- filter(alldat, iso3 %in% 'PER')
ggplot(peru_dat) + geom_violin(fill = 'red', aes(x = iso3, y = hh_size)) + theme_bw() +
facet_grid(point ~ ., scales = 'free_y')
|
79f5593b9c6f7f51517e35cf4fce542ae4853fbd
|
719500684fceaf0a7a80ce663e9cf07802e10b9a
|
/R/open_url.r
|
7ffcaf5f14198ed79f949220d2c48d9659095628
|
[] |
no_license
|
pbreheny/breheny
|
964baf0670a9eb4975946eae66772d47d9affd11
|
3e15bb78a769616adb49ea50c800d026f48ef8e7
|
refs/heads/master
| 2023-08-09T18:34:35.865871
| 2023-08-01T15:38:54
| 2023-08-01T15:38:54
| 82,972,930
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 268
|
r
|
open_url.r
|
#' Open a url: quietly, in the background, and in a new window
#'
#' @param x URL to open
#'
#' @examples
#' open_url('www.google.com')
#' @export
open_url <- function(x) {
system2('sensible-browser', c('--new-window', x), stdout=NULL, stderr=NULL, wait=FALSE)
}
|
66a501d69257b19a349696e42d9f5e07d78bdbab
|
089b39df2c24c06c3ff605d82705ba588decb419
|
/scripts/05_mdd_enrichment/01_prep_singlecell_for_ldsc.R
|
aa93ca1c549006ff02b7ff8ea9a7eab01ab7e737
|
[] |
no_license
|
JujiaoKang/2020_PNAS_Depression
|
09526a8b22baa3ecb635fdf71f9b485480692e32
|
e5bcac0d9a456f433e2d7e374af767f8d6ad2cdf
|
refs/heads/master
| 2023-05-27T09:39:26.669418
| 2021-06-07T12:11:02
| 2021-06-07T12:11:02
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 15,120
|
r
|
01_prep_singlecell_for_ldsc.R
|
library(tidyverse)
library(EWCE)
library(MAGMA.Celltyping)
library(biomaRt)
library(org.Hs.eg.db)
library(stringr)
# This script will write text files of
####################
# set up directories
####################
base_dir = '/gpfs/milgram/project/holmes/kma52/mdd_gene_expr'
umi_dir = file.path(base_dir, '/data/singlecell/')
ldsc_dir = paste0(base_dir, '/data/ldsc')
# 37 gene mapping
ensembl37 = useMart("ENSEMBL_MART_ENSEMBL", dataset="hsapiens_gene_ensembl", host="grch37.ensembl.org")
# easy to map genes
hgnc_ens37_df = getBM(attributes=c('ensembl_gene_id', 'entrezgene_id', 'hgnc_symbol','chromosome_name', 'start_position', 'end_position'), mart = ensembl37)
hgnc_ens37_df = hgnc_ens37_df[which(hgnc_ens37_df$chromosome_name %in% 1:22),]
hgnc_ens37_df = hgnc_ens37_df[!is.na(hgnc_ens37_df$entrezgene_id),]
hgnc_ens37_df = hgnc_ens37_df[!duplicated(hgnc_ens37_df$hgnc_symbol),]
##################
# Lake 2018 - DFC
##################
# EWCE cell-specific gene expression
load(paste0(base_dir, '/output/diff_expr/CellTypeData_EWCE_lake_dfc_ctd_batch.rda'), verbose=T)
dfc_ctd = ctd
dfc_ctd = prepare.quantile.groups(dfc_ctd, specificity_species="human",numberOfBins=10)
# load preprocessed data
region = 'FrontalCortex'
# current out dir
prefix = 'lake_dfc_bigcat_ldsc'
fine_prefix = 'lake_dfc_superordinate_cat_ldsc'
# make if doesnt exist
current_dir_out = paste0(ldsc_dir, '/', prefix, '_files')
current_finedir_out = paste0(ldsc_dir, '/', fine_prefix, '_files')
# write table with all genes and chromosomal positions
ldsc_out = hgnc_ens37_df[c('ensembl_gene_id', 'chromosome_name', 'start_position', 'end_position')]
colnames(ldsc_out) = c('GENE', 'CHR', 'START', 'END')
ldsc_out = ldsc_out[ldsc_out$CHR %in% as.character(1:22),]
ldsc_out = ldsc_out[order(ldsc_out$CHR),]
ldsc_out = ldsc_out[order(as.numeric(ldsc_out$CHR)),]
head(ldsc_out)
# write gene coordinates
out_path = paste0(current_dir_out, '/', prefix, '_gene_coords.txt')
write_delim(ldsc_out, out_path, delim='\t')
#
out_path = paste0(current_finedir_out, '/', fine_prefix, '_gene_coords.txt')
write_delim(ldsc_out, out_path, delim='\t')
# genes matched between hgnc coordinate data from and specificity data frame
hgnc_ens37_df_dfc = hgnc_ens37_df %>% filter(hgnc_symbol %in% rownames(dfc_ctd[[1]]$specificity_quantiles))
hgnc_ens37_df_nodfc = hgnc_ens37_df %>% filter(!hgnc_symbol %in% rownames(dfc_ctd[[1]]$specificity_quantiles))
# subset specificity data
dfc_ctd[[1]]$specificity = dfc_ctd[[1]]$specificity[rownames(dfc_ctd[[1]]$specificity) %in% hgnc_ens37_df_dfc$hgnc_symbol,]
dfc_ctd[[2]]$specificity = dfc_ctd[[2]]$specificity[rownames(dfc_ctd[[2]]$specificity) %in% hgnc_ens37_df_dfc$hgnc_symbol,]
dfc_ctd = prepare.quantile.groups(dfc_ctd, specificity_species="human", numberOfBins=10)
# sanity check using SST
spec_check = as.data.frame(dfc_ctd[[1]]$specificity)
spec_check = spec_check[rev(order(spec_check$SST)),]
which(rownames(spec_check) == 'SST')
spec_check = spec_check[rev(order(spec_check$In6)),]
which(rownames(spec_check) == 'PVALB')
spec_check = as.data.frame(dfc_ctd[[2]]$specificity)
spec_check = spec_check[rev(order(spec_check$Inh)),]
which(rownames(spec_check) == 'GAD1')
# sanity check using SST
# FINE cell cat
nbins = 10
genebin_num = round(nrow(hgnc_ens37_df_dfc)/nbins)
dfc_cells = colnames(dfc_ctd[[1]]$specificity_quantiles)
quantile_df = as.data.frame(dfc_ctd[[1]]$specificity_quantiles)
for (cell in dfc_cells){
write(cell,'')
cur_quantile = quantile_df[[cell]]
for (bin in 0:nbins){
ct = bin
bin_hgnc = rownames(quantile_df)[which(cur_quantile == bin)]
bin_ensembls = hgnc_ens37_df_dfc$ensembl_gene_id[hgnc_ens37_df_dfc$hgnc_symbol %in% bin_hgnc]
print(length(bin_ensembls))
out_file = paste0(current_finedir_out, '/', fine_prefix, '_', cell, '_', str_pad(ct,2,pad="0"),'_',as.character(nbins),'_batch_genes.txt')
write.table(bin_ensembls, out_file, quote=F, row.names=F, col.names=F)
}
}
write.table(hgnc_ens37_df_nodfc$ensembl_gene_id, paste0(current_finedir_out, '/', fine_prefix, '_nomatch_batch_genes.txt'), quote=F, row.names=F, col.names=F)
write.table(hgnc_ens37_df$ensembl_gene_id, paste0(current_finedir_out, '/', fine_prefix, '_allcontrol_batch_genes.txt'), quote=F, row.names=F, col.names=F)
# BIG cell cat
nbins = 10
genebin_num = round(nrow(hgnc_ens37_df_dfc)/nbins)
dfc_cells = colnames(dfc_ctd[[2]]$specificity_quantiles)
quantile_df = as.data.frame(dfc_ctd[[2]]$specificity_quantiles)
for (cell in dfc_cells){
cur_quantile = quantile_df[[cell]]
for (bin in 0:nbins){
ct = bin
bin_hgnc = rownames(quantile_df)[which(cur_quantile == bin)]
bin_ensembls = hgnc_ens37_df_dfc$ensembl_gene_id[hgnc_ens37_df_dfc$hgnc_symbol %in% bin_hgnc]
print(length(bin_ensembls))
out_file = paste0(current_dir_out, '/', prefix, '_', cell, '_bigcat_', str_pad(ct,2,pad="0"),'_', as.character(nbins),'_batch_genes.txt')
write.table(bin_ensembls, out_file, quote=F, row.names=F, col.names=F)
}
}
write.table(hgnc_ens37_df_nodfc$ensembl_gene_id, paste0(current_dir_out, '/', prefix, '_nomatch_batch_genes.txt'), quote=F, row.names=F, col.names=F)
write.table(hgnc_ens37_df$ensembl_gene_id, paste0(current_dir_out, '/', prefix, '_allcontrol_batch_genes.txt'), quote=F, row.names=F, col.names=F)
##################
# Lake 2018 - VIS
##################
# Lake VIS - specific gene expression
load(paste0(base_dir, '/output/diff_expr/CellTypeData_EWCE_lake_vis_ctd_batch.rda'), verbose=T)
vis_ctd = ctd
vis_ctd = prepare.quantile.groups(vis_ctd, specificity_species="human",numberOfBins=10)
# load preprocessed data
region = 'VisualCortex'
# current out dir
prefix = 'lake_vis_bigcat_ldsc'
fine_prefix = 'lake_vis_superordinate_cat_ldsc'
# make if doesnt exist
current_dir_out = paste0(ldsc_dir, '/', prefix, '_files')
current_finedir_out = paste0(ldsc_dir, '/', fine_prefix, '_files')
# write table with all genes and chromosomal positions
ldsc_out = hgnc_ens37_df[c('ensembl_gene_id', 'chromosome_name', 'start_position', 'end_position')]
colnames(ldsc_out) = c('GENE', 'CHR', 'START', 'END')
ldsc_out = ldsc_out[ldsc_out$CHR %in% as.character(1:22),]
ldsc_out = ldsc_out[order(ldsc_out$CHR),]
ldsc_out = ldsc_out[order(as.numeric(ldsc_out$CHR)),]
head(ldsc_out)
# write gene coordinate file for both BIG/FINE cats
out_path = paste0(current_dir_out, '/', prefix, '_gene_coords.txt')
write_delim(ldsc_out, out_path, delim='\t')
#
out_path = paste0(current_finedir_out, '/', fine_prefix, '_gene_coords.txt')
write_delim(ldsc_out, out_path, delim='\t')
# genes matched between hgnc coordinate data from and specificity data frame
hgnc_ens37_df_vis = hgnc_ens37_df %>% filter(hgnc_symbol %in% rownames(vis_ctd[[1]]$specificity_quantiles))
hgnc_ens37_df_novis = hgnc_ens37_df %>% filter(!hgnc_symbol %in% rownames(vis_ctd[[1]]$specificity_quantiles))
# subset specificity data
vis_ctd[[1]]$specificity = vis_ctd[[1]]$specificity[rownames(vis_ctd[[1]]$specificity) %in% hgnc_ens37_df_vis$hgnc_symbol,]
vis_ctd[[2]]$specificity = vis_ctd[[2]]$specificity[rownames(vis_ctd[[2]]$specificity) %in% hgnc_ens37_df_vis$hgnc_symbol,]
vis_ctd = prepare.quantile.groups(vis_ctd, specificity_species="human", numberOfBins=10)
# sanity check using SST
spec_check = as.data.frame(vis_ctd[[1]]$specificity)
spec_check = spec_check[rev(order(spec_check$SST)),]
which(rownames(spec_check) == 'SST')
spec_check = spec_check[rev(order(spec_check$In6)),]
which(rownames(spec_check) == 'PVALB')
spec_check = as.data.frame(vis_ctd[[2]]$specificity)
spec_check = spec_check[rev(order(spec_check$Inh)),]
which(rownames(spec_check) == 'GAD1')
# FINE cell cat
nbins = 10
genebin_num = round(nrow(hgnc_ens37_df_vis)/nbins)
vis_cells = colnames(vis_ctd[[1]]$specificity_quantiles)
quantile_df = as.data.frame(vis_ctd[[1]]$specificity_quantiles)
# for each cell, write gene lists for each decile (based on cell specificity)
for (cell in vis_cells){
write(cell,'')
cur_quantile = quantile_df[[cell]]
for (bin in 0:nbins){
ct = bin
bin_hgnc = rownames(quantile_df)[which(cur_quantile == bin)]
bin_ensembls = hgnc_ens37_df_vis$ensembl_gene_id[hgnc_ens37_df_vis$hgnc_symbol %in% bin_hgnc]
print(length(bin_ensembls))
out_file = paste0(current_finedir_out, '/', fine_prefix, '_', cell, '_', str_pad(ct,2,pad="0"),'_',as.character(nbins),'_batch_genes.txt')
write.table(bin_ensembls, out_file, quote=F, row.names=F, col.names=F)
}
}
write.table(hgnc_ens37_df_novis$ensembl_gene_id, paste0(current_finedir_out, '/', fine_prefix, '_nomatch_batch_genes.txt'), quote=F, row.names=F, col.names=F)
write.table(hgnc_ens37_df$ensembl_gene_id, paste0(current_finedir_out, '/', fine_prefix, '_allcontrol_batch_genes.txt'), quote=F, row.names=F, col.names=F)
# BIG cell cat
nbins = 10
genebin_num = round(nrow(hgnc_ens37_df_vis)/nbins)
vis_cells = colnames(vis_ctd[[2]]$specificity_quantiles)
quantile_df = as.data.frame(vis_ctd[[2]]$specificity_quantiles)
for (cell in vis_cells){
cur_quantile = quantile_df[[cell]]
for (bin in 0:nbins){
ct = bin
bin_hgnc = rownames(quantile_df)[which(cur_quantile == bin)]
bin_ensembls = hgnc_ens37_df_vis$ensembl_gene_id[hgnc_ens37_df_vis$hgnc_symbol %in% bin_hgnc]
print(length(bin_ensembls))
out_file = paste0(current_dir_out, '/', prefix, '_', cell, '_bigcat_', str_pad(ct,2,pad="0"),'_', as.character(nbins),'_batch_genes.txt')
write.table(bin_ensembls, out_file, quote=F, row.names=F, col.names=F)
}
}
write.table(hgnc_ens37_df_novis$ensembl_gene_id, paste0(current_dir_out, '/', prefix, '_nomatch_batch_genes.txt'), quote=F, row.names=F, col.names=F)
write.table(hgnc_ens37_df$ensembl_gene_id, paste0(current_dir_out, '/', prefix, '_allcontrol_batch_genes.txt'), quote=F, row.names=F, col.names=F)
# write table with all genes and chromosomal positions
ldsc_out = hgnc_ens37_df[c('ensembl_gene_id', 'chromosome_name', 'start_position', 'end_position')]
colnames(ldsc_out) = c('GENE', 'CHR', 'START', 'END')
ldsc_out = ldsc_out[ldsc_out$CHR %in% as.character(1:22),]
ldsc_out = ldsc_out[order(ldsc_out$CHR),]
ldsc_out = ldsc_out[order(as.numeric(ldsc_out$CHR)),]
out_path = paste0(current_dir_out, '/', prefix, '_gene_coords.txt')
head(ldsc_out)
write_delim(ldsc_out, out_path, delim='\t')
#
out_path = paste0(current_finedir_out, '/', fine_prefix, '_gene_coords.txt')
write_delim(ldsc_out, out_path, delim='\t')
# ABA MTG
# ABA MTG
# ABA MTG
# ABA MTG
# ABA MTG
# ABA MTG
load(paste0(base_dir, '/output/diff_expr/CellTypeData_EWCE_aba_mtg_ctd.rda'), verbose=T)
mtg_ctd = ctd
mtg_ctd = prepare.quantile.groups(mtg_ctd, specificity_species="human", numberOfBins=10)
# 37 gene mapping
ensembl38 = useMart("ensembl", dataset="hsapiens_gene_ensembl")
# easy to map genes
hgnc_ens38_df = getBM(attributes=c('ensembl_gene_id', 'entrezgene_id', 'hgnc_symbol','chromosome_name', 'start_position', 'end_position'), mart = ensembl38)
hgnc_ens38_df = hgnc_ens38_df[which(hgnc_ens38_df$chromosome_name %in% 1:22),]
hgnc_ens38_df = hgnc_ens38_df[!is.na(hgnc_ens38_df$entrezgene_id),]
hgnc_ens38_df = hgnc_ens38_df[!duplicated(hgnc_ens38_df$hgnc_symbol),]
# load preprocessed data
load(paste0(umi_dir, '/ahba_mtg/mtg_singlecell_ahba_match_seurat_processed.Rdata'), verbose=T)
#mtg_genes = colnames(mtg_dat)[1:colnames(mtg_dat)-1]
# current out dir
prefix = 'aba_mtg_bigcat_ldsc'
fine_prefix = 'aba_mtg_finecat_ldsc'
# make if doesnt exist
current_dir_out = paste0(ldsc_dir, '/', prefix, '_files')
current_finedir_out = paste0(ldsc_dir, '/', fine_prefix, '_files')
ldsc_out = hgnc_ens38_df[c('ensembl_gene_id', 'chromosome_name', 'start_position', 'end_position')]
colnames(ldsc_out) = c('GENE', 'CHR', 'START', 'END')
ldsc_out = ldsc_out[ldsc_out$CHR %in% as.character(1:22),]
ldsc_out = ldsc_out[order(ldsc_out$CHR),]
ldsc_out = ldsc_out[order(as.numeric(ldsc_out$CHR)),]
out_path = paste0(current_dir_out, '/',prefix,'_gene_coords.txt')
write_delim(ldsc_out, out_path, delim='\t')
print(out_path)
#
out_path = paste0(current_finedir_out, '/',fine_prefix,'_gene_coords.txt')
write_delim(ldsc_out, out_path, delim='\t')
# genes matched between hgnc coordinate data from and specificity data frame
hgnc_ens38_df_mtg = hgnc_ens38_df[hgnc_ens38_df$hgnc_symbol %in% rownames(mtg_ctd[[1]]$specificity_quantiles),]
hgnc_ens38_df_non_mtg = hgnc_ens38_df[!hgnc_ens38_df$hgnc_symbol %in% rownames(mtg_ctd[[1]]$specificity_quantiles),]
# subset specificity data
mtg_ctd[[1]]$specificity = mtg_ctd[[1]]$specificity[rownames(mtg_ctd[[1]]$specificity) %in% hgnc_ens38_df_mtg$hgnc_symbol,]
mtg_ctd[[2]]$specificity = mtg_ctd[[2]]$specificity[rownames(mtg_ctd[[2]]$specificity) %in% hgnc_ens38_df_mtg$hgnc_symbol,]
#mtg_ctd = prepare.quantile.groups(mtg_ctd, specificity_species="human", numberOfBins=20)
mtg_ctd = prepare.quantile.groups(mtg_ctd, specificity_species="human", numberOfBins=10)
nbins = 10
genebin_num = round(nrow(hgnc_ens38_df)/nbins)
mtg_cells = colnames(mtg_ctd[[1]]$specificity_quantiles)
quantile_df = as.data.frame(mtg_ctd[[1]]$specificity_quantiles)
for (cell in mtg_cells){
cur_quantile = quantile_df[[cell]]
for (bin in 0:nbins){
ct = bin
bin_hgnc = rownames(quantile_df)[which(cur_quantile == bin)]
bin_ensembls = hgnc_ens38_df_mtg$ensembl_gene_id[hgnc_ens38_df_mtg$hgnc_symbol %in% bin_hgnc]
print(length(bin_ensembls))
out_file = paste0(current_finedir_out, '/', fine_prefix, '_', cell, '_', str_pad(ct,2,pad="0"),'_',as.character(nbins),'_genes.txt')
write.table(bin_ensembls, out_file, quote=F, row.names=F, col.names=F)
}
}
write.table(hgnc_ens38_df_non_mtg$ensembl_gene_id, paste0(current_finedir_out, '/', fine_prefix, '_nomatch_genes.txt'), quote=F, row.names=F, col.names=F)
write.table(hgnc_ens38_df_mtg$ensembl_gene_id, paste0(current_finedir_out, '/', fine_prefix, '_allcontrol_genes.txt'), quote=F, row.names=F, col.names=F)
nbins = 10
genebin_num = round(nrow(hgnc_ens38_df)/nbins)
mtg_cells = colnames(mtg_ctd[[2]]$specificity_quantiles)
quantile_df = as.data.frame(mtg_ctd[[2]]$specificity_quantiles)
for (cell in mtg_cells){
cur_quantile = quantile_df[[cell]]
for (bin in 0:nbins){
ct = bin
bin_hgnc = rownames(quantile_df)[which(cur_quantile == bin)]
bin_ensembls = hgnc_ens38_df_mtg$ensembl_gene_id[hgnc_ens38_df_mtg$hgnc_symbol %in% bin_hgnc]
print(length(bin_ensembls))
out_file = paste0(current_dir_out, '/', prefix, '_', cell, '_bigcat_', str_pad(ct,2,pad="0"),'_', as.character(nbins),'_genes.txt')
write.table(bin_ensembls, out_file, quote=F, row.names=F, col.names=F)
}
}
write.table(hgnc_ens38_df_non_mtg$ensembl_gene_id, paste0(current_dir_out, '/', prefix, '_nomatch_genes.txt'), quote=F, row.names=F, col.names=F)
write.table(hgnc_ens38_df_mtg$ensembl_gene_id, paste0(current_dir_out, '/', prefix, '_allcontrol_genes.txt'), quote=F, row.names=F, col.names=F)
|
d96fc9effa63d8a9e7cb2b1d3c6b8993f5f2ac8f
|
d46a62735f241be070aa0363ab6870ad2fb46015
|
/R/node_ckr.R
|
df439efa95d7d8adf9f3a01b265af59af2567797
|
[
"MIT"
] |
permissive
|
DataUrbanEconGeek/wikipop
|
7bda325e50500e0061c17388fcf1c5d4dc110b8e
|
ba073d08adbd8677dcb4941081cba22fe0124b6a
|
refs/heads/master
| 2020-03-22T02:08:54.771580
| 2018-07-08T04:02:03
| 2018-07-08T04:02:03
| 139,352,274
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 168
|
r
|
node_ckr.R
|
#' A function to check css node
#' @import rvest
#' @export
node_ckr <- function(wikihtml, path){
class(try(html_nodes(wikihtml, css = path),silent=TRUE))[1]
}
|
7ad8d05617dbfba3afa6dd91ffdbc8a8602fcebf
|
cceb54a5dd3b3053fe70425a9786d670bc48285c
|
/R/indpca.R
|
9bbe5368227641db1e30e5c8f7a1efb348c08876
|
[] |
no_license
|
cran/hierfstat
|
ee123e045e3592a9ce4181b0f4aa8fd96649da62
|
30663f916505fb83451a49bbe0fa2fbe082632eb
|
refs/heads/master
| 2022-05-12T16:31:17.085452
| 2022-05-05T22:40:02
| 2022-05-05T22:40:02
| 17,696,646
| 2
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,574
|
r
|
indpca.R
|
###
#'@export
indpca<-function(dat,ind.labels=NULL,scale=FALSE){
#requires ade4
#given a genotype data set dat
#and individual labels lab
#performs a centered, unscaled PCA on individuals
#if scale=TRUE, PCA carried out on correlation matrix
if (is.genind(dat)) dat<-genind2hierfstat(dat)
indp<-pop.freq(cbind(1:dim(dat)[1],dat[,-1]))
mati<-NULL
for (i in 1:length(indp)) mati<-rbind(mati,indp[[i]])
mati<-t(mati) #ind rows, nbal col
mp<-apply(mati,2,mean,na.rm=T)
matic<-sweep(mati,2,FUN="-",mp)
matic[is.na(matic)]<-0.0 #replace NA with 0
pca.matic<-ade4::dudi.pca(matic,scannf=FALSE,scale=scale,nf=min(min(dim(matic)),50))
if (is.null(ind.labels)) pca.matic$rownames<-as.character(dat[,1])
else pca.matic$rownames<-ind.labels
res<-list(call=match.call(),ipca=pca.matic,ifreq=mati)
class(res)<-"indpca"
res
}
#' @method print indpca
#' @export
print.indpca<-function(x,...){
print(names(x))
print("$call:")
print(x$call)
print("Dimension of individidual frequency matrix is: ")
print(dim(x$ifreq))
print("$ipca:")
print(x$ipca)
invisible(x)
}
#' @method plot indpca
#' @export
plot.indpca<-function(x,eigen=FALSE,ax1=1,ax2=2,...){
if(eigen){
graphics::par(mfrow=c(2,1))
graphics::plot(x$ipca$eig/sum(x$ipca$eig),type="h",xlab="axes",ylab="Prop. variance")
}
graphics::plot(x$ipca$li[,ax1],x$ipca$li[,ax2],xlab=paste("Axis: ",ax1,sep=""),ylab=paste("Axis: ",ax2,sep=""),type="n",...)
graphics::text(x$ipca$li[,ax1],x$ipca$li[,ax2],labels=x$ipca$rownames,...)
if(eigen) graphics::par(mfrow=c(1,1))
}
|
ce565e9508d785190a52b1a3fb4a13c81ebc8afc
|
0e8b34d1a0ba4f1d75ff94bfbec54ac1fd81ee35
|
/hypothesistests.R
|
41108a51555a2842c898e14dc2ac0cc743abd36c
|
[] |
no_license
|
lolfxo/Data_Analysis
|
f49bb88c2f053d9baad929b9ab6f54ed4617a690
|
f78db3120f9414a9f819f1bc022269d88b473edf
|
refs/heads/master
| 2021-09-07T10:58:22.166168
| 2018-02-21T23:16:07
| 2018-02-21T23:16:07
| 116,081,281
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 681
|
r
|
hypothesistests.R
|
# Script to create the "hypothesistests" package
# Clear current list
# rm(list=ls())
# Get working directory
getwd()
# Create your own R package
# Step 0: Packages I will need
install.packages("devtools")
library("devtools")
devtools::install_github("klutometis/roxygen")
library(roxygen2)
# Step 1: Create your package directory
setwd("C:/Users/hongn/Documents/GitHub/Data_Analysis")
# Create folder in which to include functions. This is also the package name.
# create("hypothesistests")
setwd("./hypothesistests")
# create the document
document()
# install 'hypothesistests' package straight from GitHub
install_github("lolfxo/Data_Analysis",subdir="hypothesistests")
|
acfa6e9265e67cfc59c4be64e357fc9aa05e85c7
|
ba3b0d0347da419c1f9fd07bcc5ddb354ccafd2a
|
/20190130_equating_multiple_test_items.R
|
b247f400aa9d5a89f29443bb00fbc26fa6239174
|
[] |
no_license
|
takuizum/R
|
ed88c662b5d399444a5a60cd16155e0f49301a62
|
e07689fd1ec0a53c500b0b87731ff565e9b6500d
|
refs/heads/master
| 2020-03-30T12:15:17.624459
| 2019-06-17T16:20:09
| 2019-06-17T16:20:09
| 151,216,645
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,988
|
r
|
20190130_equating_multiple_test_items.R
|
library(tidyverse)
library(irtfun2)
# gen simulation data set
th_1 <- rnorm(1000, 0, 1)
th_2 <- rnorm(1000, 0, 1)
th_3 <- rnorm(1000, 0, 1)
nofitems <- 70
true_a <- rlnorm(nofitems, 0, 0.4)
true_b <- rnorm(nofitems, 0, 1)
# LETTERS is built-in constatn, that has the 26 upper-case letters of the Roman alphabet
ID <- character(0)
for(t in 1:(nofitems/10)){
LET <- LETTERS[t] %>% rep(10)
NUM <- c(1:10) %>% formatC(width = 3, flag = 0) %>% as.matrix()
ID <- c(ID, paste0(LET, NUM))
}
t_para <- data.frame(Item = ID, a = true_a, b = true_b, c = rep(0, nofitems))
g1 <- ID[1:30]
g2 <- ID[21:50]
g3 <- ID[41:70]
sim_gen2 <- function(theta, para){
a <- para$a
b <- para$b
c <- para$c
ID <- para$Item
dat <- sim_gen(theta = theta, a = a, b = b, c = c)
#colnames(dat)[-1] <- ID
return(dat)
}
dat1 <- t_para[ID %in% g1,] %>% sim_gen2(theta = th_1)
colnames(dat1)[-1] <- g1
dat2 <- t_para[ID %in% g2,] %>% sim_gen2(theta = th_2)
colnames(dat2)[-1] <- g2
dat3 <- t_para[ID %in% g3,] %>% sim_gen2(theta = th_3)
colnames(dat3)[-1] <- g3
res_1 <- dat1 %>% estip2(fc = 2)
res_2 <- dat2 %>% estip2(fc = 2)
res_3 <- dat3 %>% estip2(fc = 2)
res_1$para$Item <- res_1$para$Item %>% as.character()
res_2$para$Item <- res_2$para$Item %>% as.character()
res_3$para$Item <- res_3$para$Item %>% as.character()
#CEquating(T_para = res_1$para, F_para = res_2$para)
# 項目パラメタの平均と標準偏差を計算し，データフレームに納める
# 平均は項目困難度の平均を，標準偏差は外れ値を考慮して識別力幾何平均の逆数とする。
library(pracma)
pre_mean <- matrix(nrow = 3, ncol = 3)
pre_sd <- matrix(nrow = 3, ncol = 3)
# diag(pre_mean) <- c(res_1$para$b %>% mean(), res_2$para$b %>% mean(), res_2$para$b %>% mean())
# diag(pre_sd) <- c(res_1$para$a %>% geomean(), res_2$para$a %>% geomean(), res_2$para$a %>% geomean())
para_list <- list(res_1, res_2, res_3)
for(i in 1:3){ # of row
row_a <- para_list[[i]]$para$a
row_b <- para_list[[i]]$para$b
row_item <- para_list[[i]]$para$Item
for(j in 1:3){ # of column
col_item <- para_list[[j]]$para$Item
key <- row_item %in% col_item
if(sum(key) == 0){
pre_mean[i, j] <- NA
pre_sd[i, j] <- NA
next
}
target_a <- row_a[key]
target_b <- row_b[key]
pre_mean[i, j] <- target_b %>% mean()
pre_sd[i, j] <- target_a %>% geomean()
# pre_sd[i, j] <- target_b %>% sd()
}
}
pre_mean <- matrix(c(68.4, 63.3, 0, 53.6, 60.2, 63.1, 55.3, 0, 0, 62.9, 57.1, 54.4, 76.5, 0, 58.9, 54.0), nrow=4)
pre_sd <- matrix(c(18.8, 3.4, 0, 5.4, 16.5, 3.5, 14.1, 0, 0, 3.6, 12.7, 6.8, 17.3, 0, 10.9, 6.1), nrow = 4)
# n of forms
# form <- c(1, 2, 3)
form <- c(1,2,3,4) # n of rater
# n of common subjects
n <- matrix(0, nrow = length(form), ncol = length(form))
for(i in form){
row_item <- para_list[[i]]$para$Item
for(j in form){
col_item <- para_list[[j]]$para$Item
n[i, j] <- row_item %in% col_item %>% sum()
}
}
n <- matrix(c(20, 10, 0, 10, 10, 20, 10, 0, 0, 10, 20, 10, 10, 0, 10, 20), nrow = 4, ncol = 4)
# sigma = A coefficient
H1 <- matrix(0, nrow = length(form), ncol = length(form))
for(i in form){
for(j in form){
if(i == j){ # diag element
for(k in form[!form == j]){
if(is.na(pre_sd[i, k])) next
H1[i, j] <- H1[i, j] + n[i, k] * pre_sd[i, k]^2
}
} else { # non diag
H1[i, j] <- -n[i, j] * pre_sd[i, j] * pre_sd[j, i]
}
if(n[i, j] == 0){ # no common item between test two forms
H1[i, j] <- 0
next
}
}
}
inv_H1 <- eigen(H1, symmetric = F)
#inv_H1 <- pinv(H1)
A <- inv_H1$vectors[,length(form)] # objective vector
# A <- inv_H1[,length(form)]
# mean = K coefficient
H2 <- matrix(0, nrow = length(form), ncol = length(form))
for(i in form){
for(j in form){
if(n[i, j] == 0){ # no common item between test two forms
H2[i, j] <- 0
next
}
if(i == j){ # diag element
for(k in form[!form == j]){
if(is.na(pre_mean[i, k])) next
H2[i, j] <- H2[i, j] + n[i, k] * pre_mean[i, k]
}
} else { # non diag
H2[i, j] <- -n[i, j] * pre_mean[i, j]
}
}
}
H3 <- matrix(0, nrow = length(form), ncol = length(form))
for(i in form){
for(j in form){
if(n[i, j] == 0){ # no common item between test two forms
H3[i, j] <- 0
next
}
if(i == j){ # diag element
for(k in form[!form == j]){
if(is.na(pre_mean[i, k])) next
H3[i, j] <- H3[i, j] + n[i, k]
}
} else { # non diag
H3[i, j] <- -n[i, j]
}
}
}
K <- -pinv(H3) %*% t(H2) %*% A
A
K
# res_1$para%>% dplyr::mutate(a = a/A[1]) %>% dplyr::mutate(b = A[1]*b + K[1])
# equate_manually <- function(para, A, K){
# #para %>% dplyr::mutate(a = a/A) %>% dplyr::mutate(b = A*b + K)
# para$a <- para$a/A
# }
|
252e8e35d2877723ca89fc94d9425b2fb557831c
|
dc202eab79abffe0ebe0db44a84f71c3886c04c8
|
/r/sparkr/man/bluemix2d-class.Rd
|
490fc141e2aad22b622bd047869aa3acca1bbf98
|
[
"Apache-2.0"
] |
permissive
|
gjebran/ibmos2spark
|
fa4f5b3d1c2dc690d8de8a3ed76940f5c5801e8c
|
677b840289b2d7adbb58d5a4c632a3b52d66435a
|
refs/heads/master
| 2021-01-16T18:22:04.078824
| 2017-08-07T22:11:05
| 2017-08-07T22:11:05
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,443
|
rd
|
bluemix2d-class.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/osconfig.R
\docType{class}
\name{bluemix2d-class}
\alias{bluemix2d}
\alias{bluemix2d-class}
\title{sparkcontext: a SparkContext object.}
\description{
credentials: a dictionary with the following required keys:
auth_url
project_id (or projectId)
user_id (or userId)
password
region
and optional key:
name #[to be deprecated] The name of the configuration.
name: string that identifies this configuration. You can
use any string you like. This allows you to create
multiple configurations to different Object Storage accounts.
This is not required at the moment, since credentials['name']
is still supported.
When using this from a IBM Spark service instance that
is configured to connect to particular Bluemix object store
instances, the values for these credentials can be obtained
by clicking on the 'insert to code' link just below a data
source.
}
\examples{
library(ibmos2spark)
creds = list(name="XXXXX",
auth_url="https://identity.open.softlayer.com",
region="dallas",
project_id = "XXXXX",
user_id="XXXXX",
password="XXXXX",
public = FALSE)
bmsc = bluemix(sparkcontext=sc, name=name, credentials = creds)
data <- read.df(sqlContext, bmsc$url(name, space,object), source = "com.databricks.spark.csv", header = "true")
head(data)
}
|
d76689bdfee233f31e925d89b8c30a0d823912e0
|
bf9ad245511f9fb28c84ab9603d1a90c85a1f7c2
|
/R/utilities.R
|
aeace9bfa370d22f559f31f962809b1249e84c58
|
[] |
no_license
|
dcava/wpp
|
20f28a14db5a26dd650431c30419767d68ccdeea
|
95dc77839caf5d1b48f1d3d93aa8df05f0c60aee
|
refs/heads/master
| 2022-12-10T01:46:23.392824
| 2020-09-15T06:41:41
| 2020-09-15T06:41:41
| 31,527,929
| 1
| 0
| null | 2020-09-15T05:45:12
| 2015-03-02T07:15:33
|
HTML
|
UTF-8
|
R
| false
| false
| 5,467
|
r
|
utilities.R
|
spm <- function(impresults) {
summary(pool(as.mira(impresults)))
}
plotdf <- function (impresult) {
if (class(impresult)=="coxph") {
plotout <- tidy(impresult)
plotout %<>%
select(
term,
estimate,
conf.low,
conf.high,
std.error = robust.se,
p.value)
return(plotout)
} else {
plotout <- data.frame(spm(impresult))
plotout %<>%
transmute(
term = rownames(plotout),
estimate=est,
conf.low=lo.95,
conf.high = hi.95,
std.error = se,
p.value = `Pr...t..`,
fmi=round(fmi, 2))
return(plotout)
}
}
mi.ci <- function(poolscalar) {
#Take the pooled scalar values for survival and generate 95% CIs
df.ps <- with(poolscalar, list(df = df, est = qbar, var=t))
mult <- qt(0.95, df=df.ps$df)
upper <- round(df.ps$est + mult*sqrt(df.ps$var), 3)
lower <- round(df.ps$est - mult*sqrt(df.ps$var), 3)
return(str_c("5yr OS: ", round(df.ps$est,3), " (95% CI ", lower, "-", upper, ")"))
}
lichtrubin <- function(fit){
## pools the p-values of a one-sided test according to the Licht-Rubin method
## this method pools p-values in the z-score scale, and then transforms back
## the result to the 0-1 scale
## Licht C, Rubin DB (2011) unpublished
if (!is.mira(fit)) stop("Argument 'fit' is not an object of class 'mira'.")
fitlist <- fit$analyses
if (!inherits(fitlist[[1]], "htest")) stop("Object fit$analyses[[1]] is not an object of class 'htest'.")
m <- length(fitlist)
p <- rep(NA, length = m)
for (i in 1:m) p[i] <- fitlist[[i]]$p.value
z <- qnorm(p) # transform to z-scale
num <- mean(z)
den <- sqrt(1 + var(z))
pnorm( num / den) # average and transform back
}
pool.km <- function(km.mira.object) {
results <- vector("list")
for (i in c("median", "LCL", "UCL")){
km.mira.object %>%
list.apply(summary) %>%
list.map(table) %>%
data.frame() %>%
select(contains(i)) %>%
rowMeans(na.rm = T) %>%
{
round(./365.25*12,1)
} -> results[[i]]
}
data.frame(results)
}
pool.5yr <- function(km.mira.object) {
results <- vector("list")
for (i in c("surv", "std.err")){
km.mira.object %>%
list.apply(summary, times=1826.25) %>%
list.select(i) %>%
list.flatten() %>%
list.rbind() %>%
data.frame() -> results[[i]]
}
results
}
cavcombine <- function(dk, df, display = TRUE)
#dk = vector of chi-squared values, df = degrees of freedom
#My variant of the miceadds::micombine.chisquare function
{
M <- length(dk)
mean.dk <- mean(dk)
sdk.square <- var(sqrt(dk))
Dval <- (mean.dk/df - (1 - 1/M) * sdk.square)/(1 + (1 + 1/M) *
sdk.square)
df2 <- (M - 1)/df^(3/M) * (1 + M/(M + 1/M)/sdk.square)^2
pval <- pf(Dval, df1 = df, df2 = df2, lower.tail = F)
chisq.approx <- Dval * df
p.approx <- 1 - pchisq(chisq.approx, df = df)
res <- c(D = Dval, p = pval, df = df, df2 = df2, chisq.approx = chisq.approx,
p.approx = p.approx)
if (display) {
message("Combination of Chi Square Statistics for Multiply Imputed Data\n")
message(paste("Using", M, "Imputed Data Sets\n"))
message(paste("F(", df, ",", round(df2, 2), ")", "=",
round(Dval, 3), " p=", round(pval, 5), sep = ""), "\n")
message(paste("Chi Square Approximation Chi2(", df, ")",
"=", round(chisq.approx, 3), " p=", round(p.approx,
5), sep = ""), "\n")
}
invisible(res)
}
summary.MIresult<-function(object,...,alpha=0.05, logeffect=FALSE){
#My version of the MIresult summary function, easy to use with knitr
#message("Multiple imputation results:\n")
#lapply(object$call, function(a) {cat(" ");print(a)})
out<-data.frame(results=coef(object), se=sqrt(diag(vcov(object))))
crit<-qt(alpha/2,object$df, lower.tail=FALSE)
out$"(lower"<-out$results-crit*out$se
out$"upper)"<-out$results+crit*out$se
out$"missInfo" <-paste(round(100*object$missinfo), "%")
return(out)
}
stable.weights <- function (ps1, stop.method = NULL, estimand = NULL)
{
if (class(ps1) == "ps") {
if (is.null(estimand))
estimand <- ps1$estimand
if (!(estimand %in% c("ATT", "ATE")))
stop("estimand must be either \"ATT\" or \"ATE\".")
if (estimand != ps1$estimand) {
warning("Estimand specified for get.weights() differs from the estimand used to fit the ps object.")
}
if (length(stop.method) > 1)
stop("More than one stop.method was selected.")
if (!is.null(stop.method)) {
stop.method.long <- paste(stop.method, ps1$estimand,
sep = ".")
i <- match(stop.method.long, names(ps1$w))
if (is.na(i))
stop("Weights for stop.method=", stop.method,
" and estimand=", estimand, " are not available. Please a stop.method and used when fitting the ps object.")
}
else {
warning("No stop.method specified. Using ", names(ps1$ps)[1],
"\n")
i <- 1
}
if (estimand == "ATT") {
w <- with(ps1, treat + (1 - treat) * ps[[i]]/(1 -
ps[[i]]))
return(w)
}
else if (estimand == "ATE") {
w <- with(ps1, ifelse(treat==1, (mean(ps[[i]]))/ps[[i]], (mean(1-ps[[i]]))/(1-ps[[i]])))
return(w)
}
}
}
|
60fe96d1eb329055faeddb44e2af453f8ba32c54
|
fa19d30ddd68641f7baf190b3c192dbd4ac04911
|
/r-programming/assignment-2/cachematrix.R
|
e6512c3939165691e4fd0f8ec5b0d89e6049697b
|
[] |
no_license
|
rawat-he/datasciencecoursera
|
6bb0b07b9e8a9520252388270571ade8549a4274
|
fd3d37f2deab6d61519d7a8a1c8ef57473d5bf86
|
refs/heads/master
| 2021-05-30T10:33:52.690449
| 2015-07-26T12:52:53
| 2015-07-26T12:52:53
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,988
|
r
|
cachematrix.R
|
## makeCacheMatrix - This function creates a special "matrix" object that can cache its inverse.
## set - This function set the value of matrix 'x' for which matrix inverse needs to be cached
# - sets value of matrix cache 'inv' value to NULL
# - This function can also be called directly
# - mat <- makeCacheMatrix ()
# - mat$set(matrix(c(1,2,3,4), nrow=2,ncol=2))
## get - Returns the current value of matrix 'x'
## setinv - set the inverse of matrix to 'inv'
## getinv - returns the inverse value of matrix 'inv'
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinv <- function(matrix.inv) inv <<- matrix.inv
getinv <- function() inv
list(set = set, get = get,
setinv = setinv,
getinv = getinv)
}
## This function computes the inverse of the special "matrix" returned by makeCacheMatrix above.
## If the inverse has already been calculated (and the matrix has not changed),
## then the cachesolve should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
m <- x$getinv()
if(!is.null(m)) {
message("getting matrix inverse from cache")
return(m)
}
data <- x$get()
m <- solve(data, ...)
message("set new matrix inverse to cache")
x$setinv(m)
m
}
## Sample Run
# mat <- makeCacheMatrix (matrix(c(1,2,3,4), nrow=2,ncol=2))
# cacheSolve(mat)
#set new matrix inverse to cache
# [,1] [,2]
#[1,] -2 1.5
#[2,] 1 -0.5
#cacheSolve(mat)
#getting matrix inverse from cache
# [,1] [,2]
#[1,] -2 1.5
#[2,] 1 -0.5
# mat <- makeCacheMatrix()
# mat$set(matrix(c(1,2,3,4), nrow=2,ncol=2))
#set new matrix inverse to cache
# [,1] [,2]
#[1,] -2 1.5
#[2,] 1 -0.5
#cacheSolve(mat)
#getting matrix inverse from cache
# [,1] [,2]
#[1,] -2 1.5
#[2,] 1 -0.5
|
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