Best R package function to prepare sampling distribution for stratified sampling - r

Question

I'm attempting to prepare a demonstration in R of how the repeated stratified random sampling of a small population results in a near-normal sampling distribution of means. As an example consider the R code below (which works but is very slow due to looping).
#Dummy population made up of dice throws - 18 per row
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
P1 <- as.data.frame(c(5,6,5,1,6,4,2,2,4,4,6,6,5,2,3,5,1,6))
P1$Zn <- 1
names(P1) <- c('Die','Zn')
Dt <- P1
P2 <- as.data.frame(c(2,5,4,5,5,5,3,3,2,5,6,1,2,5,4,3,6,1))
P2$Zn <- 2
names(P2) <- c('Die','Zn')
Dt <- rbind(Dt,P2)
# Empty dataframe to hold random draws
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Smps <- data.frame(Die = numeric(), Zn= numeric(),Drw = numeric())
# Draw stratifed samples one from each row
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
print(paste('Start','at',Sys.time()))
n <- 10000 # number of draws
r <- 2 # number of rows (the strata)
for (j in 1:n){
# for a 2 strata
for (i in 1:r){
#sub set strata
x <- subset(Dt, Dt$Zn == i)
# random sample
y <- x[sample(1:18,1),]
y$Drw <- j
#append sample
Smps <- rbind(Smps,y)
}
# report progress
if(right(j,3) == '000'){
print(paste(j,'at',Sys.time()))
flush.console()
}
}
# Compute the sample means
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Mns <-aggregate(Smps[, 1], list(Smps$Drw), mean)
# Density plot of means
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
d <- density(Mns$x)
plot(d,xlab = 'Means', las=1, main = '')
polygon(d, col="blue", border="blue")
I'm expecting there is an R package with a function that does this type of stratified sampling but I am struggling to find the one that works in the manner I can understand. Something that inputs a data frame with a grouping field and the number of samples to be drawn from each group is something I'm expecting has already been written to allow a repeat sampling by a group. Any pointers to examples that work would be appreciated. Ideally, I would like to prepare to say 100,000 stratified samples from a known population with many more strata and then plot the distribution of the means (but quickly)

Answer 1

After a while away from this problem, I found a package called 'fifer' (https://www.rdocumentation.org/packages/fifer/versions/1.1) which seem contained a stratified function in a package but unfortunately, this package does not work on the latest versions of R. I did, however, find a clever stratified function from Ananda Mahto (https://gist.github.com/mrdwab/6424112) which works well but at the cost of having a rather long function in your script rather than the one line of loading a package. My solution to the problem above using this function is below.
#Dummy population made up of dice throws - 18 per row
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
P1 <- as.data.frame(c(5,6,5,1,6,4,2,2,4,4,6,6,5,2,3,5,1,6))
P1$Zn <- 1
names(P1) <- c('Die','Zn')
Dt <- P1
P2 <- as.data.frame(c(2,5,4,5,5,5,3,3,2,5,6,1,2,5,4,3,6,1))
P2$Zn <- 2
names(P2) <- c('Die','Zn')
Dt <- rbind(Dt,P2)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Stratfed function from web
# https://gist.github.com/mrdwab/6424112
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
stratified <- function(df, group, size, select = NULL,
replace = FALSE, bothSets = FALSE) {
if (is.null(select)) {
df <- df
} else {
if (is.null(names(select))) stop("'select' must be a named list")
if (!all(names(select) %in% names(df)))
stop("Please verify your 'select' argument")
temp <- sapply(names(select),
function(x) df[[x]] %in% select[[x]])
df <- df[rowSums(temp) == length(select), ]
}
df.interaction <- interaction(df[group], drop = TRUE)
df.table <- table(df.interaction)
df.split <- split(df, df.interaction)
if (length(size) > 1) {
if (length(size) != length(df.split))
stop("Number of groups is ", length(df.split),
" but number of sizes supplied is ", length(size))
if (is.null(names(size))) {
n <- setNames(size, names(df.split))
message(sQuote("size"), " vector entered as:\n\nsize = structure(c(",
paste(n, collapse = ", "), "),\n.Names = c(",
paste(shQuote(names(n)), collapse = ", "), ")) \n\n")
} else {
ifelse(all(names(size) %in% names(df.split)),
n <- size[names(df.split)],
stop("Named vector supplied with names ",
paste(names(size), collapse = ", "),
"\n but the names for the group levels are ",
paste(names(df.split), collapse = ", ")))
}
} else if (size < 1) {
n <- round(df.table * size, digits = 0)
} else if (size >= 1) {
if (all(df.table >= size) || isTRUE(replace)) {
n <- setNames(rep(size, length.out = length(df.split)),
names(df.split))
} else {
message(
"Some groups\n---",
paste(names(df.table[df.table < size]), collapse = ", "),
"---\ncontain fewer observations",
" than desired number of samples.\n",
"All observations have been returned from those groups.")
n <- c(sapply(df.table[df.table >= size], function(x) x = size),
df.table[df.table < size])
}
}
temp <- lapply(
names(df.split),
function(x) df.split[[x]][sample(df.table[x],
n[x], replace = replace), ])
set1 <- do.call("rbind", temp)
if (isTRUE(bothSets)) {
set2 <- df[!rownames(df) %in% rownames(set1), ]
list(SET1 = set1, SET2 = set2)
} else {
set1
}
}
# Empty dataframe to hold random draws
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Smps <- data.frame(Die = numeric(), Zn = numeric())
# Right function for reporting progress
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
right = function(text, num_char) {
substr(text, nchar(text) - (num_char-1), nchar(text))
}
# Draw stratifed samples one from each row
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
n <- 10000 # number of draws
for (j in 1:n){
y <- stratified(Dt,"Zn",1)
y <- cbind(y,j)
Smps <- rbind(Smps,y)
# report progress
if(right(j,3) == '000'){
print(paste(j,'at',Sys.time()))
flush.console()
}
}
# Compute the sample means
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Mns <-aggregate(Smps[, 1], list(Smps$j), mean)
# Density plot of means
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
d <- density(Mns$x)
plot(d,xlab = 'Means', las=1, main = '')
polygon(d, col="blue", border="blue")