Listing tables together from random samples from a generated population?
Hi:
There are two ways you could go about this: lists or arrays. It's
pretty easy to generate an array, a little more work to get the list.
I'm assuming the objective is to extract a chi-square statistic from
each table, so I'll show a couple of ways to do that, too.
library('plyr')
## Start with the data:
y<-data.frame(gender=sample(c('Male', 'Female'), size=100000,
replace=TRUE, c(0.5, 0.5)),
tea=sample(c('Yes', 'No'), size=100000,
replace=TRUE, c(0.5, 0.5)))
## Function to produce a table:
tabfun <- function(d) table(d[sample(seq_len(nrow(d)), 100), ])
x2stat <- function(m) chisq.test(m)$statistic
## Array version:
tbarr <- replicate(100, tabfun(y))
# X^2 statistics using apply() from base R and
# aaply() from plyr:
u1 <- apply(tablist, 3, x2stat)
u2 <- aaply(tablist, 3, x2stat)
## List version:
tblst <- vector('list', 100)
for(i in seq_along(tblst)) tblst[[i]] <- tabfun(y)
v1 <- unname(do.call(c, lapply(tblst, x2stat)))
v2 <- laply(tblst, x2stat)
From here, it's easy to do the histogram :)
HTH, Dennis
On Thu, Nov 10, 2011 at 12:48 PM, Simon Kiss <sjkiss at gmail.com> wrote:
.
HI there,
I'd like to show demonstrate how the chi-squared distribution works, so I've come up with a sample data frame of two categorical variables
y<-data.frame(gender=sample(c('Male', 'Female'), size=100000, replace=TRUE, c(0.5, 0.5)), tea=sample(c('Yes', 'No'), size=100000, replace=TRUE, c(0.5, 0.5)))
And I'd like to create a list of 100 different samples of those two variables and the resulting 2X2 contingency tables
table(.y[sample(nrow(.y), 100), ])
How would I combine these 100 tables into a list? I'd like to be able to go in and find some of the extreme values to show how the sampling distribution of the chi-square values.
I can already get a histogram of 100 different chi-squared values that shows the distribution nicely (see below), but I'd like to actually show the underlying tables, for demonstration's sake.
?.z<-vector()
for (i in 1:100) {
.z<-c(.z, chisq.test(table(.y[sample(nrow(.y), 200), ]))$statistic)
}
hist(.z, xlab='Chi-Square Value', main="Chi-Squared Values From 100 different samples asking\nabout gender and tea/coffee drinking")
abline(v=3.84, lty=2)
Thank you in advance,
Simon Kiss
*********************************
Simon J. Kiss, PhD
Assistant Professor, Wilfrid Laurier University
73 George Street
Brantford, Ontario, Canada
N3T 2C9
Cell: +1 905 746 7606
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