twosample_test {R2sample} | R Documentation |
This function runs a number of two sample tests using Rcpp and parallel computing.
Description
This function runs a number of two sample tests using Rcpp and parallel computing.
Usage
twosample_test(
x,
y,
vals = NA,
TS,
TSextra,
wx = rep(1, length(x)),
wy = rep(1, length(y)),
B = 5000,
nbins = c(50, 10),
maxProcessor,
UseLargeSample,
samplingmethod = "independence",
doMethods = "all"
)
Arguments
x |
a vector of numbers if data is continuous or of counts if data is discrete. |
y |
a vector of numbers if data is continuous or of counts if data is discrete. |
vals |
=NA, a vector of numbers, the values of a discrete random variable. NA if data is continuous data. |
TS |
routine to calculate test statistics for non-chi-square tests |
TSextra |
additional info passed to TS, if necessary |
wx |
A numeric vector of weights of x. |
wy |
A numeric vector of weights of y. |
B |
=5000, number of simulation runs for permutation test |
nbins |
=c(50,10), number of bins for chi square tests. |
maxProcessor |
maximum number of cores to use. If missing (the default) no parallel processing is used. |
UseLargeSample |
should p values be found via large sample theory if n,m>10000? |
samplingmethod |
="independence" or "MCMC" for discrete data |
doMethods |
="all" Which methods should be included? If missing all methods are used. |
Value
A list of two numeric vectors, the test statistics and the p values.
Examples
R2sample::twosample_test(rnorm(1000), rt(1000, 4), B=1000)
myTS=function(x,y) {z=c(mean(x)-mean(y),sd(x)-sd(y));names(z)=c("M","S");z}
R2sample::twosample_test(rnorm(1000), rt(1000, 4), TS=myTS, B=1000)
vals=1:5
x=table(sample(vals, size=100, replace=TRUE))
y=table(sample(vals, size=100, replace=TRUE, prob=c(1,1,3,1,1)))
R2sample::twosample_test(x, y, vals)