MD2sample (version 1.0.0)

twosample_test_adjusted_pvalue: Adjusted p values

Description

This function runs a number of two sample tests using Rcpp and parallel computing and then finds the correct p value for the combined tests.

Usage

twosample_test_adjusted_pvalue(
  x,
  y,
  vals_x = NA,
  vals_y = NA,
  B = c(5000, 1000),
  nbins = c(5, 5),
  minexpcount = 5,
  samplingmethod = "Binomial",
  Ranges = matrix(c(-Inf, Inf, -Inf, Inf), 2, 2),
  DoTransform = TRUE,
  rnull,
  SuppressMessages = FALSE,
  maxProcessor,
  doMethods
)

Value

NULL, results are printed out.

Arguments

x

Continuous data: either a matrix of numbers, or a list with two matrices called x and y. if it is a matrix Observations are in different rows. Discrete data: a vector of counts or a matrix with columns named vals_x, vals_y, x and y.

y

a matrix of numbers if data if data is continuous or a vector of counts if data is discrete.

vals_x

=NA, a vector of values for discrete random variable, or NA if data is continuous.

vals_y

=NA, a vector of values for discrete random variable, or NA if data is continuous.

B

=c(5000, 1000), number of simulation runs for permutation test and for estimation of the empirical distribution function.

nbins

=c(5, 5), number of bins for chi square tests (2D only).

minexpcount

= 5, minimum required expected counts for chi-square tests.

samplingmethod

="Binomial" or "independence" for discrete data.

Ranges

=matrix(c(-Inf, Inf, -Inf, Inf),2,2) a 2x2 matrix with lower and upper bounds.

DoTransform

=TRUE, should data be transformed to interval (0,1)?

rnull

routine for parametric bootstrap.

SuppressMessages

= FALSE, print informative messages?

maxProcessor

number of cores for parallel processing.

doMethods

Which methods should be included? If missing a small number of methods that generally have good power are used.

Details

For details consult the vignette("MD2sample","MD2sample")

Examples

#Note that the number of simulation runs B is very small to
#satisfy CRAN's run time constraints. 
#Two continuous data sets from a multivariate normal:
x = mvtnorm::rmvnorm(100, c(0,0))
y = mvtnorm::rmvnorm(120, c(0,0))
twosample_test_adjusted_pvalue(x, y, maxProcessor=1, B=20)
#Two discrete data sets from some distribution:
x = table(sample(1:4, size=1000, replace = TRUE))
y = table(sample(1:4, size=500, replace = TRUE, prob=c(1, 1.5, 1, 1)))
twosample_test_adjusted_pvalue(x, y, rep(1:2,2), rep(1:2, each=2), maxProcessor=1, B=20)