gamma_bootstrapper_Ttest: Compute a list of TMTI CDFs for one- and two-sample test...

In TMTI: 'Too Many, Too Improbable' (TMTI) Test Procedures

Compute a list of TMTI CDFs for one- and two-sample test scenarios

Description

Compute a list of TMTI CDFs for one- and two-sample test scenarios

Usage

gamma_bootstrapper_Ttest(
  Y,
  X = NULL,
  n = Inf,
  B = 1000,
  mc.cores = 1L,
  tau = NULL,
  K = NULL
)

Arguments

Y	A d*m matrix of m response variables with d observations. Can contain missing values in places.
X	Null if one-sample, a vector with only two unique values if two-sample.
n	Number (or Inf) indicating what kind of minimum to consider. Defaults to Inf, corresponding to the global minimum.
B	Number of bootstrap replicates. Rule of thumb is to use at least 10 * m.
mc.cores	Integer denoting the number of cores to use when using parallelization, Defaults to 1, corresponding to single-threaded computations.
tau	Numerical (in (0,1)); threshold to use in tTMTI. If set to NULL, then either TMTI (default) or rtTMTI is used.
K	Integer; Number of smallest p-values to use in rtTMTI. If se to NULL, then either TMTI (default) or tTMTI is used.

Value

A list of bootstrapped TMTI CDFs that can be used directly in the CTP_TMTI function.

Examples

d = 100
m = 3

X = sample(LETTERS[1:2], d, replace = TRUE)
Y = matrix(rnorm(d * m), nrow = d, ncol = m)
pvalues = apply(Y, 2, function(y) t.test(y ~ X)$p.value)

gammaFunctions = gamma_bootstrapper_Ttest(Y, X) # Produces a list of CDFs
CTP_TMTI(pvalues, gammaList = gammaFunctions) # Adjusted p-values using the bootstrapped CDFs

TMTI documentation built on Aug. 10, 2022, 5:06 p.m.