TopDown_TMTI: TopDown TMTI algorithm for estimating a 1-alpha confidence...

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

TopDown TMTI algorithm for estimating a 1-alpha confidence set for the number of false hypotheses among a set.

Description

TopDown TMTI algorithm for estimating a 1-alpha confidence set for the number of false hypotheses among a set.

Usage

TopDown_TMTI(
  pvals,
  subset = NULL,
  alpha = 0.05,
  gammaList = NULL,
  verbose = TRUE,
  tau = NULL,
  K = NULL,
  is.sorted = FALSE,
  mc.cores = 1L,
  chunksize = 4 * mc.cores,
  direction = "binary",
  ...
)

Arguments

pvals	A vector of p-values.
subset	Numeric vector specifying a subset a p-values to estimate a confidence set for the number of false hypotheses for. Defaults to NULL corresponding to estimating a confidence set for the number of false hypotheses in the entire set.
alpha	Level in [0,1] at which to generate confidence set. Defaults to 0.05.
gammaList	List of pre-specified gamma functions. If NULL, the functions will be approximated by bootstrap assuming independence. Defaults to NULL.
verbose	Logical, indicating whether or not to write out the progress. Defaults to TRUE.
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.
is.sorted	Logical, indicating whether the supplied p-values are already is.sorted. Defaults to FALSE.
mc.cores	Number of cores to parallelize onto.
chunksize	Integer indicating the size of chunks to parallelize. E.g., if setting chunksize = mc.cores, each time a parallel computation is set up, each worker will perform only a single task. If mc.cores > chunksize, some threads will be inactive.
direction	A string indicating whether to perform a binary search ('binary'/'b') or decreasing ('decreasing'/'d') search. Defaults to 'binary', which has better computational complexity.
...	Additional parameters.

Value

A 1-alpha lower bound for the number of false hypotheses among the set of supplied p-values

Examples

## Simulate some p-values
## The first 10 are from false hypotheses, the next 10 are from true
pvals = c(
  rbeta(10, 1, 20), ## Mean value of .05
  runif(10)
)
TopDown_TMTI(pvals)

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