multtest: mt.sample.teststat – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

mt.sample.teststat

Permutation distribution of test statistics and raw (unadjusted) p-values

Description

These functions provide tools to investigate the permutation distribution of test statistics, raw (unadjusted) p-values, and class labels.

Usage

mt.sample.teststat(V,classlabel,test="t",fixed.seed.sampling="y",B=10000,na=.mt.naNUM,nonpara="n")
mt.sample.rawp(V,classlabel,test="t",side="abs",fixed.seed.sampling="y",B=10000,na=.mt.naNUM,nonpara="n")
mt.sample.label(classlabel,test="t",fixed.seed.sampling="y",B=10000)

Arguments

`V`	A numeric vector containing the data for one of the variables (genes).
`classlabel`	A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k-1. For the `blockf` test option, observations may be divided into n/k blocks of k observations each. The observations are ordered by block, and within each block, they are labeled using the integers 0 to k-1.
`test`	A character string specifying the statistic to be used to test the null hypothesis of no association between the variables and the class labels. If `test="t"`, the tests are based on two-sample Welch t-statistics (unequal variances). If `test="t.equalvar"`, the tests are based on two-sample t-statistics with equal variance for the two samples. The square of the t-statistic is equal to an F-statistic for k=2. If `test="wilcoxon"`, the tests are based on standardized rank sum Wilcoxon statistics. If `test="f"`, the tests are based on F-statistics. If `test="pairt"`, the tests are based on paired t-statistics. The square of the paired t-statistic is equal to a block F-statistic for k=2. If `test="blockf"`, the tests are based on F-statistics which adjust for block differences (cf. two-way analysis of variance).
`side`	A character string specifying the type of rejection region. If `side="abs"`, two-tailed tests, the null hypothesis is rejected for large absolute values of the test statistic. If `side="upper"`, one-tailed tests, the null hypothesis is rejected for large values of the test statistic. If `side="lower"`, one-tailed tests, the null hypothesis is rejected for small values of the test statistic.
`fixed.seed.sampling`	If `fixed.seed.sampling="y"`, a fixed seed sampling procedure is used, which may double the computing time, but will not use extra memory to store the permutations. If `fixed.seed.sampling="n"`, permutations will be stored in memory. For the `blockf` test, the option `n` was not implemented as it requires too much memory.
`B`	The number of permutations. For a complete enumeration, `B` should be 0 (zero) or any number not less than the total number of permutations.
`na`	Code for missing values (the default is `.mt.naNUM=--93074815.62`). Entries with missing values will be ignored in the computation, i.e., test statistics will be based on a smaller sample size. This feature has not yet fully implemented.
`nonpara`	If `nonpara`="y", nonparametric test statistics are computed based on ranked data. If `nonpara`="n", the original data are used.

Value

For mt.sample.teststat, a vector containing B permutation test statistics.

For mt.sample.rawp, a vector containing B permutation unadjusted p-values.

For mt.sample.label, a matrix containing B sets of permuted class labels. Each row corresponds to one permutation.

Author(s)

Yongchao Ge, yongchao.ge@mssm.edu,
Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine.

Examples

# Gene expression data from Golub et al. (1999)
data(golub)

mt.sample.label(golub.cl,B=10)

permt<-mt.sample.teststat(golub[1,],golub.cl,B=1000)
qqnorm(permt)
qqline(permt)

permt<-mt.sample.teststat(golub[50,],golub.cl,B=1000)
qqnorm(permt)
qqline(permt)

permp<-mt.sample.rawp(golub[1,],golub.cl,B=1000)
hist(permp)

multtest

Resampling-based multiple hypothesis testing

v2.46.0

LGPL

Authors

Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit

Initial release