mt.sample.teststat {multtest} | R Documentation

## 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.

### See Also

`mt.maxT`, `mt.minP`, `golub`.

### 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)

[Package multtest version 1.6.0 Index]