decideTests {limma} | R Documentation

## Compute Matrix of Hypothesis Test Results

### Description

Classify a series of related t-statistics as up, down or not significant.

### Usage

    decideTests(object,method="separate",adjust.method="fdr",p.value=0.05)

### Arguments

`object` | `MArrayLM` object output from `eBayes` from which the t-statistics may be extracted.
`method` | character string specify how probes and contrasts are to be combined in the multiple testing strategy. Choices are `"separate"`, `"global"`, `"heirarchical"`, `"nestedF"` or any partial string.
`adjust.method` | character string specifying p-value adjustment method. See `p.adjust` for possible values.
`p.value` | numeric value between 0 and 1 giving the desired size of the test

### Details

These functions implement multiple testing procedures for determining whether each statistic in a matrix of t-statistics should be considered significantly different from zero. Rows of `tstat` correspond to genes and columns to coefficients or contrasts.

The setting `method="separate"` is equivalent to using `topTable` separately for each coefficient in the linear model fit, and will give the same lists of probes if `adjust.method` is the same. Note that the defaults for `adjust.method` are different for `decideTests` and `topTable`. `method="global"` will treat the entire matrix of t-statistics as a single vector of unrelated tests. `method="heirarchical"` adjusts down genes and then across contrasts. `method="nestedF"` adjusts down genes and then uses `classifyTestsF` to classify contrasts as significant or not for the selected genes.

### Value

An object of class `TestResults`. This is essentially a numeric matrix with elements `-1`, `0` or `1` depending on whether each t-statistic is classified as significantly negative, not significant or significantly positive respectively.

### Author(s)

Gordon Smyth

### See Also

An overview of linear model functions in limma is given by 5.LinearModels.

[Package limma version 1.9.6 Index]