uniTest-methods {xps} | R Documentation

## A Two-Group Unitest

### Description

Unitest performs a a two group uni-test such as the `t.test` on each row of the expression dataframe.
The Unitest returns a dataframe containing the results of the test.

Usage

`uniTest(object)`
`uniTest(object, value)<-`

### Arguments

`object` | object of class `UniFilter`.
`value` | character vector `c(type, alternative, correction, numperm, mu, paired, conflevel, varequ)`

### Details

The method `uniTest` initializes the following parameters:

| `type`: | a character string specifying the type of test: currently `"t.test"` (default) or `"normal.test"`.
| `alternative`: | a character string specifying the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"less"`.
| `correction`: | a correction to adjust p-values for multiple comparisons:
|  | `correction="none"`: no correction (default).
|  | `correction="bonferroni"`: Bonferroni correction.
|  | `correction="fdr"`: correction for false discovery rate (Benjamini & Hochberg).
|  | `correction="hochberg"`: Hochberg correction.
|  | `correction="holm"`: Holm correction.
|  | `correction="wy"`: Westfall-Young step-down adjusted p-chance (E.Manduchi).
| `numperm`: | optional number of permutations used to determine p-chance (default is 0).
| `mu`: | a number indicating the true value of the difference in means for a two sample test (default is 0).
| `paired`: | a logical indicating whether you want a paired uni-test (default is FALSE).
| `conflevel`: | confidence level of the interval (default is 0.95).
| `varequ`: | a logical variable indicating whether to treat the two variances as being equal. If `TRUE` then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used (default is FALSE).

### Value

An initialized `UniFilter` object.

### Author(s)

Christian Stratowa

### References

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289–300.

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.

Westfall P.H. and Young S.S. (1993) Resampling-based multiple testing:examples and methods for p-value adjustment. Wiley series in probability and mathematical statistics; Wiley.

Dudoit S., Yang Y.H., Callow M.J., Speed T.P. (2000) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Technical report 578; UC Berkeley.

Manduchi E. (2000) Software: tpWY, see: http://www.cbil.upenn.edu/tpWY/

### Examples

    unifltr <- UniFilter()
    uniTest(unifltr) <- c("t.test","two.sided","none",0,0.0,FALSE,0.98,TRUE)
    str(unifltr)

[Package xps version 1.0.2 Index]