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="BH" or "fdr"`: correction for false discovery rate (Benjamini & Hochberg).
|  | `correction="BY"`: correction for false discovery rate (Benjamini & Yekutieli).
|  | `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.

Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165–1188.

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 1.4.10 Index]