xps (version 1.32.0)

uniTest-methods: 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)

Value

An initialized UniFilter object.

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).

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)