row.kgrp.shapiro: Shapiro Wilk test of normality.
In HybridMTest: Hybrid Multiple Testing

Description Usage Arguments Value Author(s) References Examples

For each row of the expression matrix Y, use Shapiro-Wilks test to determine whether the residuals of one-way ANOVA (with groups defined by x) are normally distributed.

1	row.kgrp.shapiro(Y, x)

`Y`	the data matrix with variables in rows and observations (subjects) in columns
`x`	x is vector of group labels

A data.frame with three columns

`stat`	a vector with the Shapiro-Wilk test statistic for each row of Y
`pval`	a vector with the Shapiro-Wilk p-value for each row of Y
`ebp`	a vector with the estimated empirical Bayes probability of normality for each row of Y

Stan Pounds <stanley.pounds@stjude.org>; Demba Fofana <demba.fofana@stjude.org>

Patrick Royston (1982) An extension of Shapiro and Wilk's W test for normality to large samples. Applied Statistics, 31, 115-124. Patric Royston (1992) Algorithm As 181: The W test for Normality. Applied Statistics, 31, 176-180. Patric Royston (1995) Remarks As R94: A remark on Algorithm AS 181:The W test for normality. Applied Statistics, 44, 547-551

  
####################Three group comparison###################
# load data
data(GroupComp.data)
# Read the expression values   
brain.express.set <- exprs(GroupComp.data)
head(brain.express.set)
# Read the phenotype
brain.pheno.data <- pData(GroupComp.data)
brain.pheno.data[,1] 
#Shapiro Test of Normality
row.kgrp.shapiro(brain.express.set,brain.pheno.data[,1] )