row.kgrp.shapiro {HybridMTest} | R Documentation |
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.
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] )