row.slr.shapiro: Test normality of residuals for many variables.
In HybridMTest: Hybrid Multiple Testing

Description Usage Arguments Value Author(s) References Examples

For each row of the data matrix Y, use the Shapiro-Wilk test to determine if the residuals of simple linear regression on x are normally distributed.

1	row.slr.shapiro(Y, x)

`Y`	a data matrix with rows for variables and columns for subjects
`x`	a vector with values of the independent variables for regression of each row of Y.

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 normally distributed residuals for each row of Y

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

Patrick Royston (1982) Algorithm AS 181: The W test for Normality. Applied Statistics, 31, 176-180.

  
####################Correlation Study#####################
# load data
data(correlation.data)
# Read the expression values  
Y<-exprs(correlation.data)
# Read the phenotype
x<-pData(correlation.data)
x[,1]
#Shapiro Test
row.slr.shapiro(Y,x[,1])