raw.pvalues: Calculation of classical multiple testing statistics and...

In FAMT: Factor Analysis for Multiple Testing (FAMT) : Simultaneous Tests under Dependence in High-Dimensional Data

Calculation of classical multiple testing statistics and p-values

Description

Calculates for each gene expression, the Fisher test statistics and the corresponding p-value for H0: the gene expression does not depend on the experimental condition in a model with possible covariates.

Usage

raw.pvalues(data, x = 1, test = x[1])

Arguments

data	'FAMTdata' object, see as.FAMTdata
x	Column number(s) corresponding to the experimental condition and the optional covariates (1 by default) in the 'covariates' data frame.
test	Column number corresponding to the experimental condition (x[1] by default) of interest in the multiple testing procedure.

Value

pval	Vector containing the p-values
test	Vector containing the F statistics
resdf	Residual degrees of freedom

Author(s)

David Causeur

See Also

as.FAMTdata

Examples

data(expression)
data(covariates)
data(annotations)

# Create the 'FAMTdata'
############################################
chicken = as.FAMTdata(expression,covariates,annotations,idcovar=2)
# 'FAMTdata' summary
summaryFAMT(chicken)

# Calculation of classical p-values
############################################
# test on the 6th covariate: 
rawpval = raw.pvalues(chicken,x=6)
hist(rawpval$pval)

# with a supplementary covariate (third column of the covariates data frame)
## Not run: rawpval = raw.pvalues(chicken,x=c(3,6),test=6)
## Not run: hist(rawpval$pval)

FAMT documentation built on May 9, 2022, 5:07 p.m.