sampleSizeParameters: A generic function to calculate sample size parameters
In clippda: A package for the clinical proteomic profiling data analysis

Description Usage Arguments Value Author(s) References Examples

This generic function computes input parameters for the sample size calculation function.

1	sampleSizeParameters(Data,intraclasscorr,signifcut, ...)

`Data`	An object of `aclinicalProteomicsData` class.
`intraclasscorr`	An object of `numeric` class. It is a known value of the intraclass correlation, or an estimate from a pilot data.
`signifcut`	An object of `numeric` class. It is significance threshold (usually, taken to be 0.05 in the analysis of the protein profiling studies).
`...`	Some methods for this generic function may take additional, optional arguments. At present none do.

A list of parameters:

`Corr`	the intraclass correlation from your pilot data.
`techVar`	the technical variance from your pilot data.
`bioVar`	the biological variance from your pilot data.
`DIFF`	the clinically important difference from your pilot data.
`no.peaks`	the number of peaks detected by the Biomarker wizard.

Stephen Nyangoma

Nyangoma SO, Ferreira JA, Collins SI, Altman DG, Johnson PJ, and Billingham LJ: Sample size calculations for planning clinical proteomic profiling studies using mass spectrometry. Bioinformatics, 2009, Submitted

Smyth GK, et al.: Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 2005, 21, 2067 - 75

Smyth GK: Linear models and emperical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3, 1, Article 3

intraclasscorr  <-  0.60 #cut-off for intraclass correlation

signifcut <- 0.05      #significance cut-off

data(liverdata)

data(liver_pheno)

OBJECT=new("aclinicalProteomicsData")

OBJECT@rawSELDIdata=as.matrix(liverdata)
OBJECT@covariates=c("tumor" ,    "sex")
OBJECT@phenotypicData=as.matrix(liver_pheno)
OBJECT@variableClass=c('numeric','factor','factor')
OBJECT@no.peaks=53


sampleSizeParameters(OBJECT,intraclasscorr,signifcut)