sampleSizeParameters {clippda} | R Documentation

## A generic function to calculate sample size parameters

### Description

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

### Usage

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

### Arguments

`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.

### Value

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.

### Author(s)

Stephen Nyangoma

### References

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

### Examples

    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)

[Package clippda version 1.42.0 Index]