sampleSize: A function for sample size calculations
In clippda: A package for the clinical proteomic profiling data analysis

Description Usage Arguments Details Value Author(s) References Examples

This generic function sampleSize calculates the protein variance and the sample size required to estimate the clinically important differences (DIFF). The input data are the consensus parameters of peaks with medium biological variation.

1	sampleSize(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.

The sample sizes are computed for various combinations of the power with values beta=c(0.90,0.80,0.70) and the significance values, alpha = c(0.001, 0.01,0.05). Note that here we use beta for power rather than the conventional 1-beta.

`protein_variance`	consensus protein variance
`replicate_correlation`	consensus intraclass correlation
`sample_size`	the sample size required

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 (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

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## SAMPLE SIZE
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#The function sampleSize calculates the biological variance, differences.
#These are the consensus values of peaks with median biological variation
# It also gives sample sizes for beta=c(0.90,0.80,0.70) and alpha = c(0.001, 0.01,0.05)

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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


sampleSize(OBJECT,intraclasscorr,signifcut)

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