sampleSize {SSPA}R Documentation

User friendly interface to class 'SampleSize'

Description

User friendly interface to class "SampleSize"

Usage

  sampleSize(PilotData,
    method = c("deconv", "congrad", "tikhonov", "ferreira"),
    control = list(from = -6, to = 6, resolution = 2^9))

Arguments

PilotData

object of class 'PilotData'.

method

estimation method one of 'deconv', 'congrad', 'tikhonov' or 'ferreira'. See 'Details'.

control

A list of control parameters. See 'Details'.

Details

The default method is 'deconv' which is an kernel deconvolution density estimator implementated using fft. The 'nncg' is a nonnegative conjugate gradient algorithm based on R's implementation see optim. 'tikonov' implements ridge-regression with optimal penalty selection using the L-curve approach. Higher order penalties are possible as well using a transformation to standard form (see Hansen).

The 'control' argument is a list that can supply any of the following components. Per method logical checks are performed.

Value

object of class SampleSize.

Author(s)

Maarten van Iterson

References

van Iterson, M., P. 't Hoen, P. Pedotti, G. Hooiveld, J. den Dunnen, G. van Ommen, J. Boer, and R. de Menezes (2009): 'Relative power and sample size analysis on gene expression profiling data,' BMC Genomics, 10, 439–449.

Ferreira, J. and A. Zwinderman (2006a): 'Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method,' The International Journal of Biostatistics, 2, 1.

Ferreira, J. and A. Zwinderman (2006b): 'Approximate Sample Size Calculations with Microarray Data: An Illustration,' Statistical Applications in Genetics and Molecular Biology, 5, 1.

Hansen, P. (2010): Discrete Inverse Problems: Insight and Algorithms, SIAM: Fun- damentals of algorithms series.

Langaas, M., B. Lindqvist, and E. Ferkingstad (2005): 'Estimating the proportion of true null hypotheses, with application to DNA microarray data,' Journal of the Royal Statistical Society Series B, 67, 555–572.

Storey, J. (2003): 'The positive false discovery rate: A bayesian interpretation and the q-value,' Annals of Statistics, 31, 2013–2035.

See Also

optim

Examples

m <- 5000 ##number of genes
J <- 10 ##sample size per group
pi0 <- 0.8 ##proportion of non-differentially expressed genes
m0 <- as.integer(m*pi0)
mu <- rbitri(m - m0, a = log2(1.2), b = log2(4), m = log2(2)) #effect size distribution
data <- simdat(mu, m=m, pi0=pi0, J=J, noise=NULL)
library(genefilter)
stat <- rowttests(data, factor(rep(c(0, 1), each=J)), tstatOnly=TRUE)$statistic
pd <- pilotData(statistics=stat, samplesize=sqrt(J/2), distribution='norm')
ss <- sampleSize(pd, method='deconv')
plot(ss)

[Package SSPA version 2.20.0 Index]