sampleSize: User friendly interface to class 'SampleSize'

In SSPA: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data

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 a 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. The 'control' argument is a list that can supply any of the following components, however per method some logical checks are built-in:

• deconv:

  • method:'deconv', 'ferreira'

  • pi0Method:the pi0 estimation method one of 'Langaas', 'Storey', 'Ferreira', 'Userdefined'

  • pi0:if method = 'ferreira' grid pi0-value need to be suppled e.g. seq(0.1, 0.99, 0.01)

  • adjust:Default TRUE, adjust pi0 esitmate if density of effect size is somewhere negative.

  • a:Adjust pi0 better approach suggested by Efron. Symmetric range around zero of size 0.5.

  • bandwith:Default NULL uses 1/sqrt(log(length(statistics)))

  • kernel:Either 'fan', 'wand', 'sinc' kernels can be used.

  • from:Density of effect sizes should be estimated from = -6

  • to:6

  • resolution:Density of effect sizes should be estimated on 2^9 points.

  • verbose:Default FALSE if TRUE additional information is printed to the console.

• congrad:

  • integration:'midpoint', 'trapezoidal', 'simpson'

  • scale:'pdfstat', 'cdfstat', 'cdfpval'

  • trim:0.01, 0.99

  • symmetric:TRUE

  • bin:'epdf', 'ecdf'

  • from:-6

  • to:6

  • resolution:500

  • verbose:Default FALSE if TRUE additional information is printed to the console.

• tikhonov:

  • integration:'midpoint', 'trapezoidal', 'simpson'

  • scale:'pdfstat', 'cdfstat', 'cdfpval'

  • trim:0.01, 0.99

  • symmetric:TRUE

  • bin:'epdf', 'ecdf'

  • from:-6

  • to:6

  • resolution:500

  • modelselection:'lcurve', 'gcv', 'aic'

  • log:TRUE

  • penalty:0

  • lambda:10^seq(-10, 10, length=100)

  • verbose:Default FALSE if TRUE additional information is printed to the console.

• 'ferreira:'

Value

object of class SampleSize.

Author(s)

Maarten van Iterson

References

Langaas, Storey, Ferreira, Hansen, van Iterson

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)

SSPA documentation built on Nov. 8, 2020, 5:50 p.m.