sampleSize {SSPA} | R Documentation |
User friendly interface to class "SampleSize"
sampleSize( PilotData, method = c("deconv", "congrad", "tikhonov", "ferreira"), control = list(from = -6, to = 6, resolution = 2^9) )
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'. |
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:'
object of class SampleSize.
Maarten van Iterson
Langaas, Storey, Ferreira, Hansen, van Iterson
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)