irwsva.build: A function for estimating surrogate variables by estimating...
In jtleek/sva-devel: Surrogate Variable Analysis

Description Usage Arguments Value Examples

This function is the implementation of the iteratively re-weighted least squares approach for estimating surrogate variables. As a buy product, this function produces estimates of the probability of being an empirical control. See the function empirical.controls for a direct estimate of the empirical controls.

1	irwsva.build(dat, mod, mod0 = NULL, n.sv, B = 5)

`dat`	The transformed data matrix with the variables in rows and samples in columns
`mod`	The model matrix being used to fit the data
`mod0`	The null model being compared when fitting the data
`n.sv`	The number of surogate variables to estimate
`B`	The number of iterations of the irwsva algorithm to perform

sv The estimated surrogate variables, one in each column

pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity

pprob.b A vector of the posterior probabilities each gene is affected by mod

n.sv The number of significant surrogate variables

library(bladderbatch)
data(bladderdata)
dat <- bladderEset[1:5000,]

pheno = pData(dat)
edata = exprs(dat)
mod = model.matrix(~as.factor(cancer), data=pheno)

n.sv = num.sv(edata,mod,method="leek")
res <- irwsva.build(edata, mod, mod0 = NULL,n.sv,B=5)