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
1 2 3 4 5 6 7 8 9 10 | 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)
|
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