selectModel: Select Appropriate Linear Model

Description

Select the best fitting linear model for each gene by minimizing an information criterion.

Usage

selectModel(y, designlist, criterion="aic", df.prior=0, s2.prior=NULL, s2.true=NULL, ...)

Arguments

a matrix-like data object, containing log-ratios or log-values of expression for a series of microarrays. Any object class which can be coerced to matrix is acceptable including numeric, matrix, MAList, marrayNorm, ExpressionSet or PLMset.

designlist

list of design matrices

criterion

information criterion to be used for model selection, "aic", "bic" or "mallowscp".

df.prior

prior degrees of freedom for residual variances. See squeezeVar

s2.prior

prior value for residual variances, to be used if df.prior>0.

s2.true

numeric vector of true variances, to be used if criterion="mallowscp".

...

other optional arguments to be passed to lmFit

Value

IC: matrix of information criterion scores, rows for probes and columns for models
pref: factor indicating the model with best (lowest) information criterion score

Details

This function chooses, for each probe, the best fitting model out of a set of alternative models represented by a list of design matrices. Selection is by Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC) or by Mallow's Cp.

The criteria have been generalized slightly to accommodate an information prior on the variances represented by s2.prior and df.prior or by s2.post. Suitable values for these parameters can be estimated using squeezeVar.

Examples

Run this code

nprobes <- 100
narrays <- 5
y <- matrix(rnorm(nprobes*narrays),nprobes,narrays)
A <- c(0,0,1,1,1)
B <- c(0,1,0,1,1)
designlist <- list(
  None=cbind(Int=c(1,1,1,1,1)),
  A=cbind(Int=1,A=A),
  B=cbind(Int=1,B=B),
  Both=cbind(Int=1,AB=A*B),
  Add=cbind(Int=1,A=A,B=B),
  Full=cbind(Int=1,A=A,B=B,AB=A*B)
)
out <- selectModel(y,designlist)
table(out$pref)

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