| selectModel {limma} | R Documentation |
Select the best fitting linear model for each gene by minimizing an information criterion.
selectModel(y, designlist, criterion="aic", df.prior=0, s2.prior=NULL, s2.true=NULL, ...)
y |
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 |
designlist |
list of design matrices |
criterion |
information criterion to be used for model selection, |
df.prior |
prior degrees of freedom for residual variances. See |
s2.prior |
prior value for residual variances, to be used if |
s2.true |
numeric vector of true variances, to be used if |
... |
other optional arguments to be passed to |
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.
List with components
IC |
matrix of information criterion scores, rows for probes and columns for models |
pref |
factor indicating the model with best (lowest) information criterion score |
Alicia Oshlack and Gordon Smyth
An overview of linear model functions in limma is given by 06.LinearModels.
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)