Multiple-Stage False Discovery Rate procedure
This implements the procedure described by Benjamini and Gavrilov (2009) for model-selection of fixed-effect terms based on False Discovery Rate (FDR) concepts. It uses forward selection based on penalized likelihoods. The penalization for the number of parameters is distinct from that in Akaike's Information Criterion, and variable across iterations of the algorithm (but functions from the stats package for AIC-based model-selection are still called, so that some screen messages refer to AIC).
MSFDR(nullfit, fullfit, q = 0.05, verbose = TRUE)
nullfit |
An ML fit to the minimal model to start the forward selection from; an object of class |
fullfit |
An ML fit to the maximal model; an object of class |
q |
Nominal error rate of the underlying FDR procedure (expected proportion of
incorrectly rejected null out of the rejected). Benjamini and Gavrilov (2009) recommend |
verbose |
Whether to print information about the progress of the procedure. |
The fit of the final selected model; an object of class HLfit.
A simple forward selection procedure based on false discovery rate control. Ann. Appl. Stat, 3, 179-198 (2009).
if (spaMM.getOption("example_maxtime")>1.4) {
data("wafers")
nullfit <- fitme(y~1+(1|batch), data=wafers,family=Gamma(log))
fullfit <- fitme(y ~X1+X2+X1*X3+X2*X3+I(X2^2)+(1|batch), data=wafers, family=Gamma(log))
MSFDR(nullfit=nullfit,fullfit=fullfit)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.