MSFDR: Multiple-Stage False Discovery Rate procedure

Description Usage Arguments Value References Examples

View source: R/step_HLfit.R

Description

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 accross 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).

Usage

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MSFDR(nullfit, fullfit, q = 0.05, verbose = TRUE)

Arguments

nullfit

An ML fit to the minimal model to start the forward selection from; an object of class HLfit.

fullfit

An ML fit to the maximal model; an object of class HLfit.

q

Nominal error rate of the underlying FDR procedure (expected proportion of incorrectly rejected null out of the rejected). Benjamini and Gavrilov (2009) recommend q=0.05 on the basis of minimizing mean-squared prediction error in various simulation conditions considering only linear models.

verbose

Whether to print information about the progress of the procedure.

Value

The fit of the final selected model; an object of class HLfit.

References

A simple forward selection procedure based on false discovery rate control. Ann. Appl. Stat, 3, 179-198 (2009).

Examples

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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)
}

Example output

spaMM (version 2.7.1) is loaded.
Type 'help(spaMM)' for a short introduction,
and news(package='spaMM') for news.

spaMM documentation built on June 2, 2021, 9:06 a.m.

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