Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then "added on" to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
|Author||Francis K.C. Hui, Samuel Mueller, A.H. Welsh|
|Date of publication||2016-10-07 09:23:08|
|Maintainer||Francis Hui <firstname.lastname@example.org>|
build.start.fit: Constructs a start fit for use in the 'rpql' function
calc.marglogL: Calculate the marginal log-likelihood for a GLMM fitted using...
gendat.glmm: Simulates datasets based on a Generalized Linear Mixed Model...
lseq: Generates a sequence of tuning parameters on the log scale
nb2: A negative binomial family
rpql: Joint effects selection in GLMMs using regularized PQL.
rpql-package: Joint effects selection in GLMMs using regularized PQL
rpqlseq: Wrapper function for joint effects selection in GLMMs using...
summary.rpql: Summary of GLMM fitted using regularized PQL.