README.md
In emitanaka/rpql: Regularized PQL for Joint Selection in GLMMs
rpql: Regularized PQL for Joint Selection in GLMMs
rpql offers fast joint selection of fixed and random effects in
Generalized Linear Mixed Model (GLMMs) via regularization. The penalized
quasi-likelihood (PQL) is used as a loss fuction, and penalties are
added on to perform fixed and random effects selection. This method of
joint selection in GLMMs, referred to regularized, is fast compared to
information criterion and hypothesis testing (Hui et al., 2016).
Please note rpql is the core workshops function that performed
regularized PQL on a single set of tuning parameters. rpqlseq is a
wrapper to permit a sequence of tuning parameter values. The latter is
often what users want to use.
Installation
You can install the released version of rpql from
CRAN
with:
install.packages("rpql")
emitanaka/rpql documentation built on Aug. 12, 2019, 12:35 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
rpql: Regularized PQL for Joint Selection in GLMMs
rpql offers fast joint selection of fixed and random effects in
Generalized Linear Mixed Model (GLMMs) via regularization. The penalized
quasi-likelihood (PQL) is used as a loss fuction, and penalties are
added on to perform fixed and random effects selection. This method of
joint selection in GLMMs, referred to regularized, is fast compared to
information criterion and hypothesis testing (Hui et al., 2016).
Please note rpql is the core workshops function that performed
regularized PQL on a single set of tuning parameters. rpqlseq is a
wrapper to permit a sequence of tuning parameter values. The latter is
often what users want to use.
Installation
You can install the released version of rpql from CRAN with:
install.packages("rpql")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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