glmmEP.control {glmmEP} | R Documentation |
Controlling generalized linear mixed model fitting via expectation propagation
Description
Function for optional use in calls to glmmEP()
to control convergence values and other specifications for expectation propagation-based fitting of generalized linear mixed models.
Usage
glmmEP.control(confLev=0.95,BFGSmaxit=500,BFGSreltol=1e-10,
EPmaxit=100,EPreltol=1e-5,NMmaxit=100,NMreltol=1e-10,
quiet=FALSE,preTransfData=TRUE)
Arguments
confLev |
Confidence level of confidence intervals expressed as a proportion (i.e. a number between 0 and 1). The default is 0.95 corresponding to 95% confidence intervals. |
BFGSmaxit |
Positive integer specifying the maximum number of iterations in the Broyden-Fletcher-Goldfarb-Shanno optimization phase. The default is 500. |
BFGSreltol |
Positive number specifying the relative tolerance value as defined in the R function optim() in the Broyden-Fletcher-Goldfarb-Shanno optimization phase. The default is 1e-10. |
EPmaxit |
Positive integer specifying the maximum number of iterations in the expectation propagation message passing iterations. The default is 100. |
EPreltol |
Positive number specifying the relative tolerance value for the expectation propagation message passing iterations. The default is 1e-5. |
NMmaxit |
Positive integer specifying the maximum number of iterations in the Nelder-Mead optimization phase. The default is 100. |
NMreltol |
Positive number specifying the relative tolerance value as defined in the R function optim() in the Nelder-Mead optimization phase. The default is 1e-10. |
quiet |
Flag for specifying whether or not glmmEP() runs ‘quietly’ or with progress reports printed to the screen. The default is FALSE. |
preTransfData |
Flag for specifying whether or not the predictor data are pre-transformed to the unit interval for fitting, with estimates, predictions and confidence intervals transformed to match the units of the original data before. The default is TRUE. |
Value
A list containing values of each of the fifteen control parameters, packaged to supply the control
argument to glmmEP
. The values for glmmEP.control
can be specified in the call to glmmEP
.
Author(s)
Matt Wandmatt.wand@uts.edu.au and James Yujamescfyu@gmail.com
References
Hall, P., Johnstone, I.M., Ormerod, J.T., Wand, M.P. and Yu, J.C.F. (2018). Fast and accurate binary response mixed model analysis via expectation propagation. Submitted.
Examples
library(glmmEP)
# Obtain simulated data corresponding to the simulation study in Section 4.1.2. of
# Hall et al. (2018):
dataObj <- glmmSimData(seed=54321)
y <- dataObj$y
Xfixed <- dataObj$Xfixed
Xrandom <- dataObj$Xrandom
idNum <- dataObj$idNum
# Obtain and summarise probit mixed model fit with user control
# of some of the parameters in glmmEP.control():
myNMmaxit <- 500 ; myBFGSreltol <- 0.001
fitSimData <- glmmEP(y,Xfixed,Xrandom,idNum,
control=glmmEP.control(NMmaxit=myNMmaxit,BFGSreltol=myBFGSreltol,quiet=TRUE))
summary(fitSimData)