gSlc.control {gammSlice} | R Documentation |
Function for optional use in calls to gSlc()
to control Markov chain Monte Carlo sample sizes values and other specifications for slice sampling-based fitting of generalized additive mixed models.
gSlc.control(nBurn=5000,nKept=5000,nThin=5,fixedEffPriorVar=1e10, sdPriorScale=1e5,numBasis=NULL,preTransfData=TRUE,msgCode=1)
nBurn |
The length of the Markov chain Monte Carlo burnin. The first |
nKept |
The number of kept Markov chain Monte Carlo samples after the burnin period. The default value of |
nThin |
Thinning factor applied to the retained Markov chain Monte Carlo samples. Setting |
fixedEffPriorVar |
The variance in the independent zero mean Normal priors of the fixed effect parameters after the data of each predictor have been transformed to the interval [0,1]. The default value of |
sdPriorScale |
The scale parameter in the Half Cauchy priors on standard deviation parameters after the data of each predictor have been transformed to the interval [0,1]. The default value of |
numBasis |
Vector of positive integers specifying the number of spline basis functions to be used for each smooth function component. |
preTransfData |
Boolean flag: |
msgCode |
A code for specification of the nature of messages printed concerning progress of the Markov chain Monte Carlo sampling: |
Tung Pham tungstats@gmail.com and Matt Wand matt.wand@uts.edu.au.
Pham, T. and Wand, M.P. (2018). Generalized additive mixed model analysis via gammSlice
. Australian and New Zealand Journal of Statistics, 60, 279-300.
Zhao, Y., Staudenmayer, J., Coull, B.A. and Wand, M.P. (2003). General design Bayesian generalized linear mixed models. Statistical Science, 21, 35-51.
gSlc
## Not run: library(gammSlice) set.seed(39402) ; m <- 100 ; n <- 2 beta0True <- 0.5 ; betaxTrue <- 1.7 ; sigsqTrue <- 0.8 idnum <- rep(1:m,each=n) ; x <- runif(m*n) U <- rep(rnorm(m,0,sqrt(sigsqTrue)),each=n) mu <- 1/(1+exp(-(beta0True+betaxTrue*x+U))) y <- rbinom((m*n),1,mu) fit <- gSlc(y ~ x,random = list(idnum = ~1),family = "binomial") summary(fit) # Illustration of user-specified priors: fitMyPriors <- gSlc(y ~ x,random = list(idnum = ~1), family = "binomial", control = gSlc.control(fixedEffPriorVar=1e13, sdPriorScale=1e3)) summary(fitMyPriors) # Illustration of specification of larger Markov chain Monte Carlo samples: fitBigMCMC <- gSlc(y ~ x,random = list(idnum = ~1),family = "binomial", control = gSlc.control(nBurn=10000,nKept=8000,nThin=10)) summary(fitBigMCMC) ## End(Not run)