PTrandom {DPpackage} | R Documentation |
Extracts random effects from PTglmm objects: PTlmm, PTolmm, and PTglmm.
PTrandom(object,centered=FALSE,predictive=FALSE,ngrid=1000,gridl=NULL)
object |
PT fitted model object from which random effects estimates can be extracted. |
centered |
logical variable indicating whether the random effects
should be extracted centered, bi , or
uncentered thetai . This option cannot be only used
to get the density estimates. |
predictive |
logical variable indicating whether actual or predictive information of the random effects should be extracted. |
ngrid |
number of grid points where the density estimate is evaluated. This is only used if dimension of the random effects is lower or equal than 2 and if predictive=TRUE. The default value is 1000. |
gridl |
The limits of the interval or rectangle covered by the grid as c(xl,xu) or c(xl, xu, yl, yu), respectively. If not specified the grid is defined automatically. This is only used if dimension of the random effects is lower or equal than 2 and if predictive=TRUE. |
Alejandro Jara <ajarav@udec.cl>
Tim Hanson <hanson@biostat.umn.edu>
## Not run: # School Girls Data Example data(schoolgirls) attach(schoolgirls) # Prior information prior<-list(alpha=1, M=4, tau1=0.01,tau2=0.01, nu0=4.01, tinv=diag(10,2), mub=rep(0,2), Sb=diag(1000,2)) # Initial state state <- NULL # MCMC parameters nburn<-5000 nsave<-5000 nskip<-0 ndisplay<-100 mcmc <- list(nburn=nburn,nsave=nsave,nskip=nskip,ndisplay=ndisplay, tune1=1.5,tune2=1.1) # Fitting the model fit1<-PTlmm(fixed=height~1,random=~age|child,prior=prior,mcmc=mcmc, state=state,status=TRUE) fit1 # Extract random effects PTrandom(fit1) plot(PTrandom(fit1)) # Extract predictive information of random effects PTrandom(fit1,predictive=TRUE) plot(PTrandom(fit1,predictive=TRUE,gridl=c(75,89,3.8,7.5))) ## End(Not run)