DPpackage (version 1.1-6)

PTrandom: Extracts Random Effects

Description

Extracts random effects from PTglmm objects: PTlmm, PTolmm, and PTglmm.

Usage

PTrandom(object,centered=FALSE,predictive=FALSE,ngrid=1000,gridl=NULL)

Arguments

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.

Examples

## 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)