DPpackage (version 1.1-6)

predict.HDPMdensity: Predictive Information for the Dependent Random Probability Measures.

Description

Plot the probability measures arising from a HDPM of normals model. Support provided by the NIH/NCI R01CA75981 grant.

Usage

"predict"(object,i,r,ask=TRUE,nfigr=2,nfigc=2, ...)

Arguments

object

HDPMdensity fitted model object.

i

study indicator.

r

indicator for including (0) or not (1) the common measure.

ask

logical variable indicating whether the plots must be displayed sequentially or not.

nfigr

number of rows in the figure.

nfigc

number of columns in the figure.

...

further arguments to be passed.

Details

Must run HDPMdensity first to generate posterior simulations.

References

Mueller, P., Quintana, F. and Rosner, G. (2004). A Method for Combining Inference over Related Nonparametric Bayesian Models. Journal of the Royal Statistical Society, Series B, 66: 735-749.

See Also

HDPMdensity

Examples

## Not run: 
# 
#     # Data
#       data(calgb)
#       attach(calgb)
#       y <- cbind(Z1,Z2,Z3,T1,T2,B0,B1)
# 
#     # Prior information
#       prior <- list(pe1=0.1,
#                     pe0=0.1,
#                     ae=1,
#                     be=1,
#                     a0=rep(1,3),
#                     b0=rep(1,3),
#                     nu=9,
#                     tinv=0.25*var(y),
#  		    m0=apply(y,2,mean),
#                     S0=var(y),
#  		    nub=9,
#                     tbinv=var(y))		
# 
# 
#     # Initial state
#       state <- NULL
# 
#     # MCMC parameters
# 
#       mcmc <- list(nburn=5000,
#                    nsave=5000,
#                    nskip=3,
#                    ndisplay=100)
# 
#     # Fitting the model
#       fit1 <- HDPMdensity(y=y,
#                           study=study,
#                           prior=prior,
#                           mcmc=mcmc,
#                           state=state,
#                           status=TRUE)
# 
#     # Posterior inference
#       fit1
#       summary(fi1)
#        
#     # Plot the parameters
#     # (to see the plots gradually set ask=TRUE)
#       plot(fit1,ask=FALSE)
# 
#     # Plot the a specific parameters 
#     # (to see the plots gradually set ask=TRUE)
#       plot(fit1,ask=FALSE,param="eps",nfigr=1,nfigc=2)
# 
#    # Plot the measure for each study
#       predict(fit1,i=1,r=1) # study 1
#       predict(fit1,i=2,r=1) # study 2
# 
#     # Plot the idiosyncratic measure for each study
#       predict(fit1,i=1,r=0) # study 1
#       predict(fit1,i=2,r=0) # study 2
# 
#     # Plot the common measure
#       predict(fit1,i=0)
# ## End(Not run)