R: Predictive Information for the Dependent Random Probability...

predict.HDPMcdensity {DPpackage} | R Documentation

## Predictive Information for the Dependent Random Probability Measures.

### Description

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

### Usage

    ## S3 method for class 'HDPMcdensity'
    predict(object,pred,i,r,ask=TRUE,nfigr=2,nfigc=2, ...)

### Arguments

`object` | `HDPMcdensity` fitted model object.
`pred` | indicator for the values of the predictors, given by the row pred in xpred, for which the conditional densities must be drawn.
`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 `HDPMcdensity` first to generate posterior simulations.

### Author(s)

Alejandro Jara <atjara@uc.cl>

Peter Mueller <pmueller@mdanderson.org>

### 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

`HDPMcdensity`

### Examples

    ## Not run:
        # Data
          data(calgb)
          attach(calgb)
          y <- cbind(Z1,Z2,Z3,T1,T2,B0,B1)
          x <- cbind(CTX,GM,AMOF)

          z <- cbind(y,x)

        #  Data for prediction
          data(calgb.pred)
          xpred <- as.matrix(calgb.pred[,8:10])

        # Prior information
          prior <- list(pe1=0.1,
                        pe0=0.1,
                        ae=1,
                        be=1,
                        a0=rep(1,3),
                        b0=rep(1,3),
                        nu=12,
                        tinv=0.25*var(z),
     		  m0=apply(z,2,mean),
                        S0=var(z),
     		  nub=12,
                        tbinv=var(z))

        # Initial state
          state <- NULL

        # MCMC parameters

          mcmc <- list(nburn=5000,
                       nsave=5000,
                       nskip=3,
                       ndisplay=100)

        # Fitting the model
          fit1 <- HDPMcdensity(formula=y~x,
                              study=~study,
                              xpred=xpred,
                              prior=prior,
                              mcmc=mcmc,
                              state=state,
                              status=TRUE)

        # Posterior inference
          fit1
          summary(fit1)

        # 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
        # under first values for the predictors, xpred[1,]
          predict(fit1,pred=1,i=1,r=1) # pred1, study 1
          predict(fit1,pred=1,i=2,r=1) # pred1, study 2

        # Plot the measure for each study
        # under second values for the predictors, xpred[2,]
          predict(fit1,pred=2,i=1,r=1) # pred2, study 1
          predict(fit1,pred=2,i=2,r=1) # pred2, study 2

        # Plot the idiosyncratic measure for each study
        # under first values for the predictors, xpred[1,]
          predict(fit1,pred=1,i=1,r=0) # study 1
          predict(fit1,pred=1,i=2,r=0) # study 2

        # Plot the common measure
        # under first values for the predictors, xpred[1,]
          predict(fit1,pred=1,i=0)

    ## End(Not run)

[Package DPpackage version 1.1-7.4 Index]