R: Nodal Involvement Data

nodal {DPpackage} | R Documentation

## Nodal Involvement Data

### Description

This data set consider information on the presence of prostatic nodal involvement collected on 53 patients with prostate cancer reported by Brown (1980).

For the sample of prostate cancer patients, a number of possible predictor variables were measured before surgery. The patients then had surgery to determine nodal involvement. It was required to see if nodal involvement could be accurately predicted from the predictor variables and which ones were most important.

### Usage

    data(nodal)

### Format

A data frame with 53 observations on the following 7 variables.

`id`

  an ordered factor giving a unique identifier for the subject in the study

`ssln`

  a numeric vector giving the prostatic nodal involvement that takes the value 1 if cancer had spread to the surrounding lymph nodes and 0 otherwise

`age`

  a numeric vector giving the age of the patient in years at diagnosis

`acid`

  a numeric vector giving the level of serum acid phosphate

`xray`

  a numeric vector giving the result af an X-ray examination, coded 0 if negative and 1 if positive

`size`

  a numeric vector giving the size of the tumor, coded 0 if small and 1 if large

`grade`

  a numeric vector giving the pathological grade of the tumor, coded 0 if less serious and 1 if more serious

### Source

Brown, B.W. (1980) Prediction analysis for binary data. In Biostatistics Casebook. R.G. Miller, B. Efron, B.W. Brown and L.E. Moses (editors), 3-18. John Wiley.

### References

Chib, S. (1995) Marginal Likelihood from the Gibbs output. Journal of the American Statistical Association, 90: 1313 - 1321.

### Examples

    ## Not run:
        # Data
          data(nodal)
          attach(nodal)
          lacid<-log(acid)

        # Initial state
          state <- NULL

        # MCMC parameters
          nburn<-5000
          nsave<-10000
          nskip<-10
          ndisplay<-100
          mcmc <- list(nburn=nburn,nsave=nsave,nskip=nskip,ndisplay=ndisplay,
                       tune=1.1)

        # Prior distribution
          prior <- list(alpha=1,beta0=c(0,rep(0.75,5)),
                        Sbeta0=diag(c(100,rep(25,5)),6))

        # Fit the model
          fit1 <- DPbinary(ssln~age+lacid+xray+size+grade,prior=prior,mcmc=mcmc,
                           state=state,status=TRUE)
          fit1

    ## End(Not run)

[Package DPpackage version 1.1-7.4 Index]