DPpackage (version 1.1-6)

nodal: 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)