nodal               package:DPpackage               R Documentation

_N_o_d_a_l _I_n_v_o_l_v_e_m_e_n_t _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     data(nodal)

_F_o_r_m_a_t:

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

     '_i_d' an ordered factor giving a unique identifier for the subject
          in the study

     '_s_s_l_n' 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

     '_a_g_e' a numeric vector giving the age of the patient in years at
          diagnosis

     '_a_c_i_d' a numeric vector giving the level of serum acid phosphate

     '_x_r_a_y' a numeric vector giving the result af an X-ray examination,
          coded 0 if negative and 1 if positive

     '_s_i_z_e' a numeric vector giving the size of the tumor, coded  0 if
          small and 1 if large

     '_g_r_a_d_e' a numeric vector giving the pathological grade of the
          tumor, coded  0 if less serious and 1 if more serious

_S_o_u_r_c_e:

     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.

_R_e_f_e_r_e_n_c_e_s:

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

_E_x_a_m_p_l_e_s:

     ## 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)

