DPpackage (version 1.1-0)

FPTrasch: Bayesian analysis for a Finite Polya Tree Rasch model

Description

This function generates a posterior density sample for a Rasch model, using a Finite Polya Tree or a Mixture of Finite Polya Trees prior for the distribution of the abilities.

Usage

FPTrasch(y,prior,mcmc,offset,state,status,grid=seq(-10,10,length=1000),
         data=sys.frame(sys.parent()),compute.band=FALSE)

Arguments

y

a matrix giving the data for which the Rasch Model is to be fitted.

prior

a list giving the prior information. The list includes the following parameter: a0 and b0 giving the hyperparameters for prior distribution of the precision parameter of the Finite Poly

mcmc

a list giving the MCMC parameters. The list must include the following integers: nburn giving the number of burn-in scans, nskip giving the thinning interval, nsave giving

offset

this can be used to specify an a priori known component to the linear predictor.

state

a list giving the current value of the parameters. This list is used if the current analysis is the continuation of a previous analysis.

status

a logical variable indicating whether this run is new (TRUE) or the continuation of a previous analysis (FALSE). In the latter case the current value of the parameters must be specifie

grid

grid points where the density estimate is evaluated. The default is seq(-10,10,length=1000).

data

data frame.

compute.band

logical variable indicating whether the confidence band for the density and CDF must be computed.

Value

• An object of class FPTrasch representing the Rasch model fit. Generic functions such as print, plot, and summary have methods to show the results of the fit. The results include beta, mu, sigma2, and the precision parameter alpha. The function DPrandom can be used to extract the posterior mean of the random effects. The list state in the output object contains the current value of the parameters necessary to restart the analysis. If you want to specify different starting values to run multiple chains set status=TRUE and create the list state based on this starting values. In this case the list state must include the following objects:
• alpha
  giving the value of the precision parameter.
• b
  a vector of dimension nsubjects giving the value of the random effects for each subject.
• beta
  giving the value of the difficulty parameters.
• mu
  giving the mean of the normal baseline distributions.
• sigma2
  giving the variance of the normal baseline distributions.

Details

This generic function fits a Finite Polya Tree Rasch model as in San Martin et al. (2006), where the linear predictor is modeled as follows: $$\eta_{ij} = \theta_i - \beta_j, i=1,\ldots,n, j=1,\ldots,k$$ $$\theta_i | G \sim G$$ $$G | \alpha,\mu,\sigma^2 \sim FPT^M(\Pi^{\mu,\sigma^2},\textit{A})$$ where, the the PT is centered around a $N(\mu,\sigma^2)$ distribution, by taking each $m$ level of the partition $\Pi^{\mu,\sigma^2}$ to coincide with the $k/2^m, k=0,\ldots,2^m$ quantile of the $N(\mu,\sigma^2)$ distribution. The family $\textit{A}={\alpha_e: e \in E^{*}}$, where $E^{*}=\bigcup_{m=0}^{\infty} E^m$ and $E^m$ is the $m$-fold product of $E={0,1}$, was specified as $\alpha_{e_1 \ldots e_m}=\alpha m^2$. To complete the model specification, independent hyperpriors are assumed, $$\alpha | a_0, b_0 \sim Gamma(a_0,b_0)$$ $$\beta | \beta_0, S_{\beta_0} \sim N(\beta_0,S_{\beta_0})$$ $$\mu | \mu_b, S_b \sim N(\mu_b,S_b)$$ $$\sigma^{-2} | \tau_1, \tau_2 \sim Gamma(\tau_1/2,\tau_2/2)$$ Each of the parameters of the baseline distribution, $\mu$ and $\sigma$ can be considered as random or fixed at some particular value. In the first case, a Mixture of Polya Trees Process is considered as a prior for the distribution of the random effects. To let $\sigma$ to be fixed at a particular value, set $\tau_1$ to NULL in the prior specification. To let $\mu$ to be fixed at a particular value, set $\mu_b$ to NULL in the prior specification. In the computational implementation of the model, a Metropolis-Hastings step is used to sample the full conditional of the difficulty parameters. The full conditionals for abilities and PT parameters are sampled using slice sampling. We refer to Jara, Hanson and Lesaffre (2009) for more details and for the description regarding sampling functionals of PTs.

References

Hanson, T., and Johnson, W. (2002) Modeling regression error with a Mixture of Polya Trees. Journal of the American Statistical Association, 97: 1020 - 1033.

Jara, A., Hanson, T., Lesaffre, E. (2009) Robustifying Generalized Linear Mixed Models using a New Mixture of Multivariate Polya Trees. Journal of Computational and Graphical Statistics (To appear). Lavine, M. (1992) Some aspects of Polya tree distributions for statistical modelling. The Annals of Statistics, 20: 1222-11235. Lavine, M. (1994) More aspects of Polya tree distributions for statistical modelling. The Annals of Statistics, 22: 1161-1176. San Martin, E., Jara, A., Rolin, J.-M., and Mouchart, M. (2006) On the Analysis of Bayesian Semiparametric IRT-type Models. In preparation.

See Also

DPrandom, DPrasch

Examples

####################################
    # A simulated Data Set
    ####################################
      nsubject <- 200
      nitem <- 40
      
      y <- matrix(0,nrow=nsubject,ncol=nitem)
      dimnames(y) <- list(paste("id",seq(1:nsubject)), 
                          paste("item",seq(1,nitem)))

      
      ind <- rbinom(nsubject,1,0.5)
      theta <- ind*rnorm(nsubject,1,0.25)+(1-ind)*rnorm(nsubject,3,0.25)
      beta <- c(0,seq(-1,3,length=nitem-1))
      true.density <- function(grid)
      {
         0.5*dnorm(grid,1,0.25)+0.5*dnorm(grid,3,0.25) 
      }  

      for(i in 1:nsubject)
      {
         for(j in 1:nitem)
         {
            eta<-theta[i]-beta[j]         
            mean<-exp(eta)/(1+exp(eta))
            y[i,j]<-rbinom(1,1,mean)
         }
      }

    # Prior information

      beta0 <- rep(0,nitem-1)
      Sbeta0 <- diag(1000,nitem-1)

      prior <- list(alpha=1,
                    tau1=6.01,
                    tau2=2.01,
                    mub=0,
                    Sb=100,
                    beta0=beta0,
                    Sbeta0=Sbeta0,
                    M=5)

    # Initial state
      state <- NULL      

    # MCMC parameters

      nburn <- 5000
      nsave <- 5000
      nskip <- 0
      ndisplay <- 100
      mcmc <- list(nburn=nburn,
                   nsave=nsave,
                   nskip=nskip,
                   ndisplay=ndisplay)

    # Fit the model
      fit1 <- FPTrasch(y=y,prior=prior,mcmc=mcmc,
                     state=state,status=TRUE,grid=seq(-1,5,0.01),compute.band=TRUE)

    # Density estimate (along with HPD band) and truth
plot(fit1$grid,fit1$dens.u,lwd=2,col="blue",type="l",lty=2,xlab=expression(theta),ylab="density")
      lines(fit1$grid,fit1$dens,lwd=2,col="blue")
      lines(fit1$grid,fit1$dens.l,lwd=2,col="blue",lty=2)
      lines(fit1$grid,true.density(fit1$grid),col="red")

    # Summary with HPD and Credibility intervals
      summary(fit1)
      summary(fit1,hpd=FALSE)

    # Plot model parameters 
    # (to see the plots gradually set ask=TRUE)
      plot(fit1,ask=FALSE)
      plot(fit1,ask=FALSE,nfigr=2,nfigc=2)	

    # Extract random effects
    
      DPrandom(fit1)
      plot(DPrandom(fit1))
      DPcaterpillar(DPrandom(fit1))