Dear R users:

I was trying to fit a HMM with mixture of Gaussian into the dataset, and I
tried to implement it by R2Winbugs. But I got the following errer.
*
Error in FUN(X[[1L]], ...) :
  .C(..): 'type' must be "real" for this format*

Does anybody know what's the problem? Does R2Winbugs accept some matrix as
inits? I would really appreciate your help. Thank you very much.

The attached are codes of R and Winbugs.
-------------------------------------------------------------------------------------------------------------------------
library(R2WinBUGS)
library(MCMCpack)
library(coda)

## the input of the dataset

X=read.csv("X.csv",header=FALSE)
X=as.matrix(X)    # transform the data into matrix

## parameter setting

N=nrow(X)        # # of servers
T=ncol(X)        # Time
m=sum(X)/(N*T)    # mean of the training set

M=matrix(m,nrow=N,ncol=T)
s=sum((X-M)^2)/(N*T)    # std of the training set

K=3                # # of clusters
alpha=0.5            # parameter for Dirichlet distn
sigmae=0.5            # var of cluster mean mu
q1=rep(1/K,K)        # prior for Z(n,1)

## MCMC sampling

data=list("X","m","s","N","T","K","alpha","sigmae","q1")
inits=function(){list(a0=rbeta(1,1,1),
qx=matrix(rgamma(K^2,alpha,1),nrow=K), sigma0.r=rbeta(K,1,1))}
model.sim=bugs(data,inits,model.file="model.txt",parameters=c("mu","sigma"),
n.chains=3,n.iter=3500,n.burnin=500,n.thin=1,bugs.directory="C:/Users/t-wec/Desktop/WinBUGS14",codaPkg=T,debug=T)
mcmcout=read.bugs(model.sim)
summary(mcmcout)
------------------------------------------------------------------------------------------------------------------------
model
{
    # cluster parameters mu and tau

    tau1 <- (1-a*a)*taue
    taue <- 1/sigmae

    for (k in 1:K)
    {
        # cluster mean mu

        mu[k,1] ~ dnorm(m,tau1)

        for (t in 2:T)
        {
            mu[k,t] ~ dnorm(meanmu[k,t],taue)
            meanmu[k,t] <- m*(1-a)+a*mu[k,t-1]
        }

        # cluster varicance tau

        sigma0.r[k] ~ dbeta(1,1)
        sigma.r[k] <- s*sigma0.r[k]
        sigma[k] <- sigma.r[k]*sigma.r[k]
        tau[k] <- 1/sigma[k]
    }

    # cluster indicator Z and observation X

    for (n in 1:N)
    {
        Z[n,1] ~ dcat(q1[1:K])
        X[n,1] ~ dnorm(mu[Z[n,1],1],tau[Z[n,1]])

        for (t in 2:T)
        {
            Z[n,t] ~ dcat(q[Z[n,t-1],1:K])
            X[n,t] ~ dnorm(mu[Z[n,t],t],tau[Z[n,t]])
        }
    }

    # prior on transition matrix Q
    # each row of Q has a Dirichlet prior realized by Gamma

    for (k in 1:K)
    {
        for (l in 1:K)
        {
            q[k,l] <- qx[k,l]/sum(qx[k,1:K])
            qx[k,l] ~ dgamma(alpha,1)
        }
    }

    # prior on regression coefficient: uniform on [-1,1]

    a0 ~ dbeta(1,1)
    a <- a0*2-1
}


Wei Chen

	[[alternative HTML version deleted]]

