Hi R community,
I have some data set and construct the likelihood as follows
likelihood <- function(alpha,beta){
    lh<-1
    d<-0
    p<-0
    k<-NULL
    data<-read.table("epidemic.txt",header = TRUE)
    attach(data, warn.conflicts = F)
    k <-which(inftime==1)
    d <- (sqrt((x-x[k])^2+(y-y[k])^2))^(-beta)
    p<-1 - exp(-alpha*d)
    for(i in 1:100){
        if(i!=k){
            if(inftime[i]==0){
                lh<-lh*(1-p[i])
            }
            if(inftime[i]==2){
                lh<-lh*p[i]
            }
        }
    }
    return(lh)
}
 Then, I want to simulate this by using random walk Metropolis- Hasting
algorithm with the single parameter update. i have the following R function
mh.epidemic <-function(m,alpha, beta){
      z<- array(0,c(m+1, 2))
    z[1,1] <- alpha
    z[1,2] <- beta
    for(t in 2:m){
        u <- runif(1)
        y1 <- rexp(1,z[t-1,1])
        y2 <-rexp(1,z[t-1,2])
        z[t,1] <- likelihood(y1,z[t-1,2])/likelihood(z[t-1,1],z[t-1,2])
        a1 <-min(1,z)
        if(u<=a1) z[t,] <- y1 else
        z[t,2] <-z[t-1,1]

        z[t,2]<-likelihood(z[t,1], y2)/likelihood(z[t,1],z[t-1,2])
        accept <-min(1,z)
        if(u<=accept) z[t,] <- y2 else
        z[t,2] <-z[t-1,1]
    }
    z
}
mh.epidemic(100, 1,2)
when I run it shows the following error:
Error in if (u <= accept) z[t, ] <- y2 else z[t, 2] <- z[t - 1, 1] :
  missing value where TRUE/FALSE needed
I know it is due to the NaN produce some where. So I tried by running the
code separately, as follows
m<- 100
>     alpha <-1
>     beta <- 2
>     z<- array(0,c(m+1, 2))
>     z[1,1] <- alpha
>     z[1,2] <- beta
>     for(t in 2:m){
+         u <- runif(1)
+         y1 <- rexp(1,z[t-1,1])
+         y2 <-rexp(1,z[t-1,2])
+         z[t,1] <- likelihood(y1,z[t-1,2])/likelihood(z[t-1,1],z[t-1,2])
+         a1 <-min(1,z)
+ }
There were 50 or more warnings (use warnings() to see the first 50)
> y1
[1] NaN
> y2
[1] NaN
why, this simulation from exponential function is produce NaN?
If some one help me it will be great.

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