R coding: Function to write Approximate Bayesian Computation with Population Monte Carlo method -

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i trying write function can calculate approximate bayesian computation using population monte carlo method. however, ran troubles r code following error.

head(abc_pmc(2,5000,1,observeddata,observedsummary))

error in while (prior == 0) { : missing value true/false needed in addition: warning message: in log(importance.sample[2]) : nans produced

the code doesn't have problem when run 1 iteration, t=1. issue lies second iteration , think assignment of importance.sammpling step. can't figure out error tried run code manually , works.

any , appreciated.

thanks!

initialise variables:

set.seed(12345) n=5000 weight = rep(1,n) theta = matrix(na, nrow = n, ncol = 2) (i in 1:n) {  fit = c(runif(1,-2,2), runif(1,0,4)) theta[i,] = fit }   #calculate importance density current t use in next iteration     old.prop.weight = weight / sum(weight)     old.theta = theta     kernel.mean = cbind(old.theta[,1],old.theta[,2])     kernel.cov = cov(old.theta)

main loop:

  (i in 1:n) {         prior=0         while (prior==0) {           #loop test prior=0              choice = sample(1:n, size = 1, prob = old.prop.weight)             importance.sample = rmvnorm(1,mean = kernel.mean[choice,], sigma = kernel.cov)             #logsigma ensure theta2 nonnegative             if (importance.sample[2] != 0)               importance.sample[2] = log(importance.sample[2])             fit = c(importance.sample[1],importance.sample[2])           }            prior = as.numeric(dunif(fit[1],min = -2,max = 2) * dunif(exp(fit[2]),min = 0,max = 4))         }          theta[i,] = fit       ##end of step 2 loop     }

edit: tried make code reproducible example


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