CODEWITHSUNDEEP
rregressionjags
CRP
CRP

Reputation: 435

Predicting new values in jags (mixed model)

I asked a similar question a while ago on how to get model predictions in JAGS for mixed models. Here's my original question.

This time, I'm trying to get predictions for the same model but using new data and not the original that was used to fit the model.

model<-"model {
# Priors
mu_int~dnorm(0, 0.0001)
sigma_int~dunif(0, 100)
tau_int <- 1/(sigma_int*sigma_int)
for (j in 1:(M)){ 
alpha[j] ~ dnorm(mu_int, tau_int)
}
beta~dnorm(0, 0.01)
sigma_res~dunif(0, 100)
tau_res <- 1/(sigma_res*sigma_res)
# Likelihood
for (i in 1:n) {
mu[i] <- alpha[Mat[i]]+beta*Temp[i] # Expectation
D47[i]~dnorm(mu[i], tau_res) # The actual (random) responses
}
for(i in 1:(n)){
D47_pred[i] <- dnorm(mu[i], tau_res)
}
}"

I know this mcan be done using the posterior distributions of the resulting parameters but I'm wondering if it could also be implemented inside jags.

Thank you!

Upvotes: 0

Views: 262

Answers (1)

DaveArmstrong
DaveArmstrong

Reputation: 21992

It absolutely could be done inside JAGS. If you wanted predictions for new values of Temp for some of the same observations in Mat, you would just have to append them to the existing data with a corresponding D47 value of NA.

Upvotes: 1

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