It's fine for compiled Laplace object, so this is simply a minor cleanup issue.
model_code <- nimbleCode({
# priors
intercept ~ dnorm(0, sd = 100)
beta ~ dnorm(0, sd = 100)
sigma ~ dhalfflat()
# random effects and data
for(i in 1:10) {
# random effects
ran_eff[i] ~ dnorm(0, sd = sigma)
for(j in 1:5) {
# data
y[i,j] ~ dpois(exp(intercept + beta*X[i,j] + ran_eff[i]))
}
}
})
set.seed(123)
X <- matrix(rnorm(50), nrow = 10)
model <- nimbleModel(model_code, constants = list(X = X), calculate = FALSE,
buildDerivs = TRUE) # Here is the argument needed for AD.
model$intercept <- 0
model$beta <- 0.2
model$sigma <- 0.5
model$calculate()
model$simulate(model$getDependencies('ran_eff'))
model$calculate()
model$setData('y')
glmm_laplace <- buildLaplace(model, c('intercept','beta','sigma'))
runLaplace(glmm_laplace, c(0,0,1))
# [Warning] Running an uncompiled Laplace or AGHQ algorithm. Use compileNimble() for faster execution.
#Error in optimize(pStart = pStart, method = method, hessian = hessian, :
# object 'gr_logLik_pTransformed' not found
It's fine for compiled Laplace object, so this is simply a minor cleanup issue.