# Define model effects | |
sigmaML <- cauchy(0, 1, truncation = c(0, Inf), dim = 3) | |
a_fem <- normal(0, sigmaML[1], dim = max(female_id)) | |
a_year <- normal(0, sigmaML[2], dim = max(year)) | |
a_group <- normal(0, sigmaML[3], dim = max(group_id)) | |
a <- normal(0, 5) | |
bA <- normal(0, 3) | |
bEL <- normal(0, 3) | |
bES <- normal(0, 3) | |
bGS <- normal(0, 3) | |
bP <- normal(0, 3) | |
bPA <- normal(0, 3) | |
# Model setup | |
mu <- a + a_fem[female_id] + a_year[year] + a_group[group_id] + | |
Age*bA + Eggs_laid*bEL + Mean_eggsize*bES + Parasite*bP + | |
Group_size*bGS + Parasite*Age*bPA | |
p <- ilogit(mu) | |
distribution(fro$Successful) <- bernoulli(p) | |
cuckooModel <- model(a, bA, bEL, bES, bP, bGS, bPA) | |
# Plot | |
plot(cuckooModel) | |
# HMC sampling | |
draws <- mcmc(cuckooModel, n_samples = 4000, | |
warmup = 1000, chains = 4, n_cores = 10) | |
# Trace plots | |
mcmc_trace(draws) | |
# Parameter posterior | |
mcmc_intervals(draws, prob = .95) |
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