Problem with loo function for model comparison

Hello there, I am going to compare different models specifically,

i) one model estimated using brms,

fit_brm <- brm(y ~ x,...)

ii) and an additional one using classical stan function i.e.

rstan_model=stan("model.stan",model_code=stan_mod, data=data...)

obtaining the stan script leveraging the brms function make_stancode() and modifying it.

When I am trying to compare models using loo function, it works for brms object (fit_brm). Nevertheless, I got this message when I use the function on rstan_model.

Error in check_pars(allpars, pars) : no parameter log_lik

Any cue or suggestion?

See Writing Stan programs for use with the loo package • loo

Thanks for your reply, unfortunately I get the same error

> log_lik_1 <- extract_log_lik(rstan_model, merge_chains = FALSE)
Error in check_pars(allpars, pars) : no parameter log_lik

Did you follow the instructions to add that log_lik computation in generated quantities block?

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Thanks for your support. Nevertheless, I am working in a Dirichlet framework with no intercept, so it is quite difficult for me. Here is the Stan code and a data simulation.

Hope you can help me, I would really benefit from that

Stan code:

// generated with brms 2.18.0
functions {
  /* dirichlet-logit log-PDF
   * Args:
   *   y: vector of real response values
   *   mu: vector of category logit probabilities
   *   phi: precision parameter
   * Returns:
   *   a scalar to be added to the log posterior
   real dirichlet_logit_lpdf(vector y, vector mu, real phi) {
     return dirichlet_lpdf(y | softmax(mu) * phi);
data {
  int<lower=1> N;  // total number of observations
  int<lower=2> ncat;  // number of categories
  vector[ncat] Y[N];  // response array
  int<lower=1> K_muy2;  // number of population-level effects
  matrix[N, K_muy2] X_muy2;  // population-level design matrix
  int<lower=1> K_muy3;  // number of population-level effects
  matrix[N, K_muy3] X_muy3;  // population-level design matrix
  int prior_only;  // should the likelihood be ignored?
transformed data {
parameters {
  vector[K_muy2] b_muy2;  // population-level effects
  vector[K_muy3] b_muy3;  // population-level effects
  real<lower=0> phi;  // precision parameter
transformed parameters {
  real lprior = 0;  // prior contributions to the log posterior
  lprior += gamma_lpdf(phi | 0.01, 0.01);
model {
  // likelihood including constants
  if (!prior_only) {
    // initialize linear predictor term
    vector[N] muy2 = rep_vector(0.0, N);
    // initialize linear predictor term
    vector[N] muy3 = rep_vector(0.0, N);
    // linear predictor matrix
    vector[ncat] mu[N];
    muy2 += X_muy2 * b_muy2;
    muy3 += X_muy3 * b_muy3;
    for (n in 1:N) {
      mu[n] = transpose([0, muy2[n], muy3[n]]);
    for (n in 1:N) {
      target += dirichlet_logit_lpdf(Y[n] | mu[n], phi);
  // priors including constants
  target += lprior;
generated quantities {

Data simulationsing brms:


bind <- function(...) cbind(...)

#Data simulation
N <- length(2000:2009)*length(1:10)
df <- data.frame(
  y1 = rbinom(N, 10, 0.5), y2 = rbinom(N, 10, 0.7), 
  y3 = rbinom(N, 10, 0.9), Age = as.factor(1:10),Year = as.factor(rep(2000:2009,each=length(1:10)))
) %>%
    size = y1 + y2 + y3,
    y1 = y1 / size,
    y2 = y2 / size,
    y3 = y3 / size
df$y <- with(df, cbind(y1, y2, y3))

stan_mod <- make_stancode(bind(y1, y2, y3) ~ 0+Age+Year, df, dirichlet(),save_model = "example_model.stan")
brms_standata <- make_standata(bind(y1, y2, y3) ~ 0+Age+Year, df, dirichlet(),save_model = "data_exmple.stan")

# design matrix 

stan_mod_a <- stan_model(file="example_model.stan")
rstan_model = stan("data_exmple.stan",model_code=stan_mod_a,

This line in the model block computes the log likelihood

You just copy that and collect the outputs. Although, you need to repeat also the computation for mu unless you move that to the transformed parameters. So it should look something like this

generated quantities {
    vector[N] muy2;
    vector[N] muy3;
    vector[ncat] mu[N];
    vector[N] log_lik;
    muy2 = X_muy2 * b_muy2;
    muy3 = X_muy3 * b_muy3;
    for (n in 1:N) {
      mu[n] = transpose([0, muy2[n], muy3[n]]);
      lok_lik[n] = dirichlet_logit_lpdf(Y[n] | mu[n], phi);

Sorry, I didn’t have time to test that it runs, but I think it should.

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