stancode is a generic function that can be used to
generate Stan code for Bayesian models. Its original use is
within the brms package, but new methods for use
with objects from other packages can be registered to the same generic.
stancode(object, ...)
make_stancode(formula, ...)An object whose class will determine which method to apply. Usually, it will be some kind of symbolic description of the model form which Stan code should be generated.
Further arguments passed to the specific method.
Synonym of object for use in make_stancode.
Usually, a character string containing the generated Stan code. For pretty printing, we recommend the returned object to be of class
c("character", "brmsmodel").
See stancode.default for the default
method applied for brms models.
You can view the available methods by typing: methods(stancode)
The make_stancode function is an alias of stancode.
stancode(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = "cumulative")
#> // generated with brms 2.21.0
#> functions {
#> /* cumulative-logit log-PDF for a single response
#> * Args:
#> * y: response category
#> * mu: latent mean parameter
#> * disc: discrimination parameter
#> * thres: ordinal thresholds
#> * Returns:
#> * a scalar to be added to the log posterior
#> */
#> real cumulative_logit_lpmf(int y, real mu, real disc, vector thres) {
#> int nthres = num_elements(thres);
#> if (y == 1) {
#> return log_inv_logit(disc * (thres[1] - mu));
#> } else if (y == nthres + 1) {
#> return log1m_inv_logit(disc * (thres[nthres] - mu));
#> } else {
#> return log_inv_logit_diff(disc * (thres[y] - mu), disc * (thres[y - 1] - mu));
#> }
#> }
#> /* cumulative-logit log-PDF for a single response and merged thresholds
#> * Args:
#> * y: response category
#> * mu: latent mean parameter
#> * disc: discrimination parameter
#> * thres: vector of merged ordinal thresholds
#> * j: start and end index for the applid threshold within 'thres'
#> * Returns:
#> * a scalar to be added to the log posterior
#> */
#> real cumulative_logit_merged_lpmf(int y, real mu, real disc, vector thres, array[] int j) {
#> return cumulative_logit_lpmf(y | mu, disc, thres[j[1]:j[2]]);
#> }
#> /* ordered-logistic log-PDF for a single response and merged thresholds
#> * Args:
#> * y: response category
#> * mu: latent mean parameter
#> * thres: vector of merged ordinal thresholds
#> * j: start and end index for the applid threshold within 'thres'
#> * Returns:
#> * a scalar to be added to the log posterior
#> */
#> real ordered_logistic_merged_lpmf(int y, real mu, vector thres, array[] int j) {
#> return ordered_logistic_lpmf(y | mu, thres[j[1]:j[2]]);
#> }
#> }
#> data {
#> int<lower=1> N; // total number of observations
#> array[N] int Y; // response variable
#> int<lower=2> nthres; // number of thresholds
#> int<lower=1> K; // number of population-level effects
#> matrix[N, K] X; // population-level design matrix
#> int<lower=1> Kc; // number of population-level effects after centering
#> // data for group-level effects of ID 1
#> int<lower=1> N_1; // number of grouping levels
#> int<lower=1> M_1; // number of coefficients per level
#> array[N] int<lower=1> J_1; // grouping indicator per observation
#> // group-level predictor values
#> vector[N] Z_1_1;
#> int prior_only; // should the likelihood be ignored?
#> }
#> transformed data {
#> matrix[N, Kc] Xc; // centered version of X
#> vector[Kc] means_X; // column means of X before centering
#> for (i in 1:K) {
#> means_X[i] = mean(X[, i]);
#> Xc[, i] = X[, i] - means_X[i];
#> }
#> }
#> parameters {
#> vector[Kc] b; // regression coefficients
#> ordered[nthres] Intercept; // temporary thresholds for centered predictors
#> vector<lower=0>[M_1] sd_1; // group-level standard deviations
#> array[M_1] vector[N_1] z_1; // standardized group-level effects
#> }
#> transformed parameters {
#> real disc = 1; // discrimination parameters
#> vector[N_1] r_1_1; // actual group-level effects
#> real lprior = 0; // prior contributions to the log posterior
#> r_1_1 = (sd_1[1] * (z_1[1]));
#> lprior += student_t_lpdf(Intercept | 3, 0, 2.5);
#> lprior += student_t_lpdf(sd_1 | 3, 0, 2.5)
#> - 1 * student_t_lccdf(0 | 3, 0, 2.5);
#> }
#> model {
#> // likelihood including constants
#> if (!prior_only) {
#> // initialize linear predictor term
#> vector[N] mu = rep_vector(0.0, N);
#> mu += Xc * b;
#> for (n in 1:N) {
#> // add more terms to the linear predictor
#> mu[n] += r_1_1[J_1[n]] * Z_1_1[n];
#> }
#> for (n in 1:N) {
#> target += ordered_logistic_lpmf(Y[n] | mu[n], Intercept);
#> }
#> }
#> // priors including constants
#> target += lprior;
#> target += std_normal_lpdf(z_1[1]);
#> }
#> generated quantities {
#> // compute actual thresholds
#> vector[nthres] b_Intercept = Intercept + dot_product(means_X, b);
#> }