Stan Code for brms Models

Generate Stan code for brms models

Usage

# Default S3 method
stancode(
  object,
  data,
  family = gaussian(),
  prior = NULL,
  autocor = NULL,
  data2 = NULL,
  cov_ranef = NULL,
  sparse = NULL,
  sample_prior = "no",
  stanvars = NULL,
  stan_funs = NULL,
  knots = NULL,
  drop_unused_levels = TRUE,
  threads = getOption("brms.threads", NULL),
  normalize = getOption("brms.normalize", TRUE),
  save_model = NULL,
  ...
)

Arguments

object

An object of class formula, brmsformula, or mvbrmsformula (or one that can be coerced to that classes): A symbolic description of the model to be fitted. The details of model specification are explained in brmsformula.

data

An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model.

family

A description of the response distribution and link function to be used in the model. This can be a family function, a call to a family function or a character string naming the family. Every family function has a link argument allowing to specify the link function to be applied on the response variable. If not specified, default links are used. For details of supported families see brmsfamily. By default, a linear gaussian model is applied. In multivariate models, family might also be a list of families.

prior

One or more brmsprior objects created by set_prior or related functions and combined using the c method or the + operator. See also default_prior for more help.

autocor

(Deprecated) An optional cor_brms object describing the correlation structure within the response variable (i.e., the 'autocorrelation'). See the documentation of cor_brms for a description of the available correlation structures. Defaults to NULL, corresponding to no correlations. In multivariate models, autocor might also be a list of autocorrelation structures. It is now recommend to specify autocorrelation terms directly within formula. See brmsformula for more details.

data2

A named list of objects containing data, which cannot be passed via argument data. Required for some objects used in autocorrelation structures to specify dependency structures as well as for within-group covariance matrices.

cov_ranef

(Deprecated) A list of matrices that are proportional to the (within) covariance structure of the group-level effects. The names of the matrices should correspond to columns in data that are used as grouping factors. All levels of the grouping factor should appear as rownames of the corresponding matrix. This argument can be used, among others to model pedigrees and phylogenetic effects. It is now recommended to specify those matrices in the formula interface using the gr and related functions. See vignette("brms_phylogenetics") for more details.

sparse

(Deprecated) Logical; indicates whether the population-level design matrices should be treated as sparse (defaults to FALSE). For design matrices with many zeros, this can considerably reduce required memory. Sampling speed is currently not improved or even slightly decreased. It is now recommended to use the sparse argument of brmsformula and related functions.

sample_prior

Indicate if draws from priors should be drawn additionally to the posterior draws. Options are "no" (the default), "yes", and "only". Among others, these draws can be used to calculate Bayes factors for point hypotheses via hypothesis. Please note that improper priors are not sampled, including the default improper priors used by brm. See set_prior on how to set (proper) priors. Please also note that prior draws for the overall intercept are not obtained by default for technical reasons. See brmsformula how to obtain prior draws for the intercept. If sample_prior is set to "only", draws are drawn solely from the priors ignoring the likelihood, which allows among others to generate draws from the prior predictive distribution. In this case, all parameters must have proper priors.

stanvars

An optional stanvars object generated by function stanvar to define additional variables for use in Stan's program blocks.

stan_funs

(Deprecated) An optional character string containing self-defined Stan functions, which will be included in the functions block of the generated Stan code. It is now recommended to use the stanvars argument for this purpose instead.

knots

Optional list containing user specified knot values to be used for basis construction of smoothing terms. See gamm for more details.

drop_unused_levels

Should unused factors levels in the data be dropped? Defaults to TRUE.

threads

Number of threads to use in within-chain parallelization. For more control over the threading process, threads may also be a brmsthreads object created by threading. Within-chain parallelization is experimental! We recommend its use only if you are experienced with Stan's reduce_sum function and have a slow running model that cannot be sped up by any other means. Can be set globally for the current R session via the "brms.threads" option (see options).

normalize

Logical. Indicates whether normalization constants should be included in the Stan code (defaults to TRUE). Setting it to FALSE requires Stan version >= 2.25 to work. If FALSE, sampling efficiency may be increased but some post processing functions such as bridge_sampler will not be available. Can be controlled globally for the current R session via the `brms.normalize` option.

save_model

Either NULL or a character string. In the latter case, the model's Stan code is saved via cat in a text file named after the string supplied in save_model.

...

Other arguments for internal usage only.

Value

A character string containing the fully commented Stan code to fit a brms model. It is of class c("character", "brmsmodel") to facilitate pretty printing.

Examples

stancode(rating ~ treat + period + carry + (1|subject),
         data = inhaler, family = "cumulative")
#> // generated with brms 2.23.1
#> 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
#>   // prior contributions to the log posterior
#>   real lprior = 0;
#>   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);
#> }

stancode(count ~ zAge + zBase * Trt + (1|patient),
         data = epilepsy, family = "poisson")
#> // generated with brms 2.23.1
#> functions {
#> }
#> data {
#>   int<lower=1> N;  // total number of observations
#>   array[N] int Y;  // response variable
#>   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 without an intercept
#>   vector[Kc] means_X;  // column means of X before centering
#>   for (i in 2:K) {
#>     means_X[i - 1] = mean(X[, i]);
#>     Xc[, i - 1] = X[, i] - means_X[i - 1];
#>   }
#> }
#> parameters {
#>   vector[Kc] b;  // regression coefficients
#>   real Intercept;  // temporary intercept 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 {
#>   vector[N_1] r_1_1;  // actual group-level effects
#>   // prior contributions to the log posterior
#>   real lprior = 0;
#>   r_1_1 = (sd_1[1] * (z_1[1]));
#>   lprior += student_t_lpdf(Intercept | 3, 1.4, 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 += Intercept;
#>     for (n in 1:N) {
#>       // add more terms to the linear predictor
#>       mu[n] += r_1_1[J_1[n]] * Z_1_1[n];
#>     }
#>     target += poisson_log_glm_lpmf(Y | Xc, mu, b);
#>   }
#>   // priors including constants
#>   target += lprior;
#>   target += std_normal_lpdf(z_1[1]);
#> }
#> generated quantities {
#>   // actual population-level intercept
#>   real b_Intercept = Intercept - dot_product(means_X, b);
#> }