Extract the Stan Code of the dynamite Model

Returns the Stan code of the model. Mostly useful for debugging or for building a customized version of the model.

Usage

get_code(x, ...)

# S3 method for class 'dynamiteformula'
get_code(x, data, time, group = NULL, blocks = NULL, ...)

# S3 method for class 'dynamitefit'
get_code(x, blocks = NULL, ...)

Arguments

x

[dynamiteformula or dynamitefit]
The model formula or an existing dynamitefit object. See dynamiteformula() and dynamite().

...

Ignored.

data

[data.frame, tibble::tibble, or data.table::data.table]
The data that contains the variables in the model in long format. Supported column types are integer, logical, double, and factor. Columns of type character will be converted to factors. Unused factor levels will be dropped. The data can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input data is converted to channel specific matrix representations via stats::model.matrix.lm().

time

[character(1)]
A column name of data that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.

group

[character(1)]
A column name of data that denotes the unique groups or NULL corresponding to a scenario without any groups. If group is NULL, a new column .group is created with constant value 1L is created indicating that all observations belong to the same group. In case of name conflicts with data, see the group_var element of the return object to get the column name of the new variable.

blocks

[character()]
Stan block names to extract. If NULL, extracts the full model code.

Value

The Stan model blocks as a character string.

See also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
cat(get_code(obs(y ~ x, family = "gaussian"),
  data = d, time = "time", group = "id"
))
#> functions {
#> }
#> data {
#>   int<lower=1> T; // number of time points
#>   int<lower=1> N; // number of individuals
#>   int<lower=0> K; // total number of covariates across all channels
#>   array[T] matrix[N, K] X; // covariates as an array of N x K matrices
#>   row_vector[K] X_m; // Means of all covariates at first time point
#>   // number of fixed, varying and random coefficients, and related indices
#>   int<lower=0> K_fixed_y;
#>   int<lower=0> K_y; // K_fixed + K_varying
#>   array[K_fixed_y] int J_fixed_y;
#>   array[K_y] int J_y; // fixed and varying
#>   array[K_fixed_y] int L_fixed_y;
#>   // Parameters of vectorized priors
#>   matrix[K_fixed_y, 2] beta_prior_pars_y;
#>   matrix[N, T] y_y;
#> }
#> transformed data {
#> }
#> parameters {
#>   vector[K_fixed_y] beta_y; // Fixed coefficients
#>   real a_y; // Mean of the first time point
#>   real<lower=0> sigma_y; // SD of the normal distribution
#> }
#> transformed parameters {
#>   // Time-invariant intercept
#>   real alpha_y;
#>   // Define the first alpha using mean a_y
#>   {
#>     vector[K_y] gamma__y;
#>     gamma__y[L_fixed_y] = beta_y;
#>     alpha_y = a_y - X_m[J_y] * gamma__y;
#>   }
#> }
#> model {
#>   a_y ~ normal(0.61, 2.4);
#>   beta_y ~ normal(beta_prior_pars_y[, 1], beta_prior_pars_y[, 2]);
#>   sigma_y ~ exponential(0.83);
#>   {
#>     real ll = 0.0;
#>     vector[K_y] gamma__y;
#>     gamma__y[L_fixed_y] = beta_y;
#>     for (t in 1:T) {
#>       real intercept_y = alpha_y;
#>       ll += normal_id_glm_lupdf(y_y[, t] | X[t][, J_y], intercept_y, gamma__y, sigma_y);
#>     }
#>     target += ll;
#>   }
#> }
#> generated quantities {
#> }
# same as
cat(dynamite(obs(y ~ x, family = "gaussian"),
  data = d, time = "time", group = "id",
  debug = list(model_code = TRUE, no_compile = TRUE)
)$model_code)
#> functions {
#> }
#> data {
#>   int<lower=1> T; // number of time points
#>   int<lower=1> N; // number of individuals
#>   int<lower=0> K; // total number of covariates across all channels
#>   array[T] matrix[N, K] X; // covariates as an array of N x K matrices
#>   row_vector[K] X_m; // Means of all covariates at first time point
#>   // number of fixed, varying and random coefficients, and related indices
#>   int<lower=0> K_fixed_y;
#>   int<lower=0> K_y; // K_fixed + K_varying
#>   array[K_fixed_y] int J_fixed_y;
#>   array[K_y] int J_y; // fixed and varying
#>   array[K_fixed_y] int L_fixed_y;
#>   // Parameters of vectorized priors
#>   matrix[K_fixed_y, 2] beta_prior_pars_y;
#>   matrix[N, T] y_y;
#> }
#> transformed data {
#> }
#> parameters {
#>   vector[K_fixed_y] beta_y; // Fixed coefficients
#>   real a_y; // Mean of the first time point
#>   real<lower=0> sigma_y; // SD of the normal distribution
#> }
#> transformed parameters {
#>   // Time-invariant intercept
#>   real alpha_y;
#>   // Define the first alpha using mean a_y
#>   {
#>     vector[K_y] gamma__y;
#>     gamma__y[L_fixed_y] = beta_y;
#>     alpha_y = a_y - X_m[J_y] * gamma__y;
#>   }
#> }
#> model {
#>   a_y ~ normal(0.61, 2.4);
#>   beta_y ~ normal(beta_prior_pars_y[, 1], beta_prior_pars_y[, 2]);
#>   sigma_y ~ exponential(0.83);
#>   {
#>     real ll = 0.0;
#>     vector[K_y] gamma__y;
#>     gamma__y[L_fixed_y] = beta_y;
#>     for (t in 1:T) {
#>       real intercept_y = alpha_y;
#>       ll += normal_id_glm_lupdf(y_y[, t] | X[t][, J_y], intercept_y, gamma__y, sigma_y);
#>     }
#>     target += ll;
#>   }
#> }
#> generated quantities {
#> }