Constant priors in brms

Function used to set up constant priors in brms. The function does not evaluate its arguments -- it exists purely to help set up the model.

constant(const, broadcast = TRUE)

Arguments

const

Numeric value, vector, matrix of values to which the parameters should be fixed to. Can also be a valid Stan variable in the model.

broadcast

Should const be automatically broadcasted to the correct size of the parameter? Defaults to TRUE. If you supply vectors or matrices in const or vector/matrix valued Stan variables, you need to set broadcast to TRUE (see Examples).

Value

A named list with elements const and broadcast.

See also

set_prior

Examples

stancode(count ~ Base + Age, data = epilepsy,
         prior = prior(constant(1), class = "b"))
#> // generated with brms 2.22.0
#> functions {
#> }
#> data {
#>   int<lower=1> N;  // total number of observations
#>   vector[N] 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
#>   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 {
#>   real Intercept;  // temporary intercept for centered predictors
#>   real<lower=0> sigma;  // dispersion parameter
#> }
#> transformed parameters {
#>   vector[Kc] b;  // regression coefficients
#>   real lprior = 0;  // prior contributions to the log posterior
#>   b = rep_vector(1, rows(b));
#>   lprior += student_t_lpdf(Intercept | 3, 4, 4.4);
#>   lprior += student_t_lpdf(sigma | 3, 0, 4.4)
#>     - 1 * student_t_lccdf(0 | 3, 0, 4.4);
#> }
#> model {
#>   // likelihood including constants
#>   if (!prior_only) {
#>     target += normal_id_glm_lpdf(Y | Xc, Intercept, b, sigma);
#>   }
#>   // priors including constants
#>   target += lprior;
#> }
#> generated quantities {
#>   // actual population-level intercept
#>   real b_Intercept = Intercept - dot_product(means_X, b);
#> }

# will fail parsing because brms will try to broadcast a vector into a vector
stancode(count ~ Base + Age, data = epilepsy,
         prior = prior(constant(alpha), class = "b"),
         stanvars = stanvar(c(1, 0), name = "alpha"))
#> // generated with brms 2.22.0
#> functions {
#> }
#> data {
#>   int<lower=1> N;  // total number of observations
#>   vector[N] 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
#>   int prior_only;  // should the likelihood be ignored?
#>   vector[2] alpha;
#> }
#> 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 {
#>   real Intercept;  // temporary intercept for centered predictors
#>   real<lower=0> sigma;  // dispersion parameter
#> }
#> transformed parameters {
#>   vector[Kc] b;  // regression coefficients
#>   real lprior = 0;  // prior contributions to the log posterior
#>   b = rep_vector(alpha, rows(b));
#>   lprior += student_t_lpdf(Intercept | 3, 4, 4.4);
#>   lprior += student_t_lpdf(sigma | 3, 0, 4.4)
#>     - 1 * student_t_lccdf(0 | 3, 0, 4.4);
#> }
#> model {
#>   // likelihood including constants
#>   if (!prior_only) {
#>     target += normal_id_glm_lpdf(Y | Xc, Intercept, b, sigma);
#>   }
#>   // priors including constants
#>   target += lprior;
#> }
#> generated quantities {
#>   // actual population-level intercept
#>   real b_Intercept = Intercept - dot_product(means_X, b);
#> }

stancode(count ~ Base + Age, data = epilepsy,
         prior = prior(constant(alpha, broadcast = FALSE), class = "b"),
         stanvars = stanvar(c(1, 0), name = "alpha"))
#> // generated with brms 2.22.0
#> functions {
#> }
#> data {
#>   int<lower=1> N;  // total number of observations
#>   vector[N] 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
#>   int prior_only;  // should the likelihood be ignored?
#>   vector[2] alpha;
#> }
#> 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 {
#>   real Intercept;  // temporary intercept for centered predictors
#>   real<lower=0> sigma;  // dispersion parameter
#> }
#> transformed parameters {
#>   vector[Kc] b;  // regression coefficients
#>   real lprior = 0;  // prior contributions to the log posterior
#>   b = alpha;
#>   lprior += student_t_lpdf(Intercept | 3, 4, 4.4);
#>   lprior += student_t_lpdf(sigma | 3, 0, 4.4)
#>     - 1 * student_t_lccdf(0 | 3, 0, 4.4);
#> }
#> model {
#>   // likelihood including constants
#>   if (!prior_only) {
#>     target += normal_id_glm_lpdf(Y | Xc, Intercept, b, sigma);
#>   }
#>   // priors including constants
#>   target += lprior;
#> }
#> generated quantities {
#>   // actual population-level intercept
#>   real b_Intercept = Intercept - dot_product(means_X, b);
#> }