Define priors for specific parameters or classes of parameters.
set_prior(
prior,
class = "b",
coef = "",
group = "",
resp = "",
dpar = "",
nlpar = "",
lb = NA,
ub = NA,
check = TRUE
)
prior(prior, ...)
prior_(prior, ...)
prior_string(prior, ...)
empty_prior()A character string defining a distribution in Stan language
The parameter class. Defaults to "b"
(i.e. population-level effects).
See 'Details' for other valid parameter classes.
Name of the coefficient within the parameter class.
Grouping factor for group-level parameters.
Name of the response variable. Only used in multivariate models.
Name of a distributional parameter. Only used in distributional models.
Name of a non-linear parameter. Only used in non-linear models.
Lower bound for parameter restriction. Currently only allowed
for classes "b". Defaults to NULL, that is no restriction.
Upper bound for parameter restriction. Currently only allowed
for classes "b". Defaults to NULL, that is no restriction.
Logical; Indicates whether priors
should be checked for validity (as far as possible).
Defaults to TRUE. If FALSE, prior is passed
to the Stan code as is, and all other arguments are ignored.
Arguments passed to set_prior.
set_prior is used to define prior distributions for parameters
in brms models. The functions prior, prior_, and
prior_string are aliases of set_prior each allowing
for a different kind of argument specification.
prior allows specifying arguments as expression without
quotation marks using non-standard evaluation.
prior_ allows specifying arguments as one-sided formulas
or wrapped in quote.
prior_string allows specifying arguments as strings just
as set_prior itself.
Below, we explain its usage and list some common prior distributions for parameters. A complete overview on possible prior distributions is given in the Stan Reference Manual available at https://mc-stan.org/.
To combine multiple priors, use c(...) or the + operator
(see 'Examples'). brms does not check if the priors are written
in correct Stan language. Instead, Stan will check their
syntactical correctness when the model is parsed to C++ and
returns an error if they are not.
This, however, does not imply that priors are always meaningful if they are
accepted by Stan. Although brms trys to find common problems
(e.g., setting bounded priors on unbounded parameters), there is no guarantee
that the defined priors are reasonable for the model.
Below, we list the types of parameters in brms models,
for which the user can specify prior distributions.
Below, we provide details for the individual parameter classes that you can
set priors on. Often, it may not be immediately clear, which parameters are
present in the model. To get a full list of parameters and parameter
classes for which priors can be specified (depending on the model) use
function default_prior.
1. Population-level ('fixed') effects
Every Population-level effect has its own regression parameter
represents the name of the corresponding population-level effect.
Suppose, for instance, that y is predicted by x1 and x2
(i.e., y ~ x1 + x2 in formula syntax).
Then, x1 and x2 have regression parameters
b_x1 and b_x2 respectively.
The default prior for population-level effects (including monotonic and
category specific effects) is an improper flat prior over the reals.
Other common options are normal priors or student-t priors.
If we want to have a normal prior with mean 0 and
standard deviation 5 for x1, and a unit student-t prior with 10
degrees of freedom for x2, we can specify this via
set_prior("normal(0,5)", class = "b", coef = "x1") and set_prior("student_t(10, 0, 1)", class = "b", coef = "x2").
To put the same prior on all population-level effects at once,
we may write as a shortcut set_prior("<prior>", class = "b").
This also leads to faster sampling, because priors can be vectorized in this case.
Both ways of defining priors can be combined using for instance
set_prior("normal(0, 2)", class = "b") and set_prior("normal(0, 10)", class = "b", coef = "x1")
at the same time. This will set a normal(0, 10) prior on
the effect of x1 and a normal(0, 2) prior
on all other population-level effects.
However, this will break vectorization and
may slow down the sampling procedure a bit.
In case of the default intercept parameterization
(discussed in the 'Details' section of brmsformula),
general priors on class "b" will not affect
the intercept. Instead, the intercept has its own parameter class
named "Intercept" and priors can thus be
specified via set_prior("<prior>", class = "Intercept").
Setting a prior on the intercept will not break vectorization
of the other population-level effects.
Note that technically, this prior is set on an intercept that
results when internally centering all population-level predictors
around zero to improve sampling efficiency. On this centered
intercept, specifying a prior is actually much easier and
intuitive than on the original intercept, since the former
represents the expected response value when all predictors
are at their means. To treat the intercept as an ordinary
population-level effect and avoid the centering parameterization,
use 0 + Intercept on the right-hand side of the model formula.
In non-linear models, population-level effects are defined separately
for each non-linear parameter. Accordingly, it is necessary to specify
the non-linear parameter in set_prior so that priors
we can be assigned correctly.
If, for instance, alpha is the parameter and x the predictor
for which we want to define the prior, we can write
set_prior("<prior>", coef = "x", nlpar = "alpha").
As a shortcut we can use set_prior("<prior>", nlpar = "alpha")
to set the same prior on all population-level effects of alpha at once.
The same goes for specifying priors for specific distributional
parameters in the context of distributional regression, for example,
set_prior("<prior>", coef = "x", dpar = "sigma").
For most other parameter classes (see below), you need to indicate
non-linear and distributional parameters in the same way as shown here.
If desired, population-level effects can be restricted to fall only
within a certain interval using the lb and ub arguments
of set_prior. This is often required when defining priors
that are not defined everywhere on the real line, such as uniform
or gamma priors. When defining a uniform(2,4) prior,
you should write set_prior("uniform(2,4)", lb = 2, ub = 4).
When using a prior that is defined on the positive reals only
(such as a gamma prior) set lb = 0.
In most situations, it is not useful to restrict population-level
parameters through bounded priors
(non-linear models are an important exception),
but if you really want to this is the way to go.
2. Group-level ('random') effects
Each group-level effect of each grouping factor has a standard deviation named
sd_<group>_<coef>. Consider, for instance, the formula
y ~ x1 + x2 + (1 + x1 | g).
We see that the intercept as well as x1 are group-level effects
nested in the grouping factor g.
The corresponding standard deviation parameters are named as
sd_g_Intercept and sd_g_x1 respectively.
These parameters are restricted to be non-negative and, by default,
have a half student-t prior with 3 degrees of freedom and a
scale parameter that depends on the standard deviation of the response
after applying the link function. Minimally, the scale parameter is 2.5.
This prior is used (a) to be only weakly informative in order to influence
results as few as possible, while (b) providing at least some regularization
to considerably improve convergence and sampling efficiency.
To define a prior distribution only for standard deviations
of a specific grouping factor,
use set_prior("<prior>", class = "sd", group = "<group>").
To define a prior distribution only for a specific standard deviation
of a specific grouping factor, you may write set_prior("<prior>", class = "sd", group = "<group>", coef = "<coef>").
If there is more than one group-level effect per grouping factor,
the correlations between those effects have to be estimated.
The prior lkj_corr_cholesky(eta) or in short
lkj(eta) with eta > 0
is essentially the only prior for (Cholesky factors) of correlation matrices.
If eta = 1 (the default) all correlations matrices
are equally likely a priori. If eta > 1, extreme correlations
become less likely, whereas 0 < eta < 1 results in
higher probabilities for extreme correlations.
Correlation matrix parameters in brms models are named as
cor_<group>, (e.g., cor_g if g is the grouping factor).
To set the same prior on every correlation matrix,
use for instance set_prior("lkj(2)", class = "cor").
Internally, the priors are transformed to be put on the Cholesky factors
of the correlation matrices to improve efficiency and numerical stability.
The corresponding parameter class of the Cholesky factors is L,
but it is not recommended to specify priors for this parameter class directly.
4. Smoothing Splines
Smoothing splines are implemented in brms using the 'random effects'
formulation as explained in gamm). Thus, each
spline has its corresponding standard deviations modeling the variability
within this term. In brms, this parameter class is called sds
and priors can be specified via
set_prior("<prior>", class = "sds", coef = "<term label>").
The default prior is the same as for standard deviations of group-level effects.
5. Gaussian processes
Gaussian processes as currently implemented in brms have two
parameters, the standard deviation parameter sdgp, and
characteristic length-scale parameter lscale (see gp
for more details). The default prior of sdgp is the same as for
standard deviations of group-level effects. The default prior of
lscale is an informative inverse-gamma prior specifically tuned to
the covariates of the Gaussian process (for more details see
https://betanalpha.github.io/assets/case_studies/gp_part3/part3.html).
This tuned prior may be overly informative in some cases, so please
consider other priors as well to make sure inference is robust to the prior
specification. If tuning fails, a half-normal prior is used instead.
6. Autocorrelation parameters
The autocorrelation parameters currently implemented are named ar
(autoregression), ma (moving average), sderr (standard
deviation of latent residuals in latent ARMA models), cosy (compound
symmetry correlation), car (spatial conditional autoregression), as
well as lagsar and errorsar (spatial simultaneous
autoregression).
Priors can be defined by set_prior("<prior>", class = "ar") for
ar and similar for other autocorrelation parameters. By default,
ar and ma are bounded between -1 and 1;
cosy, car, lagsar, and errorsar are bounded
between 0 and 1. The default priors are flat over the
respective definition areas.
7. Parameters of measurement error terms
Latent variables induced via measurement error me terms
require both mean and standard deviation parameters, whose prior classes
are named "meanme" and "sdme", respectively. If multiple
latent variables are induced this way, their correlation matrix will
be modeled as well and corresponding priors can be specified via the
"corme" class. All of the above parameters have flat priors over
their respective definition spaces by default.
8. Distance parameters of monotonic effects
As explained in the details section of brm,
monotonic effects make use of a special parameter vector to
estimate the 'normalized distances' between consecutive predictor
categories. This is realized in Stan using the simplex
parameter type. This class is named "simo" (short for
simplex monotonic) in brms.
The only valid prior for simplex parameters is the
dirichlet prior, which accepts a vector of length K - 1
(K = number of predictor categories) as input defining the
'concentration' of the distribution. Explaining the dirichlet prior
is beyond the scope of this documentation, but we want to describe
how to define this prior syntactically correct.
If a predictor x with K categories is modeled as monotonic,
we can define a prior on its corresponding simplex via prior(dirichlet(<vector>), class = simo, coef = mox1).
The 1 in the end of coef indicates that this is the first
simplex in this term. If interactions between multiple monotonic
variables are modeled, multiple simplexes per term are required.
For <vector>, we can put in any R expression
defining a vector of length K - 1. The default is a uniform
prior (i.e. <vector> = rep(1, K-1)) over all simplexes
of the respective dimension.
9. Parameters for specific families
Some families need additional parameters to be estimated.
Families gaussian, student, skew_normal,
lognormal, and gen_extreme_value need the parameter
sigma to account for the residual standard deviation.
By default, sigma has a half student-t prior that scales
in the same way as the group-level standard deviations.
Further, family student needs the parameter
nu representing the degrees of freedom of Student-t distribution.
By default, nu has prior gamma(2, 0.1), which is
close to a penalized complexity prior (see Stan prior choice Wiki),
and a fixed lower bound of 1.
Family negbinomial needs a shape parameter that has by
default inv_gamma(0.4, 0.3) prior which is close to a
penalized complexity prior (see Stan prior choice Wiki).
Families gamma, weibull, and inverse.gaussian,
need a shape parameter that has a gamma(0.01, 0.01)
prior by default.
For families cumulative, cratio, sratio,
and acat, and only if threshold = "equidistant",
the parameter delta is used to model the distance between
two adjacent thresholds.
By default, delta has an improper flat prior over the reals.
The von_mises family needs the parameter kappa, representing
the concentration parameter. By default, kappa has prior
gamma(2, 0.01).
Every family specific parameter has its own prior class, so that
set_prior("<prior>", class = "<parameter>") is the right way to go.
All of these priors are chosen to be weakly informative,
having only minimal influence on the estimations,
while improving convergence and sampling efficiency.
10. Shrinkage priors
To reduce the danger of overfitting in models with many predictor terms fit
on comparably sparse data, brms supports special shrinkage priors, namely
the (regularized) horseshoe and the R2D2 prior.
These priors can be applied on many parameter classes, either directly on
the coefficient classes (e.g., class b), if directly setting priors
on them is supported, or on the corresponding standard deviation
hyperparameters (e.g., class sd) otherwise. Currently, the following
classes support shrinkage priors: b (overall regression
coefficients), sds (SDs of smoothing splines), sdgp (SDs of
Gaussian processes), ar (autoregressive coefficients), ma
(moving average coefficients), sderr (SD of latent residuals),
sdcar (SD of spatial CAR structures), sd (SD of varying
coefficients).
11. Fixing parameters to constants
Fixing parameters to constants is possible by using the constant
function, for example, constant(1) to fix a parameter to 1.
Broadcasting to vectors and matrices is done automatically.
prior(): Alias of set_prior allowing to
specify arguments as expressions without quotation marks.
prior_(): Alias of set_prior allowing to specify
arguments as as one-sided formulas or wrapped in quote.
prior_string(): Alias of set_prior allowing to
specify arguments as strings.
empty_prior(): Create an empty brmsprior object.
## use alias functions
(prior1 <- prior(cauchy(0, 1), class = sd))
#> sd ~ cauchy(0, 1)
(prior2 <- prior_(~cauchy(0, 1), class = ~sd))
#> sd ~ cauchy(0, 1)
(prior3 <- prior_string("cauchy(0, 1)", class = "sd"))
#> sd ~ cauchy(0, 1)
identical(prior1, prior2)
#> [1] TRUE
identical(prior1, prior3)
#> [1] TRUE
# check which parameters can have priors
default_prior(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = cumulative())
#> prior class coef group resp dpar nlpar lb ub
#> student_t(3, 0, 2.5) Intercept
#> student_t(3, 0, 2.5) Intercept 1
#> student_t(3, 0, 2.5) Intercept 2
#> student_t(3, 0, 2.5) Intercept 3
#> (flat) b
#> (flat) b carry
#> (flat) b period
#> (flat) b treat
#> student_t(3, 0, 2.5) sd 0
#> student_t(3, 0, 2.5) sd subject 0
#> student_t(3, 0, 2.5) sd Intercept subject 0
#> source
#> default
#> (vectorized)
#> (vectorized)
#> (vectorized)
#> default
#> (vectorized)
#> (vectorized)
#> (vectorized)
#> default
#> (vectorized)
#> (vectorized)
# define some priors
bprior <- c(prior_string("normal(0,10)", class = "b"),
prior(normal(1,2), class = b, coef = treat),
prior_(~cauchy(0,2), class = ~sd,
group = ~subject, coef = ~Intercept))
# verify that the priors indeed found their way into Stan's model code
stancode(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = cumulative(),
prior = bprior)
#> // 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 += normal_lpdf(b[1] | 1, 2);
#> lprior += normal_lpdf(b[2] | 0,10);
#> lprior += normal_lpdf(b[3] | 0,10);
#> lprior += student_t_lpdf(Intercept | 3, 0, 2.5);
#> lprior += cauchy_lpdf(sd_1[1] | 0, 2)
#> - 1 * cauchy_lccdf(0 | 0, 2);
#> }
#> 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);
#> }
# use the horseshoe prior to model sparsity in regression coefficients
stancode(count ~ zAge + zBase * Trt,
data = epilepsy, family = poisson(),
prior = set_prior("horseshoe(3)"))
#> // generated with brms 2.21.0
#> functions {
#> /* Efficient computation of the horseshoe scale parameters
#> * see Appendix C.1 in https://projecteuclid.org/euclid.ejs/1513306866
#> * Args:
#> * lambda: local shrinkage parameters
#> * tau: global shrinkage parameter
#> * c2: slap regularization parameter
#> * Returns:
#> * scale parameter vector of the horseshoe prior
#> */
#> vector scales_horseshoe(vector lambda, real tau, real c2) {
#> int K = rows(lambda);
#> vector[K] lambda2 = square(lambda);
#> vector[K] lambda_tilde = sqrt(c2 * lambda2 ./ (c2 + tau^2 * lambda2));
#> return lambda_tilde * tau;
#> }
#> /* compute scale parameters of the R2D2 prior
#> * Args:
#> * phi: local weight parameters
#> * tau2: global scale parameter
#> * Returns:
#> * scale parameter vector of the R2D2 prior
#> */
#> vector scales_R2D2(vector phi, real tau2) {
#> return sqrt(phi * tau2);
#> }
#>
#> }
#> 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
#> int<lower=1> Kscales; // number of local scale parameters
#> // data for the horseshoe prior
#> real<lower=0> hs_df; // local degrees of freedom
#> real<lower=0> hs_df_global; // global degrees of freedom
#> real<lower=0> hs_df_slab; // slab degrees of freedom
#> real<lower=0> hs_scale_global; // global prior scale
#> real<lower=0> hs_scale_slab; // slab prior scale
#> 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] zb; // unscaled coefficients
#> real Intercept; // temporary intercept for centered predictors
#> // horseshoe shrinkage parameters
#> real<lower=0> hs_global; // global shrinkage parameter
#> real<lower=0> hs_slab; // slab regularization parameter
#> vector<lower=0>[Kscales] hs_local; // local parameters for the horseshoe prior
#> }
#> transformed parameters {
#> vector[Kc] b; // scaled coefficients
#> vector<lower=0>[Kc] sdb; // SDs of the coefficients
#> vector<lower=0>[Kscales] scales; // local horseshoe scale parameters
#> real lprior = 0; // prior contributions to the log posterior
#> // compute horseshoe scale parameters
#> scales = scales_horseshoe(hs_local, hs_global, hs_scale_slab^2 * hs_slab);
#> sdb = scales[(1):(Kc)];
#> b = zb .* sdb; // scale coefficients
#> lprior += student_t_lpdf(Intercept | 3, 1.4, 2.5);
#> lprior += student_t_lpdf(hs_global | hs_df_global, 0, hs_scale_global)
#> - 1 * log(0.5);
#> lprior += inv_gamma_lpdf(hs_slab | 0.5 * hs_df_slab, 0.5 * hs_df_slab);
#> }
#> model {
#> // likelihood including constants
#> if (!prior_only) {
#> target += poisson_log_glm_lpmf(Y | Xc, Intercept, b);
#> }
#> // priors including constants
#> target += lprior;
#> target += std_normal_lpdf(zb);
#> target += student_t_lpdf(hs_local | hs_df, 0, 1)
#> - rows(hs_local) * log(0.5);
#> }
#> generated quantities {
#> // actual population-level intercept
#> real b_Intercept = Intercept - dot_product(means_X, b);
#> }
# fix certain priors to constants
bprior <- prior(constant(1), class = "b") +
prior(constant(2), class = "b", coef = "zBase") +
prior(constant(0.5), class = "sd")
stancode(count ~ zAge + zBase + (1 | patient),
data = epilepsy, prior = bprior)
#> // generated with brms 2.21.0
#> 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
#> // 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 {
#> real Intercept; // temporary intercept for centered predictors
#> real<lower=0> sigma; // dispersion parameter
#> array[M_1] vector[N_1] z_1; // standardized group-level effects
#> }
#> transformed parameters {
#> vector[Kc] b; // regression coefficients
#> vector<lower=0>[M_1] sd_1; // group-level standard deviations
#> vector[N_1] r_1_1; // actual group-level effects
#> real lprior = 0; // prior contributions to the log posterior
#> b[1] = 1;
#> b[2] = 2;
#> sd_1 = rep_vector(0.5, rows(sd_1));
#> r_1_1 = (sd_1[1] * (z_1[1]));
#> 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) {
#> // 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 += normal_id_glm_lpdf(Y | Xc, mu, b, sigma);
#> }
#> // 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);
#> }
# pass priors to Stan without checking
prior <- prior_string("target += normal_lpdf(b[1] | 0, 1)", check = FALSE)
stancode(count ~ Trt, data = epilepsy, prior = prior)
#> // generated with brms 2.21.0
#> 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 {
#> vector[Kc] b; // regression coefficients
#> real Intercept; // temporary intercept for centered predictors
#> real<lower=0> sigma; // dispersion parameter
#> }
#> transformed parameters {
#> real lprior = 0; // prior contributions to the log posterior
#> 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;
#> target += normal_lpdf(b[1] | 0, 1);
#> }
#> generated quantities {
#> // actual population-level intercept
#> real b_Intercept = Intercept - dot_product(means_X, b);
#> }
# define priors in a vectorized manner
# useful in particular for categorical or multivariate models
set_prior("normal(0, 2)", dpar = c("muX", "muY", "muZ"))
#> prior class coef group resp dpar nlpar lb ub source
#> normal(0, 2) b muX <NA> <NA> user
#> normal(0, 2) b muY <NA> <NA> user
#> normal(0, 2) b muZ <NA> <NA> user