Returns a bvarnet_priors object containing a bvarnet_prior
for every model parameter type. Any argument left as NULL uses the
package default.
Available prior distributions are:
normal(loc, scale)
student_t(loc, scale, df)
cauchy(loc, scale) For standart deviations and random effects, the prior is automatically converted to a half-prior (truncated at
loc) in the Stan code, so the printed format reflects this.
Usage
set_priors(
intercept = NULL,
beta = NULL,
phi = NULL,
sd_u = NULL,
kappa = NULL,
sigma = NULL
)Arguments
- intercept
Prior for the intercept. Only applies to gaussian and bernoulli models; for ordinal models the intercept is absorbed into the kappa (threshold parameter).
- beta
Prior for fixed-effect regression coefficients (slopes).
- phi
Prior for lag coefficients.
- sd_u
Prior for random-effect standard deviations (half-prior).
- kappa
Prior for ordinal cut-points (ordinal models only).
- sigma
Prior for residual standard deviation (gaussian models only; half-prior).