Specify normal/exponential priors for MAP and Bayesian inference
Source:R/wrappers_prior.R
prior_normal.Rd`prior_normal()` specifies normal priors for location parameters and exponential priors for scale parameters. It is intended for MAP and Bayesian inference, not for `classic()`.
Usage
prior_normal(
Intercept_sd = 10,
mu_sd = 10,
b_sd = 10,
sigma_rate = 1/5,
tau_rate = 1/5,
...
)Arguments
- Intercept_sd
Standard deviation for the intercept prior. If `NULL`, no intercept prior is added.
- mu_sd
Standard deviation for mean/intercept priors. If `NULL`, no mean prior is added.
- b_sd
Standard deviation for coefficient priors. If `NULL`, no coefficient prior is added.
- sigma_rate
Rate for residual standard deviation priors. The default `1 / 5` gives an exponential prior with mean 5. If `NULL`, no sigma prior is added.
- tau_rate
Rate for random-effect standard deviation priors. The default `1 / 5` gives an exponential prior with mean 5. If `NULL`, no tau prior is added.
- ...
Optional wrapper-specific hyperparameters.