
Convert (refit) a Bayesian model to frequentist
Source:R/convert_bayesian_to_frequentist.R
convert_bayesian_as_frequentist.Rd
Refit Bayesian model as frequentist. Can be useful for comparisons.
Usage
convert_bayesian_as_frequentist(model, data = NULL, REML = TRUE)
bayesian_as_frequentist(model, data = NULL, REML = TRUE)
Arguments
- model
A Bayesian model.
- data
Data used by the model. If
NULL
, will try to extract it from the model.- REML
For mixed effects, should models be estimated using restricted maximum likelihood (REML) (
TRUE
, default) or maximum likelihood (FALSE
)?
Examples
# \donttest{
# Rstanarm ----------------------
if (require("rstanarm")) {
# Simple regressions
model <- stan_glm(Sepal.Length ~ Species,
data = iris, chains = 2, refresh = 0
)
bayesian_as_frequentist(model)
}
#> Loading required package: rstanarm
#> Loading required package: Rcpp
#> This is rstanarm version 2.21.3
#> - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
#> - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
#>
#> Call:
#> stats::lm(formula = formula$conditional, data = data)
#>
#> Coefficients:
#> (Intercept) Speciesversicolor Speciesvirginica
#> 5.006 0.930 1.582
#>
# }
if (FALSE) {
if (require("rstanarm")) {
model <- stan_glm(vs ~ mpg,
family = "binomial",
data = mtcars, chains = 2, refresh = 0
)
bayesian_as_frequentist(model)
# Mixed models
model <- stan_glmer(Sepal.Length ~ Petal.Length + (1 | Species),
data = iris, chains = 2, refresh = 0
)
bayesian_as_frequentist(model)
model <- stan_glmer(vs ~ mpg + (1 | cyl),
family = "binomial",
data = mtcars, chains = 2, refresh = 0
)
bayesian_as_frequentist(model)
}
}