Convert (refit) a Bayesian model to frequentist

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)
}
#> 
#> Call:
#> stats::lm(formula = formula$conditional, data = data)
#> 
#> Coefficients:
#>       (Intercept)  Speciesversicolor   Speciesvirginica  
#>             5.006              0.930              1.582  
#> 
# }

# \dontrun{
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)
}
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#>   Approximation) [glmerMod]
#>  Family: binomial  ( logit )
#> Formula: vs ~ mpg + (1 | cyl)
#>    Data: data
#>      AIC      BIC   logLik deviance df.resid 
#>  31.1738  35.5710 -12.5869  25.1738       29 
#> Random effects:
#>  Groups Name        Std.Dev.
#>  cyl    (Intercept) 1.925   
#> Number of obs: 32, groups:  cyl, 3
#> Fixed Effects:
#> (Intercept)          mpg  
#>     -3.9227       0.1723  
# }