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 ----------------------
# Simple regressions
model <- rstanarm::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  
#> 

model <- rstanarm::stan_glm(vs ~ mpg,
  family = "binomial",
  data = mtcars, chains = 2, refresh = 0
)
bayesian_as_frequentist(model)
#> 
#> Call:  stats::glm(formula = formula$conditional, family = family, data = data)
#> 
#> Coefficients:
#> (Intercept)          mpg  
#>     -8.8331       0.4304  
#> 
#> Degrees of Freedom: 31 Total (i.e. Null);  30 Residual
#> Null Deviance:	    43.86 
#> Residual Deviance: 25.53 	AIC: 29.53

# Mixed models
model <- rstanarm::stan_glmer(
  Sepal.Length ~ Petal.Length + (1 | Species),
  data = iris, chains = 2, refresh = 0
)
bayesian_as_frequentist(model)
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Sepal.Length ~ Petal.Length + (1 | Species)
#>    Data: data
#> REML criterion at convergence: 119.793
#> Random effects:
#>  Groups   Name        Std.Dev.
#>  Species  (Intercept) 1.0778  
#>  Residual             0.3381  
#> Number of obs: 150, groups:  Species, 3
#> Fixed Effects:
#>  (Intercept)  Petal.Length  
#>       2.5045        0.8885  

model <- rstanarm::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 -2*log(L)  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  
# }