Convert (refit) a Bayesian model to frequentist — convert_bayesian_as

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 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  
# }