Stepwise Model Selection with Evaluation Metrics — select

Performs stepwise model selection using forward, backward, or both directions across different regression approaches. Returns a summary table with evaluation metrics (AIC, BIC, log-likelihood, deviance) and the best model.

Usage

select_models(
  data,
  outcome,
  exposures,
  approach = "logit",
  direction = "forward"
)

Arguments

data: A data frame containing the outcome and predictor variables.
outcome: A character string indicating the outcome variable.
exposures: vector of predictor variables to consider in the model.
approach: Regression method. One of: "logit", "log-binomial", "poisson", "robpoisson", "negbin", or "linear".
direction: Stepwise selection direction. One of: "forward" (default), "backward", or "both".

Value

A list with the following components:

results_table: A tibble summarising each tested model's metric (AIC, BIC, deviance, log-likelihood, adjusted R² if applicable).
best_model: The best-fitting model object based on low AIC.
all_models: A named list of all fitted models.

Examples

data <- data_PimaIndiansDiabetes
stepwise <- select_models(
  data = data,
  outcome = "glucose",
  exposures = c("age", "pregnant", "mass"),
  approach = "linear",
  direction = "forward"
)
summary(stepwise)
#>               Length Class  Mode
#> results_table  8     tbl_df list
#> best_model    13     lm     list
#> all_models     3     -none- list
stepwise$results_table
#> # A tibble: 3 × 8
#>   model_id formula              n_predictors   AIC   BIC logLik deviance adj_r2
#>      <dbl> <chr>                       <int> <dbl> <dbl>  <dbl>    <dbl>  <dbl>
#> 1        1 glucose ~ 1                     1 7386. 7395. -3691.  710508. 0     
#> 2        2 glucose ~ mass                  2 7242. 7256. -3618.  665157. 0.0529
#> 3        3 glucose ~ mass + age            2 7190. 7209. -3591.  618925. 0.118 
stepwise$best_model
#> 
#> Call:
#> lm(formula = glucose ~ mass + age, data = data)
#> 
#> Coefficients:
#> (Intercept)         mass          age  
#>     67.0644       1.0029       0.6702  
#>