Stepwise model selection with fit metrics — select

Compare candidate models added or removed stepwise, then return the accepted steps, model-fit metrics, and the lowest-AIC model.

Usage

select_models(
  data,
  outcome,
  exposures,
  approach = "logit",
  time = NULL,
  event = NULL,
  distribution = "weibull",
  direction = "forward",
  format = c("flextable", "gt", "tibble")
)

Arguments

data: A data frame containing the outcome and predictor variables.
outcome: A single character string indicating the outcome variable. Quoted and bare names are accepted. Not used for survival approaches when time and event are supplied.
exposures: Character vector of predictor variables to consider. Quoted names are recommended in scripts, and bare names are also accepted.
approach: Regression method. One of: "logit", "logbinomial", "poisson", "robpoisson", "negbin", "linear", "cox", or "survreg".
time, event: Survival time and event indicator for approach = "cox" or approach = "survreg". Quoted and bare names are accepted.
distribution: Parametric survival distribution for approach = "survreg". One of "weibull", "exponential", "lognormal", or "loglogistic". Common spellings such as "log-normal" and "log-logistic" are also accepted.
direction: Stepwise selection direction. One of: "forward" (default), "backward", or "both".
format: Output format for the viewing table. One of "flextable" (default), "gt", or "tibble". Use format = "tibble" to keep only the original list structure.

Value

A list with the following components:

results_table: A tibble summarising each accepted step's model metrics (AIC, BIC, deviance, log-likelihood, and adjusted R-squared for linear models).
best_model: The best-fitting model object based on lowest AIC.
all_models: A named list of the accepted stepwise models.
direction: Stepwise selection direction used.
table: A formatted gt_tbl or flextable when format is "gt" or "flextable".

Details

Treat stepwise selection as a screening tool. It is best used alongside the study question, clinical judgement, and model diagnostics rather than as an automatic final-model rule.

Examples

data <- data_birthwt
stepwise <- select_models(
  data = data,
  outcome = "bwt",
  exposures = c("age", "lwt", "smoke"),
  approach = "linear",
  direction = "forward"
)
stepwise$results_table
#> # A tibble: 3 × 9
#>   model_id formula   model_terms n_predictors   AIC   BIC logLik deviance adj_r2
#>      <int> <chr>     <chr>              <int> <dbl> <dbl>  <dbl>    <dbl>  <dbl>
#> 1        1 bwt ~ 1   Intercept …            0 3031. 3038. -1514.  1.000e8 0     
#> 2        2 bwt ~ sm… smoke                  1 3026. 3036. -1510.  9.63 e7 0.0311
#> 3        3 bwt ~ sm… smoke + lwt            2 3022. 3035. -1507.  9.32 e7 0.0578
stepwise$best_model
#> 
#> Call:
#> stats::lm(formula = fmla, data = model_data)
#> 
#> Coefficients:
#> (Intercept)        smoke          lwt  
#>    2501.125     -272.081        4.237  
#>