Rapid Forward Stepwise Logistic Regression — fast_logistic_regression_stepwise

Roughly duplicates the following glm-style code:

Usage

fast_logistic_regression_stepwise_forward(
  Xmm,
  ybin,
  mode = "aic",
  pval_threshold = 0.05,
  use_intercept = TRUE,
  verbose = TRUE,
  drop_collinear_variables = FALSE,
  lm_fit_tol = 1e-07,
  ...
)

Arguments

Xmm: The model.matrix for X (you need to create this yourself before).
ybin: The binary response vector.
mode: "aic" (default, fast) or "pval" (slow, but possibly yields a better model).
pval_threshold: The significance threshold to include a new variable. Default is 0.05. If mode == "aic", this argument is ignored.
use_intercept: Should we automatically begin with an intercept? Default is TRUE.
verbose: Print out messages during the loop? Default is TRUE.
drop_collinear_variables: Parameter used in fast_logistic_regression. Default is FALSE. See documentation there.
lm_fit_tol: Parameter used in fast_logistic_regression. Default is 1e-7. See documentation there.
...: Other arguments to be passed to fastLR. See documentation there.

Value

A list of raw results

Details

nullmod = glm(ybin ~ 0, data.frame(Xmm), family = binomial) fullmod = glm(ybin ~ 0 + ., data.frame(Xmm), family = binomial) forwards = step(nullmod, scope = list(lower = formula(nullmod), upper = formula(fullmod)), direction = "forward", trace = 0)

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression_stepwise_forward(
  Xmm = model.matrix(~ . - type, Pima.te), 
  ybin = as.numeric(Pima.te$type == "Yes")
)
#> iteration # 1 of possibly 8 added intercept 
#>    sub iteration # 1 of 7 with feature npreg resulted in aic 405.5482 
#>    sub iteration # 2 of 7 with feature glu resulted in aic 329.9911 
#>    sub iteration # 3 of 7 with feature bp resulted in aic 414.44 
#>    sub iteration # 4 of 7 with feature skin resulted in aic 399.9796 
#>    sub iteration # 5 of 7 with feature bmi resulted in aic 390.839 
#>    sub iteration # 6 of 7 with feature ped resulted in aic 403.7897 
#>    sub iteration # 7 of 7 with feature age resulted in aic 398.4388 
#> iteration # 2 of possibly 8 added feature # 3 named glu with aic 329.9911 
#>    sub iteration # 1 of 6 with feature npreg resulted in aic 317.0171 
#>    sub iteration # 2 of 6 with feature bp resulted in aic 329.8766 
#>    sub iteration # 3 of 6 with feature skin resulted in aic 321.1592 
#>    sub iteration # 4 of 6 with feature bmi resulted in aic 318.4944 
#>    sub iteration # 5 of 6 with feature ped resulted in aic 323.5249 
#>    sub iteration # 6 of 6 with feature age resulted in aic 320.868 
#> iteration # 3 of possibly 8 added feature # 2 named npreg with aic 317.0171 
#>    sub iteration # 1 of 5 with feature bp resulted in aic 318.1562 
#>    sub iteration # 2 of 5 with feature skin resulted in aic 309.0735 
#>    sub iteration # 3 of 5 with feature bmi resulted in aic 302.5406 
#>    sub iteration # 4 of 5 with feature ped resulted in aic 311.7246 
#>    sub iteration # 5 of 5 with feature age resulted in aic 318.0768 
#> iteration # 4 of possibly 8 added feature # 6 named bmi with aic 302.5406 
#>    sub iteration # 1 of 4 with feature bp resulted in aic 304.2813 
#>    sub iteration # 2 of 4 with feature skin resulted in aic 303.9285 
#>    sub iteration # 3 of 4 with feature ped resulted in aic 297.441 
#>    sub iteration # 4 of 4 with feature age resulted in aic 303.353 
#> iteration # 5 of possibly 8 added feature # 7 named ped with aic 297.441 
#>    sub iteration # 1 of 3 with feature bp resulted in aic 299.2284 
#>    sub iteration # 2 of 3 with feature skin resulted in aic 298.9583 
#>    sub iteration # 3 of 3 with feature age resulted in aic 298.7261