Returns the summary table a la glm
Usage
# S3 method for class 'fast_logistic_regression_stepwise'
summary(object, ...)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
summary(flr)
#> approx_coef approx_se approx_z approx_pval signif
#> (Intercept) -9.55218438 1.096208872 -8.713836 2.937612e-18 ***
#> bmi 0.08410866 0.021950115 3.831810 1.272042e-04 ***
#> glu 0.03797089 0.005441735 6.977718 3.000132e-12 ***
#> npreg 0.17806780 0.045343122 3.927118 8.596977e-05 ***
#> ped 1.16563923 0.444053293 2.624999 8.664935e-03 **