Get summary of model
Usage
model_summary_variables(
model,
coef_sign = 1,
limit_significance = 0.05,
limit_iv = 0.02,
limit_corr = 0.6,
limit_vif = 5
)
model_corr_variables(model)
model_vif_variables(model)
model_iv_variables(model)Examples
data("credit_woe")
data("credit_woe")
m <- glm(bad ~ age_woe + flag_res_phone_woe + months_in_the_job_woe +
payment_day_woe + sex_woe + profession_code_woe + marital_status_woe,
family = binomial, data = head(credit_woe, 10000)
)
model_partials(m)
#> ℹ Fitting and evaluating model with 1 variables: age_woe
#> ℹ Creating woe binning ...
#> ℹ Fitting and evaluating model with 2 variables: age_woe, flag_res_phone_woe
#> ℹ Creating woe binning ...
#> ℹ Fitting and evaluating model with 3 variables: age_woe, flag_res_phone_woe, m...
#> ℹ Creating woe binning ...
#> ℹ Fitting and evaluating model with 4 variables: age_woe, flag_res_phone_woe, m...
#> ℹ Creating woe binning ...
#> ℹ Fitting and evaluating model with 5 variables: age_woe, flag_res_phone_woe, m...
#> ℹ Creating woe binning ...
#> ℹ Fitting and evaluating model with 6 variables: age_woe, flag_res_phone_woe, m...
#> ℹ Creating woe binning ...
#> ℹ Fitting and evaluating model with 7 variables: age_woe, flag_res_phone_woe, m...
#> ℹ Creating woe binning ...
#> # A tibble: 7 × 5
#> variable ks auc iv gini
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 age_woe 0.204 0.624 0.190 0.248
#> 2 flag_res_phone_woe 0.207 0.641 0.248 0.283
#> 3 months_in_the_job_woe 0.223 0.650 0.276 0.301
#> 4 payment_day_woe 0.238 0.656 0.304 0.312
#> 5 sex_woe 0.247 0.659 0.309 0.319
#> 6 profession_code_woe 0.244 0.661 0.329 0.321
#> 7 marital_status_woe 0.244 0.662 0.335 0.324
model_summary_variables(m)
#> Correlation computed with
#> • Method: 'pearson'
#> • Missing treated using: 'pairwise.complete.obs'
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> # A tibble: 8 × 15
#> term estimate std.error statistic p.value correlation_max iv iv_label
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 (Inter… -1.40 0.0262 -53.4 0 NA NA NA
#> 2 age_woe 0.673 0.0736 9.14 6.53e-20 0.465 0.208 medium
#> 3 flag_r… 0.853 0.0898 9.50 2.19e-21 0.0637 0.0749 weak
#> 4 months… 0.623 0.0986 6.32 2.64e-10 0.339 0.0948 weak
#> 5 paymen… 0.780 0.172 4.53 6.04e- 6 0.0640 0.0185 unpredi…
#> 6 sex_woe 0.744 0.158 4.71 2.49e- 6 0.107 0.0265 weak
#> 7 profes… 0.420 0.120 3.50 4.68e- 4 0.354 0.0537 weak
#> 8 marita… 0.294 0.0957 3.07 2.14e- 3 0.465 0.0866 weak
#> # ℹ 7 more variables: vif <dbl>, vif_label <fct>, dummy_significance <lgl>,
#> # dummy_sign <lgl>, dummy_iv <lgl>, dummy_correlation <lgl>, dummy_vif <lgl>
model_corr_variables(m)
#> Correlation computed with
#> • Method: 'pearson'
#> • Missing treated using: 'pairwise.complete.obs'
#> # A tibble: 49 × 3
#> term term2 cor
#> <fct> <fct> <dbl>
#> 1 age_woe age_woe NA
#> 2 flag_res_phone_woe age_woe 0.0633
#> 3 months_in_the_job_woe age_woe 0.339
#> 4 payment_day_woe age_woe 0.0415
#> 5 sex_woe age_woe 0.107
#> 6 profession_code_woe age_woe 0.354
#> 7 marital_status_woe age_woe 0.465
#> 8 age_woe flag_res_phone_woe 0.0633
#> 9 flag_res_phone_woe flag_res_phone_woe NA
#> 10 months_in_the_job_woe flag_res_phone_woe 0.0437
#> # ℹ 39 more rows
model_vif_variables(m)
#> # A tibble: 7 × 3
#> term vif vif_label
#> <fct> <dbl> <fct>
#> 1 age_woe 1.47 low (<5)
#> 2 flag_res_phone_woe 1.01 low (<5)
#> 3 months_in_the_job_woe 1.13 low (<5)
#> 4 payment_day_woe 1.01 low (<5)
#> 5 sex_woe 1.02 low (<5)
#> 6 profession_code_woe 1.09 low (<5)
#> 7 marital_status_woe 1.27 low (<5)
model_iv_variables(m)
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> ℹ Creating woe binning ...
#> ℹ The option bin_close_right was set to FALSE.
#> # A tibble: 7 × 3
#> term iv iv_label
#> <chr> <dbl> <fct>
#> 1 age_woe 0.208 medium
#> 2 flag_res_phone_woe 0.0749 weak
#> 3 months_in_the_job_woe 0.0948 weak
#> 4 payment_day_woe 0.0185 unpredictive
#> 5 sex_woe 0.0265 weak
#> 6 profession_code_woe 0.0537 weak
#> 7 marital_status_woe 0.0866 weak