AUC of Binomial GLM with Logit Link

Fits a logistic GLM model y ~ x when y is a binary response with values 0 and 1 and x is numeric. This function is suitable when the response variable is balanced. If the response is unbalanced, then f_logistic_auc_unbalanced() should provide better results.

Usage

f_logistic_auc_balanced(x, y, df)

Arguments

x

(required, character string) name of the predictor variable.

y

(required, character string) name of the response variable

df

(required, data frame) data frame with the columns 'x' and 'y'.

Value

Area Under the Curve

See also

Other preference_order: auc_score(), case_weights(), f_gam_auc_balanced(), f_gam_auc_unbalanced(), f_gam_deviance(), f_logistic_auc_unbalanced(), f_rf_auc_balanced(), f_rf_auc_unbalanced(), f_rf_rsquared(), f_rsquared(), preference_order()

Examples

data(vi)

#subset to limit example run time
vi <- vi[1:1000, ]

f_logistic_auc_balanced(
  x = "growing_season_length", #predictor
  y = "vi_binary",             #binary response
  df = vi
)
#> [1] 0.9367964