Data Frame of Preference Functions
See also
Other preference_order_tools:
f_auto(),
f_auto_rules(),
preference_order_collinear()
Examples
f_functions()
#> name response_type predictors_types
#> 1 f_r2_pearson numeric numeric
#> 2 f_r2_spearman numeric numeric
#> 3 f_r2_glm_gaussian numeric numeric, categorical
#> 4 f_r2_glm_gaussian_poly2 numeric numeric, categorical
#> 5 f_r2_gam_gaussian numeric numeric, categorical
#> 6 f_r2_rpart numeric numeric, categorical
#> 7 f_r2_rf numeric numeric, categorical
#> 8 f_r2_glm_poisson integer counts numeric, categorical
#> 9 f_r2_glm_poisson_poly2 integer counts numeric, categorical
#> 10 f_r2_gam_poisson integer counts numeric, categorical
#> 11 f_auc_glm_binomial binomial numeric, categorical
#> 12 f_auc_glm_binomial_poly2 binomial numeric, categorical
#> 13 f_auc_glm_binomial_poly2 binomial numeric, categorical
#> 14 f_auc_gam_binomial binomial numeric, categorical
#> 15 f_auc_rpart binomial numeric, categorical
#> 16 f_auc_rf binomial numeric, categorical
#> 17 f_v categorical categorical
#> 18 f_v_rf_categorical categorical numeric, categorical
#> expression
#> 1 cor(x, y, method = 'pearson')^2
#> 2 cor(x, y, method = 'spearman')^2
#> 3 glm(y ~ x, family = gaussian(link = 'identity'))
#> 4 glm(y ~ poly(x, degree = 2, raw = TRUE), family = gaussian(link = 'identity'))
#> 5 mgcv::gam(y ~ s(x), family = gaussian(link = 'identity'))
#> 6 rpart::rpart(y ~ x)
#> 7 ranger::ranger(y ~ x)
#> 8 glm(y ~ x, family = poisson(link = 'log'))
#> 9 glm(y ~ poly(x, degree = 2, raw = TRUE), family = poisson(link = 'log'))
#> 10 mgcv::gam(y ~ s(x), family = poisson(link = 'log'))
#> 11 glm(y ~ x, family = quasibinomial(link = 'logit'), weights = case_weights(y))
#> 12 glm(y ~ poly(x, degree = 2, raw = TRUE), family = quasibinomial(link = 'logit'), weights = collinear::case_weights(y))
#> 13 glm(y ~ poly(x, degree = 2, raw = TRUE), family = quasibinomial(link = 'logit'), weights = collinear::case_weights(y))
#> 14 mgcv::gam(y ~ s(x), family = quasibinomial(link = 'logit'), weights = collinear::case_weights(y))
#> 15 rpart::rpart(y ~ x, weights = collinear::case_weights(y))
#> 16 ranger::ranger(y ~ x, case.weights = collinear::case_weights(y))
#> 17 collinear::cramer_v(x, y)
#> 18 ranger::ranger(y ~ x, case.weights = collinear::case_weights(y))
#> preference_metric
#> 1 r-squared
#> 2 pseudo r-squared
#> 3 r-squared
#> 4 r-squared
#> 5 r-squared
#> 6 r-squared
#> 7 r-squared
#> 8 r-squared
#> 9 r-squared
#> 10 r-squared
#> 11 AUC
#> 12 AUC
#> 13 AUC
#> 14 AUC
#> 15 AUC
#> 16 AUC
#> 17 Cramer's V
#> 18 Cramer's V