Association Between a Continuous Response and a Continuous Predictor

These functions take a data frame with two numeric continuous columns "x" (predictor) and "y" (response), fit a univariate model, and return the R-squared of the observations versus the model predictions:

• f_r2_pearson(): Pearson's R-squared.

• f_r2_spearman(): Spearman's R-squared.

• f_r2_glm_gaussian(): Pearson's R-squared of a GLM model fitted with stats::glm(), with formula y ~ s(x) and family stats::gaussian(link = "identity").

• f_r2_glm_gaussian_poly2(): Pearson's R-squared of a GLM model fitted with stats::glm(), with formula y ~ stats::poly(x, degree = 2, raw = TRUE) and family stats::gaussian(link = "identity").

• f_r2_gam_gaussian(): Pearson's R-squared of a GAM model fitted with mgcv::gam(), with formula y ~ s(x) and family stats::gaussian(link = "identity").

• f_r2_rpart(): Pearson's R-squared of a Recursive Partition Tree fitted with rpart::rpart() with formula y ~ x.

• f_r2_rf(): Pearson's R-squared of a 100 trees Random Forest model fitted with ranger::ranger() and formula y ~ x.

Usage

f_r2_pearson(df)

f_r2_spearman(df)

f_r2_glm_gaussian(df)

f_r2_glm_gaussian_poly2(df)

f_r2_gam_gaussian(df)

f_r2_rpart(df)

f_r2_rf(df)

Arguments

df

(required, data frame) with columns:

• "x": (numeric) continuous predictor.

• "y" (numeric) continuous response.

Value

numeric: R-squared

See also

Other preference_order_functions: f_auc, f_r2_counts, f_v(), f_v_rf_categorical()

Other preference_order_functions: f_auc, f_r2_counts, f_v(), f_v_rf_categorical()

Other preference_order_functions: f_auc, f_r2_counts, f_v(), f_v_rf_categorical()

Other preference_order_functions: f_auc, f_r2_counts, f_v(), f_v_rf_categorical()

Other preference_order_functions: f_auc, f_r2_counts, f_v(), f_v_rf_categorical()

Other preference_order_functions: f_auc, f_r2_counts, f_v(), f_v_rf_categorical()

Other preference_order_functions: f_auc, f_r2_counts, f_v(), f_v_rf_categorical()

Other preference_order_functions: f_auc, f_r2_counts, f_v(), f_v_rf_categorical()

Examples

data(vi)

#reduce size to speed-up example
vi <- vi[1:1000, ]

#numeric response and predictor
#to data frame without NAs
df <- data.frame(
  y = vi[["vi_numeric"]],
  x = vi[["swi_max"]]
) |>
  na.omit()

# Continuous response

#Pearson R-squared
f_r2_pearson(df = df)
#> [1] 0.5933516

#Spearman R-squared
f_r2_spearman(df = df)
#> [1] 0.4532391

#R-squared of a gaussian gam
f_r2_glm_gaussian(df = df)
#> [1] 0.5933516

#gaussian glm with second-degree polynomials
f_r2_glm_gaussian_poly2(df = df)
#> [1] 0.6007664

#R-squared of a gaussian gam
f_r2_gam_gaussian(df = df)
#> [1] 0.6704248

#recursive partition tree
f_r2_rpart(df = df)
#> [1] 0.6543777

#random forest model
f_r2_rf(df = df)
#> [1] 0.7279192

#load example data
data(vi)

#reduce size to speed-up example
vi <- vi[1:1000, ]

#continuous response and predictor
#to data frame without NAs
df <- data.frame(
  y = vi[["vi_numeric"]],
  x = vi[["swi_max"]]
) |>
  na.omit()

# Continuous response

#Pearson R-squared
f_r2_pearson(df = df)
#> [1] 0.5933516

#Spearman R-squared
f_r2_spearman(df = df)
#> [1] 0.4532391

#R-squared of a gaussian gam
f_r2_glm_gaussian(df = df)
#> [1] 0.5933516

#gaussian glm with second-degree polynomials
f_r2_glm_gaussian_poly2(df = df)
#> [1] 0.6007664

#R-squared of a gaussian gam
f_r2_gam_gaussian(df = df)
#> [1] 0.6704248

#recursive partition tree
f_r2_rpart(df = df)
#> [1] 0.6543777

#random forest model
f_r2_rf(df = df)
#> [1] 0.7279192