R-squared of Gaussian GLM predictions vs. observations

Fits a Gaussian GLM model y ~ x with the numeric response y and the numeric, character, or factor predictor x using stats::glm() and returns the R-squared of the observations against the predictions (see score_r2()).

Supports cross-validation via the arguments arguments cv_training_fraction (numeric between 0 and 1) and cv_iterations (integer between 1 and n) introduced via ellipsis (...). See preference_order() for further details.

Usage

f_numeric_glm(df, ...)

Arguments

df

(required, dataframe) with columns:

• x: (numeric, character, factor) predictor.

• y (numeric) continuous response.

...

(optional) Accepts the arguments cv_training_fraction (numeric between 0 and 1) and cv_iterations (integer between 1 and Inf) for cross validation.

Value

numeric or numeric vector: R-squared

See also

Other preference_order_functions: f_binomial_gam(), f_binomial_glm(), f_binomial_rf(), f_categorical_rf(), f_count_gam(), f_count_glm(), f_count_rf(), f_numeric_gam(), f_numeric_rf(), preference_order()

Examples

data(vi_smol, package = "spatialData")

df <- data.frame(
  y = vi_smol[["vi_numeric"]],
  x = vi_smol[["swi_max"]]
)

#no cross-validation
f_numeric_glm(df = df)
#> [1] 0.4351327

#cross-validation
f_numeric_glm(
  df = df,
  cv_training_fraction = 0.5,
  cv_iterations = 10
  )
#>  [1] 0.4018297 0.4643336 0.3944430 0.4365350 0.4216669 0.4952122 0.4191398
#>  [8] 0.4177152 0.3816190 0.4106134

#categorical predictor
df <- data.frame(
  y = vi_smol[["vi_numeric"]],
  x = vi_smol[["koppen_zone"]]
)

f_numeric_glm(df = df)
#> [1] 0.7071346