gaussian_metrics: Select metrics for Gaussian evaluation

In cvms: Cross-Validation for Model Selection

Select metrics for Gaussian evaluation

Description

\Sexpr[results=rd, stage=render]{lifecycle::badge("experimental")}

Enable/disable metrics for Gaussian evaluation. Can be supplied to the `metrics` argument in many of the cvms functions.

Note: Some functions may have slightly different defaults than the ones supplied here.

Usage

gaussian_metrics(
  all = NULL,
  rmse = NULL,
  mae = NULL,
  nrmse_rng = NULL,
  nrmse_iqr = NULL,
  nrmse_std = NULL,
  nrmse_avg = NULL,
  rae = NULL,
  rse = NULL,
  rrse = NULL,
  rmsle = NULL,
  male = NULL,
  mape = NULL,
  mse = NULL,
  tae = NULL,
  tse = NULL,
  r2m = NULL,
  r2c = NULL,
  aic = NULL,
  aicc = NULL,
  bic = NULL
)

Arguments

all	Enable/disable all arguments at once. (Logical) Specifying other metrics will overwrite this, why you can use (all = FALSE, rmse = TRUE) to get only the RMSE metric.
rmse	RMSE. (Default: TRUE) Root Mean Square Error.
mae	MAE. (Default: TRUE) Mean Absolute Error.
nrmse_rng	NRMSE(RNG). (Default: FALSE) Normalized Root Mean Square Error (by target range).
nrmse_iqr	NRMSE(IQR). (Default: TRUE) Normalized Root Mean Square Error (by target interquartile range).
nrmse_std	NRMSE(STD). (Default: FALSE) Normalized Root Mean Square Error (by target standard deviation).
nrmse_avg	NRMSE(AVG). (Default: FALSE) Normalized Root Mean Square Error (by target mean).
rae	RAE. (Default: TRUE) Relative Absolute Error.
rse	RSE. (Default: FALSE) Relative Squared Error.
rrse	RRSE. (Default: TRUE) Root Relative Squared Error.
rmsle	RMSLE. (Default: TRUE) Root Mean Square Log Error.
male	MALE. (Default: FALSE) Mean Absolute Log Error.
mape	MAPE. (Default: FALSE) Mean Absolute Percentage Error.
mse	MSE. (Default: FALSE) Mean Square Error.
tae	TAE. (Default: FALSE) Total Absolute Error
tse	TSE. (Default: FALSE) Total Squared Error.
r2m	r2m. (Default: FALSE) Marginal R-squared.
r2c	r2c. (Default: FALSE) Conditional R-squared.
aic	AIC. (Default: FALSE) Akaike Information Criterion.
aicc	AICc. (Default: FALSE) Corrected Akaike Information Criterion.
bic	BIC. (Default: FALSE) Bayesian Information Criterion.

Author(s)

Ludvig Renbo Olsen, [e-mail]

See Also

Other evaluation functions: binomial_metrics(), confusion_matrix(), evaluate(), evaluate_residuals(), multinomial_metrics()

Examples

# Attach packages
library(cvms)

# Enable only RMSE
gaussian_metrics(all = FALSE, rmse = TRUE)

# Enable all but RMSE
gaussian_metrics(all = TRUE, rmse = FALSE)

# Disable RMSE
gaussian_metrics(rmse = FALSE)

cvms documentation built on Sept. 11, 2024, 6:22 p.m.