performance (version 0.4.3)

model_performance.lm: Performance of Regression Models

Description

Compute indices of model performance for regression models.

Usage

# S3 method for lm
model_performance(model, metrics = "all", verbose = TRUE, ...)
# S3 method for glm
model_performance(model, metrics = "all", verbose = TRUE, ...)

Arguments

model

A model.

metrics

Can be "all", "common" or a character vector of metrics to be computed (some of c("AIC", "BIC", "R2", "RMSE", "LOGLOSS", "PCP", "SCORE")). "common" will compute AIC, BIC, R2 and RMSE.

verbose

Toggle off warnings.

...

Arguments passed to or from other methods.

Value

A data frame (with one row) and one column per "index" (see metrics).

Details

Depending on model, following indices are computed:

• AIC Akaike's Information Criterion, see AIC

• BIC Bayesian Information Criterion, see BIC

• R2 r-squared value, see r2

• R2_adj adjusted r-squared, see r2

• RMSE root mean squared error, see performance_rmse

• LOGLOSS Log-loss, see performance_logloss

• SCORE_LOG score of logarithmic proper scoring rule, see performance_score

• SCORE_SPHERICAL score of spherical proper scoring rule, see performance_score

• PCP percentage of correct predictions, see performance_pcp

Examples

# NOT RUN {
model <- lm(mpg ~ wt + cyl, data = mtcars)
model_performance(model)

model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
model_performance(model)
# }