Compute indices of model performance for regression models.
## S3 method for class 'lm' model_performance(model, metrics = "all", verbose = TRUE, ...)
Toggle off warnings.
Arguments passed to or from other methods.
model, following indices are computed:
AIC: Akaike's Information Criterion, see
AICc: Second-order (or small sample) AIC with a correction for small sample sizes
BIC: Bayesian Information Criterion, see
R2: r-squared value, see
R2_adj: adjusted r-squared, see
RMSE: root mean squared error, see
SIGMA: residual standard deviation, see
LOGLOSS: Log-loss, see
SCORE_LOG: score of logarithmic proper scoring rule, see
SCORE_SPHERICAL: score of spherical proper scoring rule, see
PCP: percentage of correct predictions, see
model_performance() correctly detects transformed response and
returns the "corrected" AIC and BIC value on the original scale. To get back
to the original scale, the likelihood of the model is multiplied by the
Jacobian/derivative of the transformation.
A data frame (with one row) and one column per "index" (see
model <- lm(mpg ~ wt + cyl, data = mtcars) model_performance(model) model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial") model_performance(model)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.