View source: R/model_performance.lm.R
model_performance.lm | R Documentation |
Compute indices of model performance for regression models.
## S3 method for class 'lm'
model_performance(model, metrics = "all", verbose = TRUE, ...)
model |
A model. |
metrics |
Can be |
verbose |
Toggle off warnings. |
... |
Arguments passed to or from other methods. |
Depending on model
, following indices are computed:
AIC: Akaike's Information Criterion, see ?stats::AIC
AICc: Second-order (or small sample) AIC with a correction for small sample sizes
BIC: Bayesian Information Criterion, see ?stats::BIC
R2: r-squared value, see r2()
R2_adj: adjusted r-squared, see r2()
RMSE: root mean squared error, see performance_rmse()
SIGMA: residual standard deviation, see insight::get_sigma()
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()
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 metrics
).
model <- lm(mpg ~ wt + cyl, data = mtcars)
model_performance(model)
model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
model_performance(model)
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