lvmisc (version 0.1.2)

accuracy: Model accuracy

Description

Computes some common model accuracy indices, such as the R squared, mean absolute error, mean absolute percent error and root mean square error.

Usage

accuracy(model, na.rm = FALSE)
# S3 method for default
accuracy(model, na.rm = FALSE)
# S3 method for lvmisc_cv
accuracy(model, na.rm = FALSE)
# S3 method for lm
accuracy(model, na.rm = FALSE)
# S3 method for lmerMod
accuracy(model, na.rm = FALSE)

Value

An object of class lvmisc_accuracy. See "Details" for more information.

Arguments

model

An object of class lvmisc_cv or an object containing a model.

na.rm

A logical value indicating whether or not to strip NA values to compute the indices. Defaults to FALSE.

Details

The method for the lm class (or for the lvmisc_cv class of a lm) returns a data frame with the columns AIC (Akaike information criterion), BIC (Bayesian information criterion), R2 (R squared), R2_adj (adjusted R squared), MAE (mean absolute error), MAPE (mean absolute percent error) and RMSE (root mean square error).

The method for the lmerMod (or for the lvmisc_cv class of a lmerMod) returns a data frame with the columns R2_marg and R2_cond instead of the columns R2 and R2_adj. All the other columns are the same as the method for lm. R2_marg is the marginal R squared, which considers only the variance by the fixed effects of a mixed model, and R2_cond is the conditional R squared, which considers both fixed and random effects variance.

Examples

mtcars <- tibble::as_tibble(mtcars, rownames = "car")
m <- stats::lm(disp ~ mpg, mtcars)
cv <- loo_cv(m, mtcars, car, keep = "used")

accuracy(m)
accuracy(cv)