Computes some common model accuracy indices, such as the R squared, mean absolute error, mean absolute percent error and root mean square error.
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)
| model | An object of class |
|---|---|
| na.rm | A logical value indicating whether or not to strip |
An object of class lvmisc_accuracy. See "Details" for more
information.
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.
mtcars <- tibble::as_tibble(mtcars, rownames = "car") m <- stats::lm(disp ~ mpg, mtcars) cv <- loo_cv(m, mtcars, car, keep = "used") accuracy(m)#> AIC BIC R2 R2_adj MAE MAPE RMSE #> 1 363.72 368.11 0.72 0.71 53.21 31.81% 64.74accuracy(cv)#> AIC BIC R2 R2_adj MAE MAPE RMSE #> 1 363.72 368.11 0.72 0.71 56.8 34.57% 68.84