Three summaries are immediately interpretible on the scale of the response variable:

`rmse()`

is the root-mean-squared-error`mae()`

is the mean absolute error`qae()`

is quantiles of absolute error.

Other summaries have varying scales and interpretations:

`mape()`

mean absolute percentage error.`rsae()`

is the relative sum of absolute errors.`mse()`

is the mean-squared-error.`rsquare()`

is the variance of the predictions divided by the variance of the response.

mse(model, data) rmse(model, data) mae(model, data) rsquare(model, data) qae(model, data, probs = c(0.05, 0.25, 0.5, 0.75, 0.95)) mape(model, data) rsae(model, data)

model | A model |
---|---|

data | The dataset |

probs | Numeric vector of probabilities |

mod <- lm(mpg ~ wt, data = mtcars) mse(mod, mtcars)#> [1] 8.697561rmse(mod, mtcars)#> [1] 2.949163rsquare(mod, mtcars)#> [1] 0.7528328mae(mod, mtcars)#> [1] 2.340642qae(mod, mtcars)#> 5% 25% 50% 75% 95% #> 0.1784985 1.0005640 2.0946199 3.2696108 6.1794815mape(mod, mtcars)#> [1] 0.1260733rsae(mod, mtcars)#> [1] 0.1165042