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)

## Arguments

model A model The dataset Numeric vector of probabilities

## Examples

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