Compute model quality for a given dataset

Source: R/quality.R

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
data

The dataset
probs

Numeric vector of probabilities

Examples

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