model-quality {modelr} | R Documentation |
rmse
is the root-mean-squared-error, mae
is the mean
absolute error, qae
is quantiles of absolute error. These can both
be interpreted on the scale of the response; mae
is less sensitive
to outliers. rsquare
is the variance of the predictions divided by
by the variance of the response.
rmse(model, data) mae(model, data) rsquare(model, data) qae(model, data, probs = c(0.05, 0.25, 0.5, 0.75, 0.95))
model |
A model |
data |
The dataset |
probs |
Numeric vector of probabilit |
mod <- lm(mpg ~ wt, data = mtcars) rmse(mod, mtcars) rsquare(mod, mtcars) mae(mod, mtcars) qae(mod, mtcars)