modelr (version 0.1.1)

model-quality: Compute model quality for a given dataset

Description

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.

Usage

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

Arguments

model

A model

data

The dataset

probs

Numeric vector of probabilities

Examples

# NOT RUN {
mod <- lm(mpg ~ wt, data = mtcars)
rmse(mod, mtcars)
rsquare(mod, mtcars)
mae(mod, mtcars)
qae(mod, mtcars)
# }