model-quality: Compute model quality for a given dataset

In modelr: Modelling Functions that Work with the Pipe

Compute model quality for a given dataset

Description

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.

Usage

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)
rmse(mod, mtcars)
rsquare(mod, mtcars)
mae(mod, mtcars)
qae(mod, mtcars)
mape(mod, mtcars)
rsae(mod, mtcars)

modelr documentation built on March 31, 2023, 5:20 p.m.