Compute model quality for a given dataset

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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

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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 probabilit

Examples

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mod <- lm(mpg ~ wt, data = mtcars)
rmse(mod, mtcars)
rsquare(mod, mtcars)
mae(mod, mtcars)
qae(mod, mtcars)