model-quality {modelr} | R Documentation |
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)
[Package modelr version 0.1.11 Index]