{modelr} R package

{modelr}

Compute model quality for a given dataset

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.

Root Mean Square Error

rmse(fit, data= cheddar)

## [1] 9.431174

mean absolute error

mae(fit, data= cheddar)

## [1] 7.586727

qae(fit, data= cheddar)

##        5%       25%       50%       75%       95% 
##  1.051164  4.087882  5.238398 10.848030 16.609669

Other summaries

• 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.

rsquare(fit, data= cheddar)

## [1] 0.6517747