R: Compute model quality for a given dataset

model-quality {modelr} | R Documentation

## 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 probabilit

### Examples

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

[Package modelr version 0.1.0 Index]