R: Compute model quality for a given dataset

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 0.1.2 Index]