# A3 Error Metrics for Predictive Models

### Description

A package for the generation of accurate, accessible, and adaptable error metrics for developing high quality predictions and inferences. The name A3 (pronounced "A-Cubed") comes from the combination of the first letters of these three primary adjectives.

### Details

The overarching purpose of the outputs and tools in this package are to make the accurate assessment of model errors more accessible to a wider audience. Furthermore, a standardized set of reporting features are provided by this package which create consistent outputs for virtually any predictive model. This makes it straightforward to compare, for instance, a linear regression model to more exotic techniques such as Random forests or Support vector machines.

The standard outputs for each model fit provided by the A3 package include:

Average Slope: Equivalent to a linear regression coefficient.

Cross Validated

*R^2*: Robust calculation of*R^2*(percent of squared error explained by the model compared to the null model) values adjusting for over-fitting.p Values: Robust calculation of p-values requiring no parametric assumptions other than independence between observations (which may be violated if compensated for).

The primary functions that will be used are
`a3`

for arbitrary modeling functions and
`a3.lm`

for linear models. This package also
includes `print.A3`

and `plot.A3`

for outputting the A3 results.

### Author(s)

Scott Fortmann-Roe scottfr@berkeley.edu http://Scott.Fortmann-Roe.com