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.