Patient Rule Induction Method (PRIM)

PRIM for bump hunting for high-dimensional regression-type data.

Details

The data are \((\bold{X}_1, Y_1), \dots, (\bold{X}_n, Y_n)\) where \(\bold{X}_i\) is d-dimensional and \(Y_i\) is a scalar response. We wish to find the modal (and/or anti-modal) regions in the conditional expectation \( m(\bold{x}) = \bold{E} (Y | \bold{x}).\) PRIM is a bump-hunting technique introduced by Friedman & Fisher (1999), taken from data mining. PRIM estimates are a sequence of nested hyper-rectangles (boxes).

For an overview of this package, see vignette("prim") for PRIM for bump hunting and estimating highest density difference regions.

Author

Tarn Duong <tarn.duong@gmail.com>

References

Friedman, J.H. & Fisher, N.I. (1999) Bump-hunting for high dimensional data. Statistics and Computing 9, 123–143.

Hyndman, R.J. (1996) Computing and graphing highest density regions. American Statistician 50, 120–126.