Kpart-package: Kpart

In Kpart: Cubic Spline Fitting with Knot Selection

Description

Cubic spline regression using the absolute maximum deviate to determine potential knots. This version also includes support for addidtional independednt variables to be included in the model.

Details

Package:	Kpart
Type:	Package
Version:	1.2.2
Date:	2012-08-02
License:	Open Source

~~ This package is intended for use with non-linearly associated data. The function part firsts selects points for cubic spline knots using an algorithm to find the absolute maximum deviate from the partition mean, then fits a best fitting model by using the best subset method and maximum adjR2. The function returns the values selected as knots in the model. The function part(d, outcomeVariable, splineTerm, additionalVars = NULL, K) takes five arguments. K is a positive integer that indicates how many equally spaced partitions the user would like to search.~~

– Recent update includes support for additional variables, 2016-07-23. –

Author(s)

Eric Golinko

Maintainer: [e-mail]

References

Golinko, Eric David. A min/max algorithm for cubic splines over k-partitions. Florida Atlantic University, 2012.

Golinko, Eric, and Lianfen Qian. "A Min. Max Algorithm for Spline Based Modeling of Violent Crime Rates in USA." arXiv preprint arXiv:1804.06806 (2018).

Kpart documentation built on May 2, 2019, 1:45 p.m.