NKnots: AIC and BIC selection of number of spline knots

In davidaarmstrong/damisc_nodep: Dave Armstrong's Miscellaneous Functions

Description

Calculates AIC and BIC for the selection of knots in a spline over values (potentially including polynomials) up to a user-defined maximum.

Usage

    NKnots(form, var, data, degree=3, min.knots=1,
       max.knots=10, includePoly = FALSE, plot=FALSE,
       criterion=c("AIC", "BIC", "CV"), cvk=10, cviter=10)

Arguments

form	A formula detailing the model for which smoothing is to be evaluated.
var	A character string identifying the variable for which smoothing is to be evaluated.
data	Data frame providing values of all variables in form.
degree	Degree of polynomial in B-spline basis functions.
min.knots	Minimum number of internal B-spline knots to be evaluated.
max.knots	Maximum number of internal B-spline knots to be evaluated.
includePoly	Include linear and polynomial models up to, and including degree-th order polynomials.
plot	Logical indicating whether a plot should be returned.
criterion	Statistical criterion to minimize in order to find the best number of knots - AIC, BIC or Cross-validation.
cvk	Number of groups for cross-validation
cviter	Number of iterations of cross-validation to average over. 10 is the default but in real-world applications, this should be somewhere around 200.

Value

A plot, if plot=TRUE, otherwise a data frame with the degrees of freedom and corresponding fit measure.

Author(s)

Dave Armstrong

Examples

library(car)
NKnots(prestige ~ education + type, var="income", data=na.omit(Prestige), plot=FALSE)

davidaarmstrong/damisc_nodep documentation built on May 15, 2019, 6:25 p.m.