NKnotsTest: Test of functional form assumption using B-splines

In davidaarmstrong/damisc: Dave Armstrong's Miscellaneous Functions

Test of functional form assumption using B-splines

Description

Estimate hypothesis test of lower- and higher-order non-linear relationships against an assumed target relationship.

Usage

NKnotsTest(
  form,
  var,
  data,
  targetdf = 1,
  degree = 3,
  min.knots = 1,
  max.knots = 10,
  adjust = "none"
)

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.
targetdf	The assumed degrees of freedom against which the tests will be conducted.
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.
adjust	Method by which p-values will be adjusted (see p.adjust)

Value

A matrix with the following columns:

F	F statistics of test of candidate models against target model
DF1	Numerator DF from F-test
DF2	Denominator DF from F-test
p(F)	p-value from the F-test
Clarke	Test statistic from the Clarke test
Pr(Better)	The Clarke statistic divided by the number of observations
p(Clarke)	p-value from the Clarke test. (T) means that the significant p-value is in favor of the Target model and (C) means the significant p-value is in favor of the candidate (alternative) model.
Delta_AIC	AIC(candidate model) - AIC(target model)
Delta_AICc	AICc(candidate model) - AICc(target model)
Delta_BIC	BIC(candidate model) - BIC(target model)

Author(s)

Dave Armstrong

Examples

data(Prestige, package="carData")
NKnotsTest(prestige ~ education + type, var="income", data=na.omit(Prestige), targetdf=3)

davidaarmstrong/damisc documentation built on Oct. 1, 2023, 3:05 p.m.