NKnotsTest {DAMisc} R Documentation

## 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,
)


### 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)

Dave Armstrong

### Examples


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



[Package DAMisc version 1.7.2 Index]