davies.test {segmented} | R Documentation |
Given a generalized linear model, the Davies' test can be employed to test for a non-constant regression parameter in the linear predictor.
davies.test(ogg, term, k = 10, alternative = c("two.sided", "less", "greater"))
ogg |
a fitted model returned by glm or lm . |
term |
a character string to mean the segmented variable being tested. |
k |
number of points where the test should be evaluated. See details. |
alternative |
a character string specifying the alternative hypothesis. |
davies.test
tests for a non zero difference-in-slope parameter of a segmented
relationship. Roughtly speaking, the procedure computes k
`naive' (i.e. assuming
fixed and known the breakpoint) Wald statistics for the difference-in-slope,
seeks the `best' value (according to the alternative hypothesis), and then corrects the selected
(minimum) p-value. The k evaluation points are the quantiles of the variable term
.
A list with class 'htest' containing the following components:
method |
title (character) |
data.name |
the regression model and the segmented variable being tested |
statistic |
the point at which the maximum (or the minimum if alternative="less" ) occurs |
parameter |
number of evaluation points |
p.value |
the adjusted p-value |
Currently davies.test
does not work if the fitted model ogg
has been built without the argument data
.
Strictly speaking, the Davies test is not confined to the segmented regression; the procedure can be applied when a nuicance parameter vanishes under the null hypothesis. The test is slightly conservative, as the computed p-value is actually an upper bound.
Vito M.R. Muggeo
Davies, R.B. (1987) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74, 33–43.
set.seed(20) z<-runif(100) x<-rnorm(100,2) y<-2+10*pmax(z-.5,0)+rnorm(100,0,2) o<-lm(y~z+x) davies.test(o,"z") davies.test(o,"x")