confint.segmented: Confidence intervals for breakpoints

Description

Computes confidence intervals for the breakpoints in a fitted `segmented' model.

Usage

## S3 method for class 'segmented':
confint(object, parm, level=0.95, rev.sgn=FALSE,
        digits=max(3, getOption("digits") - 3), ...)

Arguments

object

a fitted segmented object.

parm

the segmented variable of interest. If missing all the segmented variables are considered.

level

the confidence level required (default to 0.95).

rev.sgn

vector of logicals. The length should be equal to the length of parm; recycled otherwise. when TRUE it is assumed that the current parm is `minus' the actual segmented variable, therefore the sign is reverse

digits

controls the number of digits to print when printing the output.

...

additional parameters

Value

A list of matrices. Each matrix includes point estimate and confidence limits of the breakpoint(s) for each segmented variable in the model.

Details

Currently confint.segmented computes confidence limits using standard errors coming from the Delta method for the ratio of two random variables of the estimated. This value is a better approximation of the one reported in the `psi' component of the list returned by any segmented method. The resulting confidence intervals are based on the asymptotic Normal distribution of the breakpoint estimator which is reliable just for clear-cut kink relationships. See Details in segmented.

Examples

Run this code

set.seed(10)
x<-1:100
z<-runif(100)
y<-2+1.5*pmax(x-35,0)-1.5*pmax(x-70,0)+10*pmax(z-.5,0)+rnorm(100,0,2)
out.lm<-lm(y~x)
o<-segmented(out.lm,seg.Z=~x+z,psi=list(x=c(30,60),z=.4))
confint(o)