confint.segmented {segmented}R Documentation

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, var.diff=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 reversed before printing. This is useful when a null-constraint has been set on the last slope.

var.diff

logical. If var.diff=TRUE and there is a single segmented variable, the standard error is based on sandwich-type formula of the covariance matrix. See Details in summary.segmented.

digits

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

...

additional parameters

Details

Currently confint.segmented computes confidence limits for the breakpoints using the standard error coming from the Delta method for the ratio of two random variables. This value is an approximation (slightly) better than 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.

Value

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

Author(s)

Vito M.R. Muggeo

See Also

segmented and lines.segmented to plot the estimated breakpoints with corresponding confidence intervals.

Examples

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)
[Package segmented version 0.5-2.1 Index]