R: Confidence intervals for breakpoints

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(4, getOption("digits") - 1), ...)

### 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 returning 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 0.5-3.0 Index]