R: Slope estimates from segmented relationships

slope {segmented}

R Documentation

Slope estimates from segmented relationships

Description

Computes the slopes of each ‘segmented’ relationship in the fitted model.

Usage

slope(ogg, parm, conf.level = 0.95, rev.sgn=FALSE, 
    var.diff=FALSE, APC=FALSE, digits = max(3, getOption("digits") - 3))

Arguments

`ogg`	an object of class "segmented", returned by any `segmented` method.
`parm`	the segmented variable whose slopes have to be computed. If missing all the segmented variables are considered.
`conf.level`	the confidence level required.
`rev.sgn`	vector of logicals. The length should be equal to the length of `parm`, but it is 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 computed standard errors are based on a sandwich-type formula of the covariance matrix. See Details in `summary.segmented`.
`APC`	logical. If `APC=TRUE` the ‘annual percent changes’, i.e. 100(exp(b)-1)*, are computed for each interval (b is the slope). Only point estimates and confidence intervals are returned.
`digits`	controls number of digits printed in output.

Details

To fit broken-line relationships, segmented uses a parameterization whose coefficients are not the slopes. Therefore given an object "segmented", slope computes point estimates, standard errors, t-values and confidence intervals of the slopes of each segmented relationship in the fitted model.

Value

slope returns a list of matrices. Each matrix represents a segmented relationship and its number of rows equal to the number of segments, while five columns summarize the results.

Note

The returned summary is based on limiting Gaussian distribution for the model parameters involved in the computations. Sometimes, even with large sample sizes such approximations are questionable (e.g., with small difference-in-slope parameters) and the results returned by slope might be unreliable. Therefore is responsability of the user to gauge the applicability of such asymptotic approximations. Anyway, the t values may be not assumed for testing purposes and they should be used just as guidelines to assess the estimate uncertainty.

Author(s)

Vito M. R. Muggeo, vito.muggeo@unipa.it

References

Muggeo, V.M.R. (2003) Estimating regression models with unknown break-points. Statistics in Medicine 22, 3055–3071.

Examples

set.seed(16)
x<-1:100
y<-2+1.5*pmax(x-35,0)-1.5*pmax(x-70,0)+rnorm(100,0,3)
out<-glm(y~1)
out.seg<-segmented(out,seg.Z=~x,psi=list(x=c(20,80)))
## the slopes of the three segments....
slope(out.seg)
rm(x,y,out,out.seg)
#
## an heteroscedastic example..
set.seed(123)
n<-100
x<-1:n/n
y<- -x+1.5*pmax(x-.5,0)+rnorm(n,0,1)*ifelse(x<=.5,.4,.1)
o<-lm(y~x)
oseg<-segmented(o,seg.Z=~x,psi=.6)
slope(oseg)
slope(oseg,var.diff=TRUE) #better CI

[Package segmented version 0.5-2.1 Index]