confint.segmentation {tilingArray}R Documentation

Compute Bai's confidence intervals for specified segmentations

Description

Compute Bai's confidence intervals for specified segmentations

Usage

## S3 method for class 'segmentation':
confint(object,parm="breakpoints", level = 0.95,
                                 nSegments=NULL, 
                                 het.reg = FALSE,het.err = FALSE, ...)

Arguments

object Object of class "segmentation"; Result of findSegments
parm character; which parameters to compute confidence intervals for; only "breakpoints" is implemented
level Confidence level of the confidence intervals
nSegments Number of segments in segmentations to compute confidence intervals for. Defaults to computing them for all segmentations considered in findSegments
het.reg logical. Should heterogenous regressors be assumed? If set to FALSE the distribution of the regressors is assumed to be homogenous over the segments.
het.err logical. Should heterogenous errors be assumed? If set to FALSE the distribution of the errors is assumed to be homogenous over the segments.
... currently not used

Details

Basically, this function just prepares an object for calling the function computeConfInt.

The distribution function used for the computation of confidence intervals of breakpoints is given in Bai (1997). The procedure, in particular the usage of heterogenous regressors and/or errors, is described in more detail in Bai & Perron (2003).

The breakpoints should be computed from a formula with breakpoints, then the confidence intervals for the breakpoints can be derived by confint and these can be visualized with the segmentation. For an example see plot.segmentation.

Value

An object of class "segmentation". Actually the same as the argument object with the following list items replaced

chosenSegNo Numeric; Segment numbers of segmentations, for which confidence intervals were computed
confInt List of confidence intervals as tables for those segmentations
residuals List of numeric vectors containing the residuals for those segmentations
call with call of this function appended

Author(s)

Joern Toedling toedling@ebi.ac.uk

References

Bai J. (1997), Estimation of a Change Point in Multiple Regression Models, Review of Economics and Statistics, 79, 551-563.

Bai J., Perron P. (2003), Computation and Analysis of Multiple Structural Change Models, Journal of Applied Econometrics, 18, 1-22.

See Also

computeConfInt,findSegments, plot.segmentation,confint

Examples

   # generate random data with 5 segments:
   x <- c(rnorm(10,0,1),rnorm(10,3,1),rnorm(10,0.5,0.5),
          rnorm(10,1.5,1),rnorm(10,5,1))

   segres <-  findSegments(x, maxcp=10, maxk=15)
   segres <- confint.segmentation(segres,nSegments=c(3,4,5,6))

   # see that the step between segments 3 and 4 is less certain than
   #  the other ones:
   segres$confInt
   plot(segres,5, pch=20)

[Package tilingArray version 1.2.0 Index]