confint.segmentation {tilingArray} | R Documentation |
Compute Bai's confidence intervals for specified segmentations
## S3 method for class 'segmentation': confint(object,parm="breakpoints", level = 0.95, nSegments=NULL, het.reg = FALSE,het.err = FALSE, ...)
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 |
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
.
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 |
Joern Toedling toedling@ebi.ac.uk
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.
computeConfInt
,findSegments
,
plot.segmentation
,confint
# 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)