computeConfInt {tilingArray} | R Documentation |
Computes confidence intervals for breakpoints.
Does not need to be called by the tilingArray
user.
computeConfInt( object, parm = NULL, level = 0.95, breaks = NULL, het.reg = FALSE, het.err = TRUE, ...)
object |
a list containing the data, formula, breakpoints, and residuals |
parm |
the same as breaks , only one of the two should be
specified. |
level |
the confidence level required. |
breaks |
an integer specifying the number of breaks to be used. By default the breaks of the minimum BIC partition are used. |
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. |
As the breakpoints are integers (observation numbers) the corresponding confidence intervals are also rounded to integers.
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
.
A matrix containing the breakpoints and their lower and upper confidence boundary for the given level.
Achim Zeileis Achim.Zeileis@R-project.org, with minor contributions from Joern Toedling toedling@ebi.ac.uk and Wolfgang Huber huber@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.
findSegments
,
confint.breakpointsfull
### see 'plot.segmentation' for an example with plot