tilingArray (version 1.50.0)

segmentation: The class segmentation represents a segmentation result.

Description

This class represents the result of a segmentation, usually a call to the function segment.

Objects from the Class

Objects can be created by calls of the function segment or by calls of the form new("segmentation", ...).

Slots

y:

A matrix with the data (the dependent variable(s)), see segment.

x:

A numeric vector with the regressor variable. The length of this vector must be either the same as nrow(y), or 0. The latter case is equivalent to x=1:nrow(y).

flag:

An integer vector, whose length must be either the same as nrow(y), or 0. This can be used to flag certain probes for special treatment, for example by plotAlongChrom.

breakpoints:

List of segmentations. The element breakpoints[[j]] corresponds to a segmentation fit of j segments, i.e. with j-1 breakpoints. It is a matrix with (j-1) rows and 1 or 3 columns. It always contains a column named estimate with the point estimates. Optionally, it may contain columns lower and upper with the confidence intervals. The point estimates are the row indices in y where new segments start, for example: let z=breakpoints[[j]], then the first segment is from row 1 to z[1, "estimate"]-1, the second from row z[1, "estimate"] to z[2, "estimate"]-1, and so on.

logLik:

Numeric vector of the same length as breakpoints, containing the log-likelihood of the piecewise constant models under the data y.

hasConfint:

Logical vector of the same length as breakpoints. TRUE if the confidence interval estimates are present, i.e. if the matrix breakpoints[[j]] has columns lower and upper.

nrSegments:

A scalar integer, value must be either NA or between 1 and length(breakpoints). Can be used to select one of the fits in breakpoints for special treatment, for example by plotAlongChrom.

Methods

confint

The method confint(object, parm, level=0.95, het.reg=FALSE, het.err=FALSE, ...) computes confidence intervals for the change point estimates of the segmentation. Typically, these were obtained from a previous call to the function segment that created the object. This is just a wrapper for the function confint.breakpointsfull from the strucchange package, which does all the hard computations. Parameters: object an object of class segmentation, parm an integer vector, it determines for which of the segmentation fits confidence intervals are computed. See also segment. The other parameters are directly passed on to confint.breakpointsfull.

logLik

The method logLik(object, penalty="none", ...) returns the log-likelihoods of fitted models. Valid values for the argument penalty are none, AIC and BIC.

plot

The method plot(x, y, xlim, xlab="x", ylab="y", bpcol="black", bplty=1, pch=16, ...) provides a simple visualization of the result of a segmentation. Parameters: x an object of class segmentation, y an integer between 1 and length(x@breakpoints), selecting which of the fits contained in x to plot, bpcol and bplty color and line type of breakpoints. The plot shows the numeric data along with breakpoints and if available their confidence intervals.

show

summary.

See Also

segment

Examples

## generate random data with 5 segments:
y = unlist(lapply(c(0,3,0.5,1.5,5), function(m) rnorm(10, mean=m)))

seg = segment(y, maxseg=10, maxk=15)
seg = confint(seg, parm=c(3,4,5))

if(interactive())
  plot(seg, 5)

show(seg)