segmentation {tilingArray} | R Documentation

## 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.
`negloglik`:
  Numeric vector of the same length as `breakpoints`. The negative 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.

### Author(s)

Wolfgang Huber huber@ebi.ac.uk

### 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)

[Package tilingArray version 1.22.0 Index]