R: Class "cpt.range"

cpt.range-class {changepoint} | R Documentation

## Class "cpt.range"

### Description

A class for changepoint objects that return more than 1 segmentation. Inherits from cpt class.

### Objects from the Class

Objects can be created by calls of the form `new("cpt.range", ...)`.

`new("cpt.range", ...)`:

  creates a new object with class cpt.range

### Slots

`cpts.full`:

  Object of class `"matrix"`, each row of the matrix is a different segmentation of the data (different set of changepoints).

`pen.value.full`:

  Object of class `"vector"`, each element is the penalty used to create the set of changepoints in the corresponding row of `cpts.full`.

The remaining slots are inherited from the `cpt` class.

`data.set`:

  Object of class `"ts"`, a coerced time series of the original data. Inherited from cpt class.

`cpttype`:

  Object of class `"character"`, the type of changepoint that was identified. Inherited from cpt class.

`method`:

  Object of class `"character"`, the method that was used to search for changepoints. Inherited from cpt class.

`test.stat`:

  Object of class `"character"`, the test statistic for the analysis of the data. Inherited from cpt class.

`pen.type`:

  Object of class `"character"`, the penalty type specified in the analysis. Inherited from cpt class.

`pen.value`:

  Object of class `"numeric"`, the value of the penalty used in the analysis. Inherited from cpt class.

`minseglen`:

  Object of class `"numeric"`, the minimum segment length (no. of observations between changepoints) used in the analysis. Inherited from cpt class.

`cpts`:

  Object of class `"numeric"`, vector of optimal changepoints identified. Inherited from cpt class.

`ncpts.max`:

  Object of class `"numeric"`, maximum number of changepoint that can be identified. Inherited from cpt class.

`param.est`:

  Object of class `"list"`, list where each element is a vector of parameter estimates, if requested. Inherited from cpt class.

`date`:

  Object of class `"character"`, date and time the changepoint analysis was run. Inherited from cpt class.

`version`:

  Object of class `"character"`, version number of the package used when the analysis was run. Inherited from cpt class.

### Methods

cpts.full

  `signature(object = "cpt.range")`: retrieves cpts.full slot

pen.value.full

  `signature(object = "cpt.range")`: retrieves pen.value.full slot

cpts.full<-

  `signature(object = "cpt.range")`: replaces cpts.full slot

param

  `signature(object="cpt.range",ncpts=NA)`: creates parameter estimates for the segmentation with `ncpts` number of changepoints. If ncpts=NA then the optimal set of changepoints according to the set penalty is used.

pen.value.full<-

  `signature(object = "cpt.range")`: replaces pen.value.full slot

plot

  `signature(object="cpt.range",ncpts=NA,diagnostic=FALSE)`: by default plots the optimal segmentation as for `class="cpt"`. If ncpts is specified then plots the segmentation for `ncpts` number of changepoints. If `diagnostic=TRUE` then produces a diagnostic plot to aide selection of the number of changes.

print

  `signature(object = "cpt.range")`: prints details of the cpt.range object including summary

summary

  `signature(object = "cpt.range")`: prints a summary of the cpt.range object

### Author(s)

Rebecca Killick

### See Also

`cpts.full-methods`,`cpt`,`cpt.mean`,`cpt.var`,`cpt.meanvar`

### Examples

    showClass("cpt.range") # shows the structure of the cpt.range class

    x=new("cpt.range") # creates a new object with the cpt.range class defaults
    cpts(x) # retrieves the cpts slot from x
    cpts(x)<-c(10,50,100) # replaces the cpts slot from x with c(10,50,100)

    # Example of multiple changes in variance at 50,100,150 in simulated normal data
    set.seed(1)
    x=c(rnorm(50,0,1),rnorm(50,0,10),rnorm(50,0,5),rnorm(50,0,1))
    out=cpt.var(x,pen.value=c(log(length(x)),10*log(length(x))),penalty="CROPS",method="PELT")
    print(out) # prints details of the analysis including a summary
    summary(out)
    plot(out,diagnostic=TRUE) # a diagnostic plot to identify number of changepoints
    # looks like the segmentation with 3 changepoints, 50,99,150 is the most appropriate
    plot(out,ncpts=3) # plots the segmentation for 3 changes
    logLik(out,ncpts=3)
    # raw likelihood of the data with changepoints, second value is likelihood + penalty

[Package changepoint version 2.2.2 Index]