R: Cost of segmentation models

SegCost {directlabels} | R Documentation

## Cost of segmentation models

### Description

20 segmentation models were fit to 2 simulated signals, and several different error measures were used to quantify the model fit.

### Usage

    data(SegCost)

### Format

A data frame with 560 observations on the following 5 variables.

`bases.per.probe`

  a factor with levels `374` `7`: the sampling density of the signal.

`segments`

  numeric: the model complexity measured using number of segments.

`cost`

  numeric: the cost value.

`type`

  a factor with levels `Signal` `Breakpoint` `Complete` `Incomplete` `Positive`: how to judge model fit? Signal: log mean squared error between latent signal and estimated signal. Breakpoint: exact breakpoint error. Complete: annotation error with a complete set of annotations. Incomplete: annotation error with only half of those annotations. Positive: no negative annotations.

`error`

  a factor with levels `E` `FP` `FN` `I`: what kind of error? FP = False Positive, FN = False Negative, I = Imprecision, E = Error (sum of the other terms).

### Source

PhD thesis of Toby Dylan Hocking, chapter Optimal penalties for breakpoint detection using segmentation model selection.

[Package directlabels version 2018.05.22 Index]