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 2021.1.13

Index]