SegCost

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

datasets

An extensible framework
for automatically placing direct labels onto multicolor 'lattice' or
'ggplot2' plots.
Label positions are described using Positioning Methods
which can be re-used across several different plots.
There are heuristics for examining "trellis" and "ggplot" objects
and inferring an appropriate Positioning Method.

Toby Hocking

directlabels

Direct Labels for Multicolor Plots

SegCost function

A data frame with 560 observations on the following 5 variables.<dl class="dl-horizontal">
 <dt><code>bases.per.probe</code></dt><dd>a factor with levels <code>374</code>
 <code>7</code>: the sampling density of the signal.</dd>
 <dt><code>segments</code></dt><dd>numeric: the model complexity
 measured using number of segments.</dd>
 <dt><code>cost</code></dt><dd>numeric: the cost value.</dd>
 <dt><code>type</code></dt><dd>a factor with levels <code>Signal</code>
 <code>Breakpoint</code> <code>Complete</code> <code>Incomplete</code>
 <code>Positive</code>: 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.</dd>
 <dt><code>error</code></dt><dd>a factor with levels <code>E</code> <code>FP</code>
 <code>FN</code> <code>I</code>: what kind of error? FP = False
 Positive, FN = False Negative, I = Imprecision, E = Error
 (sum of the other terms).</dd>
 </dl>

Format

Cost of segmentation models — SegCost

A data frame with 560 observations on the following 5 variables.<dl class='dl-horizontal'>
 <dt><code>bases.per.probe</code></dt><dd>a factor with levels <code>374</code>
 <code>7</code>: the sampling density of the signal.</dd>
 <dt><code>segments</code></dt><dd>numeric: the model complexity
 measured using number of segments.</dd>
 <dt><code>cost</code></dt><dd>numeric: the cost value.</dd>
 <dt><code>type</code></dt><dd>a factor with levels <code>Signal</code>
 <code>Breakpoint</code> <code>Complete</code> <code>Incomplete</code>
 <code>Positive</code>: 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.</dd>
 <dt><code>error</code></dt><dd>a factor with levels <code>E</code> <code>FP</code>
 <code>FN</code> <code>I</code>: what kind of error? FP = False
 Positive, FN = False Negative, I = Imprecision, E = Error
 (sum of the other terms).</dd>
 </dl>

SegCost: Cost of segmentation models

Description

Usage

Arguments

Format