SegCost: Cost of segmentation models
In directlabels: Direct Labels for Multicolor Plots
SegCost 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.probea factor with levels 374
7: the sampling density of the signal.
segmentsnumeric: the model complexity
measured using number of segments.
costnumeric: the cost value.
typea 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.
errora 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.
directlabels documentation built on May 29, 2024, 6:59 a.m.
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| SegCost | 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.probea factor with levels
3747: the sampling density of the signal.segmentsnumeric: the model complexity measured using number of segments.
costnumeric: the cost value.
typea factor with levels
SignalBreakpointCompleteIncompletePositive: 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.errora factor with levels
EFPFNI: 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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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