targetIntervals {penaltyLearning} | R Documentation |
Compute target intervals
Description
Compute target intervals of log(penalty) values that result in
predicted changepoint models
with minimum incorrect labels.
Use this function after labelError
, and before IntervalRegression*.
Usage
targetIntervals(models,
problem.vars)
Arguments
models |
data.table with columns errors, min.log.lambda, max.log.lambda, typically labelError()$model.errors. |
problem.vars |
character: column names used to identify data set / segmentation problem. |
Value
data.table with columns problem.vars
, one row for each
segmentation problem. The "min.log.lambda", and "max.log.lambda"
columns give the largest interval of log(penalty) values which
results in the minimum incorrect labels for that problem. This can
be used to create the target.mat parameter of the
IntervalRegression* functions.
Author(s)
Toby Dylan Hocking
Examples
data.table::setDTthreads(1)
library(penaltyLearning)
data(neuroblastomaProcessed, envir=environment())
targets.dt <- targetIntervals(
neuroblastomaProcessed$errors,
problem.vars=c("profile.id", "chromosome"))
[Package penaltyLearning version 2024.1.25 Index]