targetIntervals: Compute target intervals

Description Usage Arguments Value Author(s) Examples

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

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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

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library(penaltyLearning)
data(neuroblastomaProcessed, envir=environment())
targets.dt <- targetIntervals(
  neuroblastomaProcessed$errors,
  problem.vars=c("profile.id", "chromosome"))

penaltyLearning documentation built on July 1, 2020, 10:26 p.m.