modelSelection: Compute exact model selection function

In penaltyLearning: Penalty Learning

Compute exact model selection function

Description

Given loss.vec L_i, model.complexity K_i, the model selection function i*(lambda) = argmin_i L_i + lambda*K_i, compute all of the solutions (i, min.lambda, max.lambda) with i being the solution for every lambda in (min.lambda, max.lambda). Use this function after having computed changepoints and loss values for each model, and before using labelError. This function uses the linear time algorithm implemented in C code (modelSelectionC).

Usage

modelSelection(models, 
    loss = "loss", complexity = "complexity")

Arguments

models	data.frame with one row per model. There must be at least two columns models[[loss]] and models[[complexity]], but there can also be other meta-data columns.
loss	character: column name of models to interpret as loss L_i.
complexity	character: column name of models to interpret as complexity K_i.

Value

data.frame with a row for each model that can be selected for at least one lambda value, and the following columns. (min.lambda, max.lambda) and (min.log.lambda, max.log.lambda) are intervals of optimal penalty constants, on the original and log scale; the other columns (and rownames) are taken from models. This should be used as the models argument of labelError.

Author(s)

Toby Dylan Hocking

penaltyLearning documentation built on Sept. 8, 2023, 5:47 p.m.