# IntervalRegressionRegularized: IntervalRegressionRegularized In penaltyLearning: Penalty Learning

## Description

Repeatedly use `IntervalRegressionInternal` to solve interval regression problems for a path of regularization parameters. This function does not perform automatic selection of the regularization parameter; instead, it returns regression models for a range of regularization parameters, and it is up to you to select which one to use. For automatic regularization parameter selection, use `IntervalRegressionCV`.

## Usage

 ```1 2 3 4 5``` ```IntervalRegressionRegularized(feature.mat, target.mat, initial.regularization = 0.001, factor.regularization = 1.2, verbose = 0, margin = 1, ...) ```

## Arguments

 `feature.mat` Numeric feature matrix. `target.mat` Numeric target matrix. `initial.regularization` Initial regularization parameter. `factor.regularization` Increase regularization by this factor after finding an optimal solution. Or NULL to compute just one model (`initial.regularization`). `verbose` Print messages if >= 1. `margin` Non-negative `margin` size parameter, default 1. `...` Other parameters to pass to `IntervalRegressionInternal`.

## Value

List representing fit model. You can do fit\$predict(feature.matrix) to get a matrix of predicted log penalty values. The param.mat is the n.features * n.regularization numeric matrix of optimal coefficients (on the original scale).

## Author(s)

Toby Dylan Hocking

## Examples

 ```1 2 3 4 5 6 7 8``` ```if(interactive()){ library(penaltyLearning) data("neuroblastomaProcessed", package="penaltyLearning", envir=environment()) i.train <- 1:500 fit <- with(neuroblastomaProcessed, IntervalRegressionRegularized( feature.mat[i.train,], target.mat[i.train,])) plot(fit) } ```

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