# IntervalRegressionCVmargin: IntervalRegressionCVmargin In penaltyLearning: Penalty Learning

## Description

Use cross-validation to fit an L1-regularized linear interval regression model by optimizing both margin and regularization parameters. This function just calls `IntervalRegressionCV` with a margin.vec parameter that is computed based on the finite target interval limits. If default parameters are used, this function should be about 10 times slower than `IntervalRegressionCV` (since this function computes n.margin=10 models per regularization parameter whereas `IntervalRegressionCV` only computes one). On large (N > 1000 rows) data sets, this function should yield a model which is a little more accurate than `IntervalRegressionCV` (since the margin parameter is optimized).

## Usage

 ```1 2 3``` ```IntervalRegressionCVmargin(feature.mat, target.mat, log10.diff = 2, n.margin = 10L, ...) ```

## Arguments

 `feature.mat` Numeric feature matrix, n observations x p features. `target.mat` Numeric target matrix, n observations x 2 limits. `log10.diff` Numeric scalar: factors of 10 below the largest finite limit difference to use as a minimum margin value (difference on the log10 scale which is used to generate margin parameters). Bigger values mean a grid of margin parameters with a larger range. For example if the largest finite limit in `target.mat` is 26 and the smallest finite limit is -4 then the largest limit difference is 30, which will be used as the maximum margin parameter. If `log10.diff` is the default of 2 then that means the smallest margin parameter will be 0.3 (two factors of 10 smaller than 30). `n.margin` Integer scalar: number of margin parameters, by default 10. `...` Passed to `IntervalRegressionCV`.

## Value

Model fit list from `IntervalRegressionCV`.

## Author(s)

Toby Dylan Hocking

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```if(interactive()){ library(penaltyLearning) data( "neuroblastomaProcessed", package="penaltyLearning", envir=environment()) if(require(future)){ plan(multiprocess) } set.seed(1) fit <- with(neuroblastomaProcessed, IntervalRegressionCVmargin( feature.mat, target.mat, verbose=1)) plot(fit) print(fit\$plot.heatmap) } ```

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