# Regularize Wide Data in Parallel

This example shows how to regularize a model with many more predictors than observations. *Wide data* is data with more predictors than observations. Typically, with wide data you want to identify important predictors. Use `lassoglm`

as an exploratory or screening tool to select a smaller set of variables to prioritize your modeling and research. Use parallel computing to speed up cross validation.

Load the `ovariancancer`

data. This data has 216 observations and 4000 predictors in the `obs`

workspace variable. The responses are binary, either `'Cancer'`

or `'Normal'`

, in the `grp`

workspace variable. Convert the responses to binary for use in `lassoglm`

.

load ovariancancer y = strcmp(grp,'Cancer');

Set options to use parallel computing. Prepare to compute in parallel using `parpool`

.

```
opt = statset('UseParallel',true);
parpool()
```

Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 6). ans = ProcessPool with properties: Connected: true NumWorkers: 6 Cluster: local AttachedFiles: {} AutoAddClientPath: true IdleTimeout: 30 minutes (30 minutes remaining) SpmdEnabled: true

Fit a cross-validated set of regularized models. Use the `Alpha`

parameter to favor retaining groups of highly correlated predictors, as opposed to eliminating all but one member of the group. Commonly, you use a relatively large value of `Alpha`

.

rng('default') % For reproducibility tic [B,S] = lassoglm(obs,y,'binomial','NumLambda',100, ... 'Alpha',0.9,'LambdaRatio',1e-4,'CV',10,'Options',opt); toc

Elapsed time is 90.892114 seconds.

Examine cross-validation plot.

lassoPlot(B,S,'PlotType','CV'); legend('show') % Show legend

Examine trace plot.

lassoPlot(B,S,'PlotType','Lambda','XScale','log')

The right (green) vertical dashed line represents the `Lambda`

providing the smallest cross-validated deviance. The left (blue) dashed line has the minimal deviance plus no more than one standard deviation. This blue line has many fewer predictors:

[S.DF(S.Index1SE) S.DF(S.IndexMinDeviance)]

`ans = `*1×2*
50 89

You asked `lassoglm`

to fit using 100 different `Lambda`

values. How many did it use?

size(B)

`ans = `*1×2*
4000 84

`lassoglm`

stopped after 84 values because the deviance was too small for small `Lambda`

values. To avoid overfitting, `lassoglm`

halts when the deviance of the fitted model is too small compared to the deviance in the binary responses, ignoring the predictor variables.

You can force `lassoglm`

to include more terms by using the `'Lambda'`

name-value pair argument. For example, define a set of `Lambda`

values that additionally includes three values smaller than the values in `S.Lambda`

.

minLambda = min(S.Lambda); explicitLambda = [minLambda*[.1 .01 .001] S.Lambda];

Specify `'Lambda',explicitLambda`

when you call the `lassoglm`

function. `lassoglm`

halts when the deviance of the fitted model is too small, even though you explicitly provide a set of `Lambda`

values.

To save time, you can use:

Fewer

`Lambda`

, meaning fewer fitsFewer cross-validation folds

A larger value for

`LambdaRatio`

Use serial computation and all three of these time-saving methods:

tic [Bquick,Squick] = lassoglm(obs,y,'binomial','NumLambda',25,... 'LambdaRatio',1e-2,'CV',5); toc

Elapsed time is 16.517331 seconds.

Graphically compare the new results to the first results.

lassoPlot(Bquick,Squick,'PlotType','CV'); legend('show') % Show legend

lassoPlot(Bquick,Squick,'PlotType','Lambda','XScale','log')

The number of nonzero coefficients in the lowest plus one standard deviation model is around 50, similar to the first computation.