# lassoglm

Lasso or elastic net regularization for generalized linear models

## Syntax

``B = lassoglm(X,y)``
``B = lassoglm(X,y,distr)``
``B = lassoglm(X,y,distr,Name,Value)``
``````[B,FitInfo] = lassoglm(___)``````

## Description

example

````B = lassoglm(X,y)` returns penalized, maximum-likelihood fitted coefficients for generalized linear models of the predictor data `X` and the response `y`, where the values in `y` are assumed to have a normal probability distribution. Each column of `B` corresponds to a particular regularization coefficient in `Lambda`. By default, `lassoglm` performs lasso regularization using a geometric sequence of `Lambda` values.```
````B = lassoglm(X,y,distr)` performs lasso regularization to fit the models using the probability distribution `distr` for `y`.```
````B = lassoglm(X,y,distr,Name,Value)` fits regularized generalized linear regressions with additional options specified by one or more name-value pair arguments. For example, `'Alpha',0.5` sets elastic net as the regularization method, with the parameter `Alpha` equal to 0.5.```

example

``````[B,FitInfo] = lassoglm(___)``` also returns the structure `FitInfo`, which contains information about the fit of the models, using any of the input arguments in the previous syntaxes.```

## Examples

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Construct a data set with redundant predictors and identify those predictors by using `lassoglm`.

Create a random matrix `X` with 100 observations and 10 predictors. Create the normally distributed response `y` using only four of the predictors and a small amount of noise.

```rng default X = randn(100,10); weights = [0.6;0.5;0.7;0.4]; y = X(:,[2 4 5 7])*weights + randn(100,1)*0.1; % Small added noise```

Perform lasso regularization.

`B = lassoglm(X,y);`

Find the coefficient vector for the 75th `Lambda` value in `B`.

`B(:,75)`
```ans = 10×1 0 0.5431 0 0.3944 0.6173 0 0.3473 0 0 0 ```

`lassoglm` identifies and removes the redundant predictors.

Construct data from a Poisson model, and identify the important predictors by using `lassoglm`.

Create data with 20 predictors. Create a Poisson response variable using only three of the predictors plus a constant.

```rng default % For reproducibility X = randn(100,20); weights = [.4;.2;.3]; mu = exp(X(:,[5 10 15])*weights + 1); y = poissrnd(mu);```

Construct a cross-validated lasso regularization of a Poisson regression model of the data.

`[B,FitInfo] = lassoglm(X,y,'poisson','CV',10);`

Examine the cross-validation plot to see the effect of the `Lambda` regularization parameter.

```lassoPlot(B,FitInfo,'plottype','CV'); legend('show') % Show legend``` The green circle and dotted line locate the `Lambda` with minimum cross-validation error. The blue circle and dotted line locate the point with minimum cross-validation error plus one standard deviation.

Find the nonzero model coefficients corresponding to the two identified points.

```idxLambdaMinDeviance = FitInfo.IndexMinDeviance; mincoefs = find(B(:,idxLambdaMinDeviance))```
```mincoefs = 7×1 3 5 6 10 11 15 16 ```
```idxLambda1SE = FitInfo.Index1SE; min1coefs = find(B(:,idxLambda1SE))```
```min1coefs = 3×1 5 10 15 ```

The coefficients from the minimum-plus-one standard error point are exactly those coefficients used to create the data.

Predict whether students got a B or above on their last exam by using `lassoglm`.

Load the `examgrades` data set. Convert the last exam grades to a logical vector, where `1` represents a grade of 80 or above and `0` represents a grade below 80.

```load examgrades X = grades(:,1:4); y = grades(:,5); yBinom = (y>=80);```

Partition the data into training and test sets.

```rng default % Set the seed for reproducibility c = cvpartition(yBinom,'HoldOut',0.3); idxTrain = training(c,1); idxTest = ~idxTrain; XTrain = X(idxTrain,:); yTrain = yBinom(idxTrain); XTest = X(idxTest,:); yTest = yBinom(idxTest);```

Perform lasso regularization for generalized linear model regression with 3-fold cross-validation on the training data. Assume the values in `y` are binomially distributed. Choose model coefficients corresponding to the `Lambda` with minimum expected deviance.

```[B,FitInfo] = lassoglm(XTrain,yTrain,'binomial','CV',3); idxLambdaMinDeviance = FitInfo.IndexMinDeviance; B0 = FitInfo.Intercept(idxLambdaMinDeviance); coef = [B0; B(:,idxLambdaMinDeviance)]```
```coef = 5×1 -21.1911 0.0235 0.0670 0.0693 0.0949 ```

Predict exam grades for the test data using the model coefficients found in the previous step. Specify the link function for a binomial response using `'logit'`. Convert the prediction values to a logical vector.

```yhat = glmval(coef,XTest,'logit'); yhatBinom = (yhat>=0.5);```

Determine the accuracy of the predictions using a confusion matrix.

`c = confusionchart(yTest,yhatBinom);` The function correctly predicts 31 exam grades. However, the function incorrectly predicts that 1 student receives a B or above and `4` students receive a grade below a B.

## Input Arguments

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Predictor data, specified as a numeric matrix. Each row represents one observation, and each column represents one predictor variable.

Data Types: `single` | `double`

Response data, specified as a numeric vector, logical vector, categorical array, or two-column numeric matrix.

• When `distr` is not `'binomial'`, `y` is a numeric vector or categorical array of length n, where n is the number of rows in `X`. The response `y(i)` corresponds to row i in `X`.

• When `distr` is `'binomial'`, `y` is one of the following:

• Numeric vector of length n, where each entry represents success (`1`) or failure (`0`)

• Logical vector of length n, where each entry represents success or failure

• Categorical array of length n, where each entry represents success or failure

• Two-column numeric matrix, where the first column contains the number of successes for each observation and the second column contains the total number of trials

Data Types: `single` | `double` | `logical` | `categorical`

Distribution of response data, specified as one of the following:

• `'normal'`

• `'binomial'`

• `'poisson'`

• `'gamma'`

• `'inverse gaussian'`

`lassoglm` uses the default link function corresponding to `distr`. Specify another link function using the `Link` name-value pair argument.

### Name-Value Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

Example: `lassoglm(X,y,'poisson','Alpha',0.5)` performs elastic net regularization assuming that the response values are Poisson distributed. The `'Alpha',0.5` name-value pair argument sets the parameter used in the elastic net optimization.

Weight of lasso (L1) versus ridge (L2) optimization, specified as the comma-separated pair consisting of `'Alpha'` and a positive scalar value in the interval `(0,1]`. The value `Alpha = 1` represents lasso regression, `Alpha` close to `0` approaches ridge regression, and other values represent elastic net optimization. See Elastic Net.

Example: `'Alpha',0.75`

Data Types: `single` | `double`

Cross-validation specification for estimating the deviance, specified as the comma-separated pair consisting of `'CV'` and one of the following:

• `'resubstitution'``lassoglm` uses `X` and `y` to fit the model and to estimate the deviance without cross-validation.

• Positive scalar integer `K``lassoglm` uses `K`-fold cross-validation.

• `cvpartition` object `cvp``lassoglm` uses the cross-validation method expressed in `cvp`. You cannot use a `'leaveout'` partition with `lassoglm`.

Example: `'CV',10`

Maximum number of nonzero coefficients in the model, specified as the comma-separated pair consisting of `'DFmax'` and a positive integer scalar. `lassoglm` returns results only for `Lambda` values that satisfy this criterion.

Example: `'DFmax',25`

Data Types: `single` | `double`

Regularization coefficients, specified as the comma-separated pair consisting of `'Lambda'` and a vector of nonnegative values. See Lasso.

• If you do not supply `Lambda`, then `lassoglm` estimates the largest value of `Lambda` that gives a nonnull model. In this case, `LambdaRatio` gives the ratio of the smallest to the largest value of the sequence, and `NumLambda` gives the length of the vector.

• If you supply `Lambda`, then `lassoglm` ignores `LambdaRatio` and `NumLambda`.

• If `Standardize` is `true`, then `Lambda` is the set of values used to fit the models with the `X` data standardized to have zero mean and a variance of one.

The default is a geometric sequence of `NumLambda` values, with only the largest value able to produce `B` = `0`.

Data Types: `single` | `double`

Ratio of the smallest to the largest `Lambda` values when you do not supply `Lambda`, specified as the comma-separated pair consisting of `'LambdaRatio'` and a positive scalar.

If you set `LambdaRatio` = 0, then `lassoglm` generates a default sequence of `Lambda` values and replaces the smallest one with `0`.

Example: `'LambdaRatio',1e–2`

Data Types: `single` | `double`

Mapping between the mean µ of the response and the linear predictor Xb, specified as the comma-separated pair consisting of `'Link'` and one of the values in this table.

ValueDescription
`'comploglog'`

log(–log((1 – µ))) = Xb

`'identity'`, default for the distribution `'normal'`

µ = Xb

`'log'`, default for the distribution `'poisson'`

log(µ) = Xb

`'logit'`, default for the distribution `'binomial'`

log(µ/(1 – µ)) = Xb

`'loglog'`

log(–log(µ)) = Xb

`'probit'`

Φ–1(µ) = Xb, where Φ is the normal (Gaussian) cumulative distribution function

`'reciprocal'`, default for the distribution `'gamma'`

µ–1 = Xb

`p` (a number), default for the distribution ```'inverse gaussian'``` (with p = –2)

µp = Xb

A cell array of the form `{FL FD FI}`, containing three function handles created using `@`, which define the link (`FL`), the derivative of the link (`FD`), and the inverse link (`FI`). Or, a structure of function handles with the field `Link` containing `FL`, the field `Derivative` containing `FD`, and the field `Inverse` containing `FI`.

Example: `'Link','probit'`

Data Types: `char` | `string` | `single` | `double` | `cell`

Maximum number of iterations allowed, specified as the comma-separated pair consisting of `'MaxIter'` and a positive integer scalar.

If the algorithm executes `MaxIter` iterations before reaching the convergence tolerance `RelTol`, then the function stops iterating and returns a warning message.

The function can return more than one warning when `NumLambda` is greater than `1`.

Example: `'MaxIter',1e3`

Data Types: `single` | `double`

Number of Monte Carlo repetitions for cross-validation, specified as the comma-separated pair consisting of `'MCReps'` and a positive integer scalar.

• If `CV` is `'resubstitution'` or a `cvpartition` of type `'resubstitution'`, then `MCReps` must be `1`.

• If `CV` is a `cvpartition` of type `'holdout'`, then `MCReps` must be greater than `1`.

Example: `'MCReps',2`

Data Types: `single` | `double`

Number of `Lambda` values `lassoglm` uses when you do not supply `Lambda`, specified as the comma-separated pair consisting of `'NumLambda'` and a positive integer scalar. `lassoglm` can return fewer than `NumLambda` fits if the deviance of the fits drops below a threshold fraction of the null deviance (deviance of the fit without any predictors `X`).

Example: `'NumLambda',150`

Data Types: `single` | `double`

Additional predictor variable, specified as the comma-separated pair consisting of `'Offset'` and a numeric vector with the same number of rows as `X`. The `lassoglm` function keeps the coefficient value of `Offset` fixed at `1.0`.

Data Types: `single` | `double`

Option to cross-validate in parallel and specify the random streams, specified as the comma-separated pair consisting of `'Options'` and a structure. This option requires Parallel Computing Toolbox™.

Create the `Options` structure with `statset`. The option fields are:

• `UseParallel` — Set to `true` to compute in parallel. The default is `false`.

• `UseSubstreams` — Set to `true` to compute in parallel in a reproducible fashion. For reproducibility, set `Streams` to a type allowing substreams: `'mlfg6331_64'` or `'mrg32k3a'`. The default is `false`.

• `Streams` — A `RandStream` object or cell array consisting of one such object. If you do not specify `Streams`, then `lassoglm` uses the default stream.

Example: `'Options',statset('UseParallel',true)`

Data Types: `struct`

Names of the predictor variables, in the order in which they appear in `X`, specified as the comma-separated pair consisting of `'PredictorNames'` and a string array or cell array of character vectors.

Example: `'PredictorNames',{'Height','Weight','Age'}`

Data Types: `string` | `cell`

Convergence threshold for the coordinate descent algorithm , specified as the comma-separated pair consisting of `'RelTol'` and a positive scalar. The algorithm terminates when successive estimates of the coefficient vector differ in the L2 norm by a relative amount less than `RelTol`.

Example: `'RelTol',2e–3`

Data Types: `single` | `double`

Flag for standardizing the predictor data `X` before fitting the models, specified as the comma-separated pair consisting of `'Standardize'` and either `true` or `false`. If `Standardize` is `true`, then the `X` data is scaled to have zero mean and a variance of one. `Standardize` affects whether the regularization is applied to the coefficients on the standardized scale or the original scale. The results are always presented on the original data scale.

Example: `'Standardize',false`

Data Types: `logical`

Observation weights, specified as the comma-separated pair consisting of `'Weights'` and a nonnegative vector. `Weights` has length n, where n is the number of rows of `X`. At least two values must be positive.

Data Types: `single` | `double`

## Output Arguments

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Fitted coefficients, returned as a numeric matrix. `B` is a p-by-L matrix, where p is the number of predictors (columns) in `X`, and L is the number of `Lambda` values. You can specify the number of `Lambda` values using the `NumLambda` name-value pair argument.

The coefficient corresponding to the intercept term is a field in `FitInfo`.

Data Types: `single` | `double`

Fit information of the generalized linear models, returned as a structure with the fields described in this table.

Field in `FitInfo`Description
`Intercept`Intercept term β0 for each linear model, a `1`-by-L vector
`Lambda`Lambda parameters in ascending order, a `1`-by-L vector
`Alpha`Value of the `Alpha` parameter, a scalar
`DF`Number of nonzero coefficients in `B` for each value of `Lambda`, a `1`-by-L vector
`Deviance`

Deviance of the fitted model for each value of `Lambda`, a `1`-by-L vector

If the model is cross-validated, then the values for `Deviance` represent the estimated expected deviance of the model applied to new data, as calculated by cross-validation. Otherwise, `Deviance` is the deviance of the fitted model applied to the data used to perform the fit.

`PredictorNames`Value of the `PredictorNames` parameter, stored as a cell array of character vectors

If you set the `CV` name-value pair argument to cross-validate, the `FitInfo` structure contains these additional fields.

Field in `FitInfo`Description
`SE`Standard error of `Deviance` for each `Lambda`, as calculated during cross-validation, a `1`-by-L vector
`LambdaMinDeviance``Lambda` value with minimum expected deviance, as calculated by cross-validation, a scalar
`Lambda1SE`Largest `Lambda` value such that `Deviance` is within one standard error of the minimum, a scalar
`IndexMinDeviance`Index of `Lambda` with the value `LambdaMinDeviance`, a scalar
`Index1SE`Index of `Lambda` with the value `Lambda1SE`, a scalar

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A link function f(μ) maps a distribution with mean μ to a linear model with data X and coefficient vector b using the formula

f(μ) = Xb.

You can find the formulas for the link functions in the `Link` name-value pair argument description. This table lists the link functions that are typically used for each distribution.

`'normal'``'identity'`
`'binomial'``'logit'``'comploglog'`, `'loglog'`, `'probit'`
`'poisson'``'log'`
`'gamma'``'reciprocal'`
`'inverse gaussian'``–2`

### Lasso

For a nonnegative value of λ, `lassoglm` solves the problem

`$\underset{{\beta }_{0},\beta }{\mathrm{min}}\left(\frac{1}{N}\text{Deviance}\left({\beta }_{0},\beta \right)+\lambda \sum _{j=1}^{p}|{\beta }_{j}|\right).$`
• The function Deviance in this equation is the deviance of the model fit to the responses using the intercept β0 and the predictor coefficients β. The formula for Deviance depends on the `distr` parameter you supply to `lassoglm`. Minimizing the λ-penalized deviance is equivalent to maximizing the λ-penalized loglikelihood.

• N is the number of observations.

• λ is a nonnegative regularization parameter corresponding to one value of `Lambda`.

• The parameters β0 and β are a scalar and a vector of length p, respectively.

As λ increases, the number of nonzero components of β decreases.

The lasso problem involves the L1 norm of β, as contrasted with the elastic net algorithm.

### Elastic Net

For α strictly between 0 and 1, and nonnegative λ, elastic net solves the problem

`$\underset{{\beta }_{0},\beta }{\mathrm{min}}\left(\frac{1}{N}\text{Deviance}\left({\beta }_{0},\beta \right)+\lambda {P}_{\alpha }\left(\beta \right)\right),$`

where

`${P}_{\alpha }\left(\beta \right)=\frac{\left(1-\alpha \right)}{2}{‖\beta ‖}_{2}^{2}+\alpha {‖\beta ‖}_{1}=\sum _{j=1}^{p}\left(\frac{\left(1-\alpha \right)}{2}{\beta }_{j}^{2}+\alpha |{\beta }_{j}|\right).$`

Elastic net is the same as lasso when α = 1. For other values of α, the penalty term Pα(β) interpolates between the L1 norm of β and the squared L2 norm of β. As α shrinks toward 0, elastic net approaches `ridge` regression.

 Tibshirani, R. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B, Vol. 58, No. 1, 1996, pp. 267–288.

 Zou, H., and T. Hastie. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society. Series B, Vol. 67, No. 2, 2005, pp. 301–320.

 Friedman, J., R. Tibshirani, and T. Hastie. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software. Vol. 33, No. 1, 2010. `https://www.jstatsoft.org/v33/i01`

 Hastie, T., R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. 2nd edition. New York: Springer, 2008.

 Dobson, A. J. An Introduction to Generalized Linear Models. 2nd edition. New York: Chapman & Hall/CRC Press, 2002.

 McCullagh, P., and J. A. Nelder. Generalized Linear Models. 2nd edition. New York: Chapman & Hall/CRC Press, 1989.

 Collett, D. Modelling Binary Data. 2nd edition. New York: Chapman & Hall/CRC Press, 2003.