An Introduction to glmnet

Introduction

Glmnet is a package that fits generalized linear and similar models via penalized maximum likelihood. The regularization path is computed for the lasso or elastic net penalty at a grid of values (on the log scale) for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix x. It fits linear, logistic and multinomial, poisson, and Cox regression models. It can also fit multi-response linear regression, generalized linear models for custom families, and relaxed lasso regression models. The package includes methods for prediction and plotting, and functions for cross-validation.

The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Kenneth Tay and Noah Simon, with contribution from Junyang Qian, and the R package is maintained by Trevor Hastie. A MATLAB version of glmnet is maintained by Junyang Qian, and a Python version by B. Balakumar (although both are a few versions behind).

This vignette describes basic usage of glmnet in R. There are additional vignettes that should be useful:

• “Regularized Cox Regression” describes how to fit regularized Cox models for survival data with glmnet.
• “GLM family functions in glmnet” describes how to fit custom generalized linear models (GLMs) with the elastic net penalty via the family argument.
• “The Relaxed Lasso” describes how to fit relaxed lasso regression models using the relax argument.

glmnet solves the problem

$$ \min_{\beta_0,\beta} \frac{1}{N} \sum_{i=1}^{N} w_i l(y_i,\beta_0+\beta^T x_i) + \lambda\left[(1-\alpha)\|\beta\|_2^2/2 + \alpha \|\beta\|_1\right], $$

over a grid of values of λ covering the entire range of possible solutions. Here l(y_i, η_i) is the negative log-likelihood contribution for observation i; e.g. for the Gaussian case it is $\frac{1}{2}(y_i-\eta_i)^2$. The elastic net penalty is controlled by α, and bridges the gap between lasso regression (α = 1, the default) and ridge regression (α = 0). The tuning parameter λ controls the overall strength of the penalty.

It is known that the ridge penalty shrinks the coefficients of correlated predictors towards each other while the lasso tends to pick one of them and discard the others. The elastic net penalty mixes these two: if predictors are correlated in groups, an α = 0.5 tends to either select or leave out the entire group of features. This is a higher level parameter, and users might pick a value upfront or experiment with a few different values. One use of α is for numerical stability; for example, the elastic net with α = 1 − ϵ for some small ϵ > 0 performs much like the lasso, but removes any degeneracies and wild behavior caused by extreme correlations.

The glmnet algorithms use cyclical coordinate descent, which successively optimizes the objective function over each parameter with others fixed, and cycles repeatedly until convergence. The package also makes use of the strong rules for efficient restriction of the active set. Due to highly efficient updates and techniques such as warm starts and active-set convergence, our algorithms can compute the solution path very quickly.

The code can handle sparse input-matrix formats, as well as range constraints on coefficients. The core of glmnet is a set of Fortran subroutines, which make for very fast execution.

The theory and algorithms in this implementation are described in Friedman, Hastie, and Tibshirani (2010), Simon et al. (2011), Tibshirani et al. (2012) and Simon, Friedman, and Hastie (2013).

Installation

Like many other R packages, the simplest way to obtain glmnet is to install it directly from CRAN. Type the following command in R console:

install.packages("glmnet", repos = "https://cran.us.r-project.org")

Users may change the repos argument depending on their locations and preferences. Other arguments such as the directories to install the packages at can be altered in the command. For more details, see help(install.packages). Alternatively, users can download the package source from CRAN and type Unix commands to install it to the desired location.

Quick Start

The purpose of this section is to give users a general sense of the package. We will briefly go over the main functions, basic operations and outputs. After this section, users may have a better idea of what functions are available, which ones to use, or at least where to seek help.

First, we load the glmnet package:

library(glmnet)

The default model used in the package is the Guassian linear model or “least squares”, which we will demonstrate in this section. We load a set of data created beforehand for illustration:

data(QuickStartExample)
x <- QuickStartExample$x
y <- QuickStartExample$y

The command loads an input matrix x and a response vector y from this saved R data archive.

We fit the model using the most basic call to glmnet.

fit <- glmnet(x, y)

fit is an object of class glmnet that contains all the relevant information of the fitted model for further use. We do not encourage users to extract the components directly. Instead, various methods are provided for the object such as plot, print, coef and predict that enable us to execute those tasks more elegantly.

We can visualize the coefficients by executing the plot method:

plot(fit)

Each curve corresponds to a variable. It shows the path of its coefficient against the ℓ₁-norm of the whole coefficient vector as λ varies. The axis above indicates the number of nonzero coefficients at the current λ, which is the effective degrees of freedom (df) for the lasso. Users may also wish to annotate the curves: this can be done by setting label = TRUE in the plot command.

A summary of the glmnet path at each step is displayed if we just enter the object name or use the print function:

print(fit)

## 
## Call:  glmnet(x = x, y = y) 
## 
##    Df  %Dev  Lambda
## 1   0  0.00 1.63100
## 2   2  5.53 1.48600
## 3   2 14.59 1.35400
## 4   2 22.11 1.23400
## 5   2 28.36 1.12400
## 6   2 33.54 1.02400
....

It shows from left to right the number of nonzero coefficients (Df), the percent (of null) deviance explained (%dev) and the value of λ (Lambda). Although glmnet fits the model for 100 values of lambda by default, it stops early if %dev does not change sufficently from one lambda to the next (typically near the end of the path.) Here we have truncated the prinout for brevity.

We can obtain the model coefficients at one or more λ’s within the range of the sequence:

coef(fit, s = 0.1)

## 21 x 1 sparse Matrix of class "dgCMatrix"
##                       s1
## (Intercept)  0.150928072
## V1           1.320597195
## V2           .          
## V3           0.675110234
## V4           .          
## V5          -0.817411518
## V6           0.521436671
## V7           0.004829335
....

(Why s and not lambda? In case we want to allow one to specify the model size in other ways in the future.) Users can also make predictions at specific λ’s with new input data:

set.seed(29)
nx <- matrix(rnorm(5 * 20), 5, 20)
predict(fit, newx = nx, s = c(0.1, 0.05))

##              s1         s2
## [1,] -4.3067990 -4.5979456
## [2,] -4.1244091 -4.3447727
## [3,] -0.1133939 -0.1859237
## [4,]  3.3458748  3.5270269
## [5,] -1.2366422 -1.2772955

The function glmnet returns a sequence of models for the users to choose from. In many cases, users may prefer the software to select one of them. Cross-validation is perhaps the simplest and most widely used method for that task. cv.glmnet is the main function to do cross-validation here, along with various supporting methods such as plotting and prediction.

cvfit <- cv.glmnet(x, y)

cv.glmnet returns a cv.glmnet object, a list with all the ingredients of the cross-validated fit. As with glmnet, we do not encourage users to extract the components directly except for viewing the selected values of λ. The package provides well-designed functions for potential tasks. For example, we can plot the object:

plot(cvfit)

This plots the cross-validation curve (red dotted line) along with upper and lower standard deviation curves along the λ sequence (error bars). Two special values along the λ sequence are indicated by the vertical dotted lines. lambda.min is the value of λ that gives minimum mean cross-validated error, while lambda.1se is the value of λ that gives the most regularized model such that the cross-validated error is within one standard error of the minimum.

We can use the following code to get the value of lambda.min and the model coefficients at that value of λ:

cvfit$lambda.min

## [1] 0.06284188

coef(cvfit, s = "lambda.min")

## 21 x 1 sparse Matrix of class "dgCMatrix"
##                       s1
## (Intercept)  0.145832036
## V1           1.340981414
## V2           .          
## V3           0.708347140
## V4           .          
## V5          -0.848087765
## V6           0.554823782
## V7           0.038519738
....

To get the corresponding values at lambda.1se, simply replace lambda.min with lambda.1se above, or omit the s argument, since lambda.1se is the default.

Note that the coefficients are represented in the sparse matrix format. This is because the solutions along the regularization path are often sparse, and hence it is more efficient in time and space to use a sparse format. If you prefer non-sparse format, pipe the output through as.matrix().

Predictions can be made based on the fitted cv.glmnet object as well. The code below gives predictions for the new input matrix newx at lambda.min:

predict(cvfit, newx = x[1:5,], s = "lambda.min")

##      lambda.min
## [1,] -1.3574653
## [2,]  2.5776672
## [3,]  0.5846421
## [4,]  2.0280562
## [5,]  1.5780633

This concludes glmnet 101. With the tools introduced so far, users are able to fit the entire elastic net family, including ridge regression, using squared-error loss. There are many more arguments in the package that give users a great deal of flexibility. To learn more, move on to later sections.

Linear Regression: family = "gaussian" (default)

"gaussian" is the default family argument for the function glmnet. Suppose we have observations x_i ∈ ℝ^p and the responses y_i ∈ ℝ, i = 1, …, N. The objective function for the Gaussian family is $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{p+1}}\frac{1}{2N} \sum_{i=1}^N (y_i -\beta_0-x_i^T \beta)^2+\lambda \left[ (1-\alpha)\|\beta\|_2^2/2 + \alpha\|\beta\|_1\right], $$ where λ ≥ 0 is a complexity parameter and 0 ≤ α ≤ 1 is a compromise between ridge regression (α = 0) and lasso regression (α = 1).

glmnet applies coordinate descent to solve the problem. Specifically, suppose we have current estimates $\tilde{\beta_0}$ and β̃_ℓ ∀ℓ ∈ 1, …, p. By computing the gradient at β_j = β̃_j and simple calculus, the update is $$ \tilde{\beta}_j \leftarrow \frac{S(\frac{1}{N}\sum_{i=1}^N x_{ij}(y_i-\tilde{y}_i^{(j)}),\lambda \alpha)}{1+\lambda(1-\alpha)}, $$ where ỹ_i^(j) = β̃₀ + ∑_{ℓ ≠ j}x_iℓβ̃_ℓ, and S(z, γ) is the soft-thresholding operator with value sign(z)(|z| − γ)₊.

This formula above applies when the x variables are standardized to have unit variance (the default); it is slightly more complicated when they are not. Note that for family = "gaussian", glmnet standardizes y to have unit variance before computing its lambda sequence (and then unstandardizes the resulting coefficients). If you wish to reproduce or compare results with other software, it is best to supply a standardized y first (Using the “1/N” variance formula).

wts <-  c(rep(1,50), rep(2,50))
fit <- glmnet(x, y, alpha = 0.2, weights = wts, nlambda = 20)

print(fit)

## 
## Call:  glmnet(x = x, y = y, weights = wts, alpha = 0.2, nlambda = 20) 
## 
##    Df  %Dev Lambda
## 1   0  0.00 7.9390
## 2   4 17.89 4.8890
## 3   7 44.45 3.0110
## 4   7 65.67 1.8540
## 5   8 78.50 1.1420
## 6   9 85.39 0.7033
## 7  10 88.67 0.4331
## 8  11 90.25 0.2667
## 9  14 91.01 0.1643
## 10 17 91.38 0.1012
## 11 17 91.54 0.0623
## 12 17 91.60 0.0384
## 13 19 91.63 0.0236
## 14 20 91.64 0.0146
## 15 20 91.64 0.0090
## 16 20 91.65 0.0055
## 17 20 91.65 0.0034

fit <- glmnet(x, y)
any(fit$lambda == 0.5)  # 0.5 not in original lambda sequence

## [1] FALSE

coef.apprx <- coef(fit, s = 0.5, exact = FALSE)
coef.exact <- coef(fit, s = 0.5, exact = TRUE, x=x, y=y)
cbind2(coef.exact[which(coef.exact != 0)], 
       coef.apprx[which(coef.apprx != 0)])

##            [,1]       [,2]
## [1,]  0.2613110  0.2613110
## [2,]  1.0055470  1.0055473
## [3,]  0.2677140  0.2677134
## [4,] -0.4476485 -0.4476475
## [5,]  0.2379287  0.2379283
## [6,] -0.8230862 -0.8230865
## [7,] -0.5553678 -0.5553675

predict(fit, newx = x[1:5,], type = "response", s = 0.05)

##              s1
## [1,] -1.3362652
## [2,]  2.5894245
## [3,]  0.5872868
## [4,]  2.0977222
## [5,]  1.6436280

plot(fit, xvar = "lambda", label = TRUE)

plot(fit, xvar = "dev", label = TRUE)

cvfit <- cv.glmnet(x, y, type.measure = "mse", nfolds = 20)

print(cvfit)

## 
## Call:  cv.glmnet(x = x, y = y, type.measure = "mse", nfolds = 20) 
## 
## Measure: Mean-Squared Error 
## 
##      Lambda Index Measure     SE Nonzero
## min 0.07569    34   1.063 0.1135       9
## 1se 0.14517    27   1.164 0.1297       8

library(doMC)
registerDoMC(cores = 2)
X <- matrix(rnorm(1e4 * 200), 1e4, 200)
Y <- rnorm(1e4)

system.time(cv.glmnet(X, Y))

##    user  system elapsed 
##   2.440   0.080   2.518

system.time(cv.glmnet(X, Y, parallel = TRUE))

##    user  system elapsed 
##   2.450   0.157   1.567

cvfit$lambda.min

## [1] 0.07569327

predict(cvfit, newx = x[1:5,], s = "lambda.min")

##      lambda.min
## [1,] -1.3638848
## [2,]  2.5713428
## [3,]  0.5729785
## [4,]  1.9881422
## [5,]  1.5179882

coef(cvfit, s = "lambda.min")

## 21 x 1 sparse Matrix of class "dgCMatrix"
##                      s1
## (Intercept)  0.14867414
## V1           1.33377821
## V2           .         
## V3           0.69787701
## V4           .         
## V5          -0.83726751
## V6           0.54334327
## V7           0.02668633
....

foldid <- sample(1:10, size = length(y), replace = TRUE)
cv1  <- cv.glmnet(x, y, foldid = foldid, alpha = 1)
cv.5 <- cv.glmnet(x, y, foldid = foldid, alpha = 0.5)
cv0  <- cv.glmnet(x, y, foldid = foldid, alpha = 0)

par(mfrow = c(2,2))
plot(cv1); plot(cv.5); plot(cv0)
plot(log(cv1$lambda)   , cv1$cvm , pch = 19, col = "red",
     xlab = "log(Lambda)", ylab = cv1$name)
points(log(cv.5$lambda), cv.5$cvm, pch = 19, col = "grey")
points(log(cv0$lambda) , cv0$cvm , pch = 19, col = "blue")
legend("topleft", legend = c("alpha= 1", "alpha= .5", "alpha 0"),
       pch = 19, col = c("red","grey","blue"))

tfit <- glmnet(x, y, lower.limits = -0.7, upper.limits = 0.5)
plot(tfit)

p.fac <- rep(1, 20)
p.fac[c(1, 3, 5)] <- 0
pfit <- glmnet(x, y, penalty.factor = p.fac)
plot(pfit, label = TRUE)

Linear Regression: family = "mgaussian" (multi-response)

The multi-response Gaussian family is useful when there are a number of (correlated) responses, also known as the “multi-task learning” problem. Here, a variable is either included in the model for all the responses, or excluded for all the responses. Most of the arguments for this family are the same as that for family = "gaussian", so we focus on the differences with the single response model.

As the name suggests, the response y is not a vector but a matrix of quantitative responses. As a result, the coefficients at each value of lambda are also a matrix.

glmnet solves the problem $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{(p+1)\times K}}\frac{1}{2N} \sum_{i=1}^N \|y_i -\beta_0-\beta^T x_i\|^2_F+\lambda \left[ (1-\alpha)\|\beta\|_F^2/2 + \alpha\sum_{j=1}^p\|\beta_j\|_2\right]. $$ Here β_j is the jth row of the p × K coefficient matrix β, and we replace the absolute penalty on each single coefficient by a group-lasso penalty on each coefficient K-vector β_j for a single predictor (i.e. column of the x matrix). The group lasso penalty behaves like the lasso, but on the whole group of coefficients for each response: they are either all zero, or else none are zero, but are shrunk by an amount depending on λ.

We use a set of data generated beforehand for illustration. We fit a regularized multi-response Gaussian model to the data, with an object mfit returned.

data(MultiGaussianExample)
x <- MultiGaussianExample$x
y <- MultiGaussianExample$y
mfit <- glmnet(x, y, family = "mgaussian")

The standardize.response argument is only for mgaussian family. If standardize.response = TRUE, the response variables are standardized (default is FALSE).

As before, we can use the plot method to visualize the coefficients:

plot(mfit, xvar = "lambda", label = TRUE, type.coef = "2norm")

Note that we set type.coef = "2norm". Under this setting, a single curve is plotted per variable, with value equal to the ℓ₂ norm of the variable’s coefficient vector. The default setting is type.coef = "coef", where a coefficient plot is created for each response (multiple figures). xvar and label are two other arguments which have the same functionality as in the single-response case.

We can extract the coefficients and make predictions at requested values of λ by using the coef and predict methods respectively, as before. Here is an example of a predict call:

predict(mfit, newx = x[1:5,], s = c(0.1, 0.01))

## , , 1
## 
##              y1         y2         y3       y4
## [1,] -4.7106263 -1.1634574  0.6027634 3.740989
## [2,]  4.1301735 -3.0507968 -1.2122630 4.970141
## [3,]  3.1595229 -0.5759621  0.2607981 2.053976
## [4,]  0.6459242  2.1205605 -0.2252050 3.146286
## [5,] -1.1791890  0.1056262 -7.3352965 3.248370
## 
## , , 2
## 
##              y1         y2         y3       y4
## [1,] -4.6415158 -1.2290282  0.6118289 3.779521
## [2,]  4.4712843 -3.2529658 -1.2572583 5.266039
## [3,]  3.4735228 -0.6929231  0.4684037 2.055574
## [4,]  0.7353311  2.2965083 -0.2190297 2.989371
## [5,] -1.2759930  0.2892536 -7.8259206 3.205211

The prediction result is saved in a three-dimensional array with the first two dimensions being the prediction matrix for each response variable and the third corresponding to the response variables.

Logistic Regression: family = "binomial"

Logistic regression is a widely-used model when the response is binary. Suppose the response variable takes values in 𝒢 = {1, 2}. Denote y_i = I(g_i = 1). We model

$$\mbox{Pr}(G=2|X=x)=\frac{e^{\beta_0+\beta^Tx}}{1+e^{\beta_0+\beta^Tx}},$$

which can be written in the following form:

$$\log\frac{\mbox{Pr}(G=2|X=x)}{\mbox{Pr}(G=1|X=x)}=\beta_0+\beta^Tx,$$ the so-called “logistic” or log-odds transformation.

The objective function for logistic regression is the penalized negative binomial log-likelihood, and is $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{p+1}} -\left[\frac{1}{N} \sum_{i=1}^N y_i \cdot (\beta_0 + x_i^T \beta) - \log (1+e^{(\beta_0+x_i^T \beta)})\right] + \lambda \big[ (1-\alpha)\|\beta\|_2^2/2 + \alpha\|\beta\|_1\big]. $$ Logistic regression is often plagued with degeneracies when p > N and exhibits wild behavior even when N is close to p; the elastic net penalty alleviates these issues, and regularizes and selects variables as well.

We use a “proximal Newton” algorithm for optimizing this criterion. This makes repeated use of a quadratic approximation to the log-likelihood, and then weighted coordinate descent on the resulting penalized weighted least-squares problem. These constitute an outer and inner loop, also known as iteratively reweighted penalized least squares.

For illustration purposes, we load the pre-generated input matrix x and the response vector y from the data file. The input matrix x is the same as for other families. For binomial logistic regression, the response variable y should be either a binary vector, a factor with two levels, or a two-column matrix of counts or proportions. The latter is useful for grouped binomial data, or in applications where we have “soft” class membership, such as occurs in the EM algorithm.

data(BinomialExample)
x <- BinomialExample$x
y <- BinomialExample$y

Other optional arguments of glmnet for binomial regression are almost same as those for Gaussian family. Don’t forget to set family option to “binomial”:

fit <- glmnet(x, y, family = "binomial")

As before, we can print and plot the fitted object, extract the coefficients at specific λ’s and also make predictions. For plotting, the optional arguments such as xvar and label work in the same way as for family = "gaussian". Prediction is a little different for family = "binomial", mainly in the function argument type:

• “link” gives the linear predictors.

• “response” gives the fitted probabilities.

• “class” produces the class label corresponding to the maximum probability.

As with family = "gaussian", “coefficients” computes the coefficients at values of s and “nonzero” retuns a list of the indices of the nonzero coefficients for each value of s. Note that the results (“link”, “response”, “coefficients”, “nonzero”) are returned only for the class corresponding to the second level of the factor response.

In the following example, we make prediction of the class labels at λ = 0.05, 0.01.

predict(fit, newx = x[1:5,], type = "class", s = c(0.05, 0.01))

##      s1  s2 
## [1,] "0" "0"
## [2,] "1" "1"
## [3,] "1" "1"
## [4,] "0" "0"
## [5,] "1" "1"

For logistic regression, cv.glmnet has similar arguments and usage as Gaussian. nfolds, weights, lambda, parallel are all available to users. There are some differences in type.measure: “deviance” and “mse” do not both mean squared loss. Rather,

• “mse” uses squared loss.

• “deviance” uses actual deviance.

• “mae” uses mean absolute error.

• “class” gives misclassification error.

• “auc” (for two-class logistic regression ONLY) gives area under the ROC curve.

For example, the code below uses misclassification error as the criterion for 10-fold cross-validation:

cvfit <- cv.glmnet(x, y, family = "binomial", type.measure = "class")

As before, we can plot the object and show the optimal values of λ.

plot(cvfit)

cvfit$lambda.min

## [1] 0.02140756

cvfit$lambda.1se

## [1] 0.04945423

coef and predict for the cv.glmnet object for family = "binomial" are simliar to the Gaussian case and we omit the details.

Like other generalized linear models, glmnet allows for an “offset”. This is a fixed vector of N numbers that is added into the linear predictor. For example, you may have fitted some other logistic regression using other variables (and data), and now you want to see if the present variables can add further predictive power. To do this, you can use the predicted logit from the other model as an offset in the glmnet call. Offsets are also useful in Poisson models, which we discuss later.

Multinomial Regression: family = "multinomial"

The multinomial model extends the binomial when the number of classes is more than two. Suppose the response variable has K levels ${\cal G}=\{1,2,\ldots,K\}$. Here we model $$\mbox{Pr}(G=k|X=x)=\frac{e^{\beta_{0k}+\beta_k^Tx}}{\sum_{\ell=1}^Ke^{\beta_{0\ell}+\beta_\ell^Tx}}.$$ There is a linear predictor for each class!

Let Y be the N × K indicator response matrix, with elements y_iℓ = I(g_i = ℓ). Then the elastic net penalized negative log-likelihood function becomes $$ \ell(\{\beta_{0k},\beta_{k}\}_1^K) = -\left[\frac{1}{N} \sum_{i=1}^N \Big(\sum_{k=1}^Ky_{il} (\beta_{0k} + x_i^T \beta_k)- \log \big(\sum_{\ell=1}^K e^{\beta_{0\ell}+x_i^T \beta_\ell}\big)\Big)\right] +\lambda \left[ (1-\alpha)\|\beta\|_F^2/2 + \alpha\sum_{j=1}^p\|\beta_j\|_q\right]. $$ Here we really abuse notation! β is a p × K matrix of coefficients. β_k refers to the kth column (for outcome category k), and β_j the jth row (vector of K coefficients for variable j). The last penalty term is ∥β_j∥_q. We support two options for q: q ∈ {1, 2}. When q = 1, this is a lasso penalty on each of the parameters. When q = 2, this is a grouped-lasso penalty on all the K coefficients for a particular variable, which makes them all be zero or nonzero together.

The standard Newton algorithm can be tedious here. Instead, for q = 1 we use a so-called partial Newton algorithm by making a partial quadratic approximation to the log-likelihood, allowing only (β_0k, β_k) to vary for a single class at a time. For each value of λ, we first cycle over all classes indexed by k, computing each time a partial quadratic approximation about the parameters of the current class. Then, the inner procedure is almost the same as for the binomial case. When q = 2, we use a different approach that we will not explain here.

For the family = "multinomial" case, usage is similar to that for family = "binomial". In this section we describe the differences. First, we load a set of generated data:

data(MultinomialExample)
x <- MultinomialExample$x
y <- MultinomialExample$y

The response variable can be a nc >= 2 level factor, or an nc-column matrix of counts or proportions. Internally glmnet will make the rows of this matrix sum to 1, and absorb the total mass into the weight for that observation. offset should be a nobs x nc matrix if one is provided.

A special option for multinomial regression is type.multinomial, which allows the usage of a grouped lasso penalty (q = 2) if type.multinomial = "grouped". The default is type.multinomial = "ungrouped" (q = 1).

fit <- glmnet(x, y, family = "multinomial", type.multinomial = "grouped")
plot(fit, xvar = "lambda", label = TRUE, type.coef = "2norm")

For the plot method, the function arguments are xvar, label and type.coef, in addition to other ordinary graphical parameters. xvar and label are the same as other families while type.coef is only for multinomial regression and multi-response Gaussian model. It can produce a figure of coefficients for each response variable if type.coef = "coef" or a figure showing the ℓ₂-norm in one figure if type.coef = "2norm".

We can also do cross-validation and plot the returned object. Note that although type.multinomial is not a named argument in cv.glmnet, in fact any argument that can be passed to glmnet is valid in the argument list of cv.glmnet. Such arguments are passed via the ... argument directly to the calls to glmnet inside the cv.glmnet function.

cvfit <- cv.glmnet(x, y, family = "multinomial", type.multinomial = "grouped")
plot(cvfit)

Users may wish to predict at the optimally selected λ:

predict(cvfit, newx = x[1:10,], s = "lambda.min", type = "class")

##       1  
##  [1,] "3"
##  [2,] "2"
##  [3,] "2"
##  [4,] "3"
##  [5,] "1"
##  [6,] "3"
##  [7,] "3"
##  [8,] "1"
##  [9,] "1"
## [10,] "2"

Poisson Regression: family = "poisson"

Poisson regression is used to model count data under the assumption of Poisson error, or otherwise non-negative data where the mean and variance are proportional. Like the Gaussian and binomial models, the Poisson distribution is a member of the exponential family of distributions. We usually model its positive mean on the log scale: log μ(x) = β₀ + β′x.

The log-likelihood for observations {x_i, y_i}₁^N is given by $$ l(\beta|X, Y) = \sum_{i=1}^N \left(y_i (\beta_0+\beta^T x_i) - e^{\beta_0+\beta^Tx_i}\right). $$ As before, we optimize the penalized log-likelihood: $$ \min_{\beta_0,\beta} -\frac1N l(\beta|X, Y) + \lambda \left((1-\alpha) \sum_{i=1}^N \beta_i^2/2 +\alpha \sum_{i=1}^N |\beta_i|\right). $$

glmnet uses an outer Newton loop and an inner weighted least-squares loop (as in logistic regression) to optimize this criterion.

First, we load a pre-generated set of Poisson data:

data(PoissonExample)
x <- PoissonExample$x
y <- PoissonExample$y

We apply the function glmnet with family = "poisson":

fit <- glmnet(x, y, family = "poisson")

The optional input arguments of glmnet for "poisson" family are similar to those for other families.

offset is a particularly useful argument for Poisson models. When dealing with rate data in Poisson models, the counts collected are often based on different exposures such as length of time observed, area and years. A poisson rate μ(x) is relative to a unit exposure time, so if an observation y_i was exposed for E_i units of time, then the expected count would be E_iμ(x), and the log mean would be log (E_i) + log (μ(x)). In a case like this, we would supply an offset log (E_i) for each observation. Hence offset is a vector of length N that is included in the linear predictor. (Warning: if offset is supplied in glmnet, offsets must also also be supplied to predict via the newoffset argument to make reasonable predictions.)

Again, we plot the coefficients to have a first sense of the result.

plot(fit)

As before, we can extract the coefficients and make predictions at certain λ’s using coef and predict respectively. The optional input arguments are similar to those for other families. For the predict method, the argument type has the same meaning as that for family = "binomial", except that “response” gives the fitted mean (rather than fitted probabilities in the binomial case). For example, we can do the following:

coef(fit, s = 1)

## 21 x 1 sparse Matrix of class "dgCMatrix"
##                      s1
## (Intercept)  0.61123371
## V1           0.45819758
## V2          -0.77060709
## V3           1.34015128
## V4           0.04350500
....

predict(fit, newx = x[1:5,], type = "response", s = c(0.1,1))

##              s1         s2
## [1,]  2.4944232  4.4263365
## [2,] 10.3513120 11.0586174
## [3,]  0.1179704  0.1781626
## [4,]  0.9713412  1.6828778
## [5,]  1.1133472  1.9934537

We may also use cross-validation to find the optimal λ’s and thus make inferences.

cvfit <- cv.glmnet(x, y, family = "poisson")

Options are almost the same as the Gaussian family except that for type.measure:

• “deviance” (default) gives the deviance.
• “mse” is for mean squared error.
• “mae” is for mean absolute error.

Cox Regression: family = "cox"

The Cox proportional hazards model is commonly used for the study of the relationship beteween predictor variables and survival time. We have another vignette (“Regularized Cox Regression”) dedicated solely to fitting regularized Cox models with the glmnet package; please consult that vignette for details.

Programmable GLM families: family = family()

Since version 4.0, glmnet has the facility to fit any GLM family by specifying a family object, as used by stats::glm. For these more general families, the outer Newton loop is performed in R, while the inner elastic-net loop is performed in Fortran, for each value of lambda. The price for this generality is a small hit in speed. For details, see the vignette “GLM family functions in glmnet”

Assessing models on test data

Once we have fit a series of models using glmnet, we often assess their performance on a set of evaluation or test data. We usually go through the process of building a prediction matrix, deciding on the performance measure, and computing these measures for a series of values for lambda (and gamma for relaxed fits). glmnet provides three functions (assess.glmnet, roc.glmnet and confusion.glmnet) that make these tasks easier.

data(BinomialExample)
x <- BinomialExample$x
y <- BinomialExample$y
itrain <- 1:70
fit <- glmnet(x[itrain, ], y[itrain], family = "binomial", nlambda = 5)
assess.glmnet(fit, newx = x[-itrain, ], newy = y[-itrain])

## $deviance
##        s0        s1        s2        s3        s4 
## 1.3877348 0.8612331 1.7948439 3.0335905 3.6930687 
## attr(,"measure")
## [1] "Binomial Deviance"
## 
## $class
##        s0        s1        s2        s3        s4 
## 0.4666667 0.1666667 0.2333333 0.1666667 0.1666667 
## attr(,"measure")
## [1] "Misclassification Error"
## 
## $auc
## [1] 0.5000000 0.9062500 0.8482143 0.8169643 0.8303571
## attr(,"measure")
## [1] "AUC"
## 
## $mse
##        s0        s1        s2        s3        s4 
## 0.5006803 0.2529304 0.3633411 0.3514574 0.3500440 
## attr(,"measure")
## [1] "Mean-Squared Error"
## 
## $mae
##        s0        s1        s2        s3        s4 
## 0.9904762 0.5114320 0.4597047 0.3928790 0.3767338 
## attr(,"measure")
## [1] "Mean Absolute Error"

pred <- predict(fit, newx = x[-itrain, ])
assess.glmnet(pred, newy = y[-itrain], family = "binomial")

glmnet.measures()

## $gaussian
## [1] "mse" "mae"
## 
## $binomial
## [1] "deviance" "class"    "auc"      "mse"      "mae"     
## 
## $poisson
## [1] "deviance" "mse"      "mae"     
## 
## $cox
## [1] "deviance" "C"       
## 
## $multinomial
## [1] "deviance" "class"    "mse"      "mae"     
## 
## $mgaussian
## [1] "mse" "mae"
## 
## $GLM
## [1] "deviance" "mse"      "mae"

cfit <- cv.glmnet(x[itrain, ], y[itrain], family = "binomial", nlambda = 30)
assess.glmnet(cfit, newx = x[-itrain, ], newy = y[-itrain])

## $deviance
## lambda.1se 
##  0.9482246 
## attr(,"measure")
## [1] "Binomial Deviance"
## 
## $class
## lambda.1se 
##        0.2 
## attr(,"measure")
## [1] "Misclassification Error"
....

assess.glmnet(cfit, newx = x[-itrain, ],newy = y[-itrain], s = "lambda.min")

## $deviance
## lambda.min 
##  0.8561849 
## attr(,"measure")
## [1] "Binomial Deviance"
## 
## $class
## lambda.min 
##  0.1666667 
## attr(,"measure")
## [1] "Misclassification Error"
....

cfit <- cv.glmnet(x, y, family = "binomial", keep = TRUE, nlambda = 30)
assess.glmnet(cfit$fit.preval, newy = y, family = "binomial")

## $deviance
##        s0        s1        s2        s3        s4        s5        s6        s7 
## 1.4131504 1.2947431 1.1887748 1.0940954 0.9903013 0.8999068 0.8407559 0.8075691 
##        s8        s9       s10       s11       s12       s13       s14       s15 
## 0.8053265 0.8302531 0.8783749 0.9490040 1.0501903 1.1733004 1.2947184 1.4200727 
##       s16       s17       s18       s19       s20       s21       s22       s23 
## 1.5596069 1.7098302 1.8594572 2.0060912 2.1283083 2.2510820 2.3757275 2.4799198 
##       s24       s25       s26       s27       s28       s29 
## 2.5765861 2.6589164 2.7143188 2.7634920 2.8119805 2.8404461 
## attr(,"measure")
## [1] "Binomial Deviance"
....

cfit <- cv.glmnet(x, y, family = "binomial", type.measure = "auc", 
                  keep = TRUE)
rocs <- roc.glmnet(cfit$fit.preval, newy = y)

best <- cvfit$index["min",]
plot(rocs[[best]], type = "l")
invisible(sapply(rocs, lines, col="grey"))
lines(rocs[[best]], lwd = 2,col = "red")

data(MultinomialExample)
x <- MultinomialExample$x
y <- MultinomialExample$y
set.seed(101)
itrain <- sample(1:500, 400, replace = FALSE)
cfit <- cv.glmnet(x[itrain, ], y[itrain], family = "multinomial")
cnf <- confusion.glmnet(cfit, newx = x[-itrain, ], newy = y[-itrain])

print(cnf)

##          True
## Predicted  1  2  3 Total
##     1     13  6  4    23
##     2      7 25  5    37
##     3      4  3 33    40
##     Total 24 34 42   100
## 
##  Percent Correct:  0.71

cfit <- cv.glmnet(x, y, family = "multinomial", type = "class", keep = TRUE)
cnf <- confusion.glmnet(cfit$fit.preval, newy = y, family = "multinomial")
best <- cfit$index["min",]
print(cnf[[best]])

##          True
## Predicted   1   2   3 Total
##     1      76  22  14   112
##     2      39 129  23   191
##     3      27  23 147   197
##     Total 142 174 184   500
## 
##  Percent Correct:  0.704

Filtering variables

The exclude argument to glmnet accepts a vector of indices, indicating which variables should be excluded from the fit, i.e. get zeros for their coefficients. From version 4.1-2, the exclude argument can accept a function. The idea is that variables can be filtered based on some of their properties (e.g. too sparse) before any models are fit. When performing cross-validation (CV), this filtering should be done separately inside each fold of cv.glmnet.

Here is a typical filter function: it excludes variables that are more than 80% sparse.

filter <- function(x, ...) which(colMeans(x == 0) > 0.8)

This function gets invoked inside the call to glmnet, and uses the supplied x to generate the indices.

We give a simple example using this:

set.seed(101)
n <-500; p <- 50
x <- matrix(rnorm(n * p), n, p)
x[sample(seq(length(x)), 4 * n * p / 5)] <- 0
y <- rnorm(n) + x %*% (rnorm(p) / 5) > 0
excl <- filter(x)
print(excl)

##  [1]  4  5  6  7  8  9 11 15 22 23 24 25 26 27 28 29 33 35 36 40 42 46

fit.orig <- glmnet(x, y, family = "binomial", exclude = excl)
fit.new  <- glmnet(x, y, family = "binomial", exclude = filter)
all.equal(fit.orig, fit.new)

## [1] "Component \"call\": target, current do not match when deparsed"

The fits are the same (the call component is all that differs).

The nice thing with this approach is that cv.glmnet does the right thing: it applies the filter function separately to each training-fold x, and hence accounts for any bias that may be incurred by the filtering.

cvfit.filt <- cv.glmnet(x, y, family = "binomial", exclude = filter)

The most general form of the filter function is

filter <- function(x, y, weights, ...) {}

Yes, users can use y as well in their filtering criterion, and the observation weights if that makes sense. As we did above, some of the arguments can be omitted, but the ... must always be there. We will give some useful examples of filter functions a bit later.

An important point to note is that any other arguments in a user’s filter function are ignored. So, what we should if we want one or more additional parameters to guide the filtering, such as the choice of 0.8 in our filter function above? The safest way to achieve this is to write a filter generator function, that produces a filter function as its output. We give an example:

sparsity <- function(fraction = 0.7) {
  function(x, ...) which(colMeans(x == 0) > fraction)
}
sparsity(0.5)

## function (x, ...) 
## which(colMeans(x == 0) > fraction)
## <environment: 0x5593391b71c0>

Here, sparsity() is a function that produces a function. The argument fraction is found in the environment of that function, and hence is built in. We can now run cv.glmnet as follows:

foldid <- sample(rep(1:10,length.out = length(y)))
cvfit.filt1 <- cv.glmnet(x, y, family = "binomial", foldid = foldid, 
                         exclude = filter)
cvfit.filt2 <- cv.glmnet(x, y, family = "binomial", foldid = foldid, 
                         exclude = sparsity(0.8))
all.equal(cvfit.filt1, cvfit.filt2)

## [1] "Component \"call\": target, current do not match when deparsed"                          
## [2] "Component \"glmnet.fit\": Component \"call\": target, current do not match when deparsed"

These agree in the important parts.

We end this section with some useful filtering functions. In genomics, where the x matrix can be very wide, we often filter features based on variance. We will use expression arrays as an example. All else being equal, we expect the variance of the gene expression of a gene to be higher if there is something going on across the samples, and we guess that that something is related to the response. Here is a function that efficiently computes the column-wise variance for a wide matrix, followed by a filter function that uses it.

uvar <- function(x, means = FALSE) {
  # if means = TRUE, the means and variances are returned, 
  # otherwise just the variances
  m <- colMeans(x)
  n <- nrow(x)
  x <- x - outer(rep(1,n),m)
  v <- colSums(x^2) / (n - 1)
  if (means) list(mean = m, var = v) else v
}

vfilter <- function(q = 0.3) {
  function(x,...) {
    v <- uvar(x)
    which(v < quantile(v, q))
  }
}

Here, our filter function vfilter() is a function generator as discussed above, and will exclude the fraction q of variables with the lowest variance.

We can also use y as part of our filtering. This final example builds on the example above. Here the response y is binary, taking two values. We want to filter out variables with low values of the two-sample t-statistic with groups defined by y. Again, we present an efficient function for computing the t-statistics, and then the filter. (Notice that for convenience, our function uvar above can optionally return a mean vector and a variance vector.)

ut.test <- function(x, y, s0 = 0) {
  ni <- table(y); n <- sum(ni)
  if(length(ni) != 2) stop("Only two-sample t-test here")
  index <- seq(n)
  mv <- tapply(index, y, function(i, x) uvar(x[i, ], means = TRUE), x = x)
  ss <- ((ni[1] - 1) * mv[[1]]$var  + (ni[2] - 1) * mv[[2]]$var)
  sd <- sqrt(ss * (1 / ni[[1]] + 1 / ni[[2]]) / (n - 2))
  numer <- mv[[1]]$mean - mv[[2]]$mean
  numer / (sd + s0)
}

tfilter <- function(q = 0.3, s0 = 0) {
  function(x, y, ...) {
    tstats <- ut.test(x, y, s0 = s0)
    which(tstats < quantile(tstats, q))
  }
}

One might think that unsupervised filtering using just x before fitting the model is fair game, and perhaps need not be accounted for in cross-validation. This is an interesting issue, and one can argue that there could be some bias if it was not accounted for. But filtering using x and y definitely introduces potential bias. However, it can still give good results, as long as it it correctly accounted for when running CV.

cvfit.filt3 <- cv.glmnet(x, y, family = "binomial", foldid = foldid, 
                         exclude = tfilter(0.4))

Other Package Features

In this section, we describe other features in the glmnet package that might be of interest to users.

data(SparseExample)
x <- SparseExample$x
y <- SparseExample$y
class(x)

## [1] "dgCMatrix"
## attr(,"package")
## [1] "Matrix"

fit <- glmnet(x, y)

cvfit = cv.glmnet(x, y)
plot(cvfit)

i <- sample(1:5, size = 25, replace = TRUE)
j <- sample(1:20, size = 25, replace = TRUE)
x <- rnorm(25)
nx <- sparseMatrix(i = i, j = j, x = x, dims = c(5, 20))
predict(cvfit, newx = nx, s = "lambda.min")

##      lambda.min
## [1,]  2.3731134
## [2,] -0.1100765
## [3,] -3.0124618
## [4,]  0.3655119
## [5,] -0.4475473

data(BinomialExample)
x <- BinomialExample$x
y <- BinomialExample$y
fit <- bigGlm(x, y, family = "binomial", lower.limits = -1)
print(fit)

## 
## Call:  bigGlm(x = x, y = y, family = "binomial", lower.limits = -1) 
## 
##   Df  %Dev Lambda
## 1 30 77.57      0

set.seed(101)
X <- matrix(rnorm(5), nrow = 5)
X2 <- sample(letters[1:3], 5, replace = TRUE)
X3 <- sample(LETTERS[1:3], 5, replace = TRUE)
df <- data.frame(X, X2, X3)
makeX(df)

##            X X2a X2b X2c X3B X3C
## 1 -0.3260365   0   0   1   0   1
## 2  0.5524619   0   1   0   0   1
## 3 -0.6749438   0   0   1   1   0
## 4  0.2143595   0   0   1   0   1
## 5  0.3107692   1   0   0   0   1

makeX(df, sparse = TRUE)

## 5 x 6 sparse Matrix of class "dgCMatrix"
##            X X2a X2b X2c X3B X3C
## 1 -0.3260365   .   .   1   .   1
## 2  0.5524619   .   1   .   .   1
## 3 -0.6749438   .   .   1   1   .
## 4  0.2143595   .   .   1   .   1
## 5  0.3107692   1   .   .   .   1

Xn  <- X ; Xn[3,1] <- NA
X2n <- X2; X2n[1]  <- NA
X3n <- X3; X3n[5]  <- NA
dfn <- data.frame(Xn, X2n, X3n)
dfn

##           Xn  X2n  X3n
## 1 -0.3260365 <NA>    C
## 2  0.5524619    b    C
## 3         NA    c    B
## 4  0.2143595    c    C
## 5  0.3107692    a <NA>

makeX(dfn)

##           Xn X2na X2nb X2nc X3nB X3nC
## 1 -0.3260365   NA   NA   NA    0    1
## 2  0.5524619    0    1    0    0    1
## 3         NA    0    0    1    1    0
## 4  0.2143595    0    0    1    0    1
## 5  0.3107692    1    0    0   NA   NA

makeX(dfn, na.impute = TRUE, sparse = TRUE)

## 5 x 6 sparse Matrix of class "dgCMatrix"
##           Xn X2na X2nb X2nc X3nB X3nC
## 1 -0.3260365 0.25 0.25  0.5 .    1.00
## 2  0.5524619 .    1.00  .   .    1.00
## 3  0.1878885 .    .     1.0 1.00 .   
## 4  0.2143595 .    .     1.0 .    1.00
## 5  0.3107692 1.00 .     .   0.25 0.75

set.seed(102)
X <- matrix(rnorm(5), nrow = 5)
X2 <- sample(letters[1:3], 5, replace = TRUE)
X3 <- sample(LETTERS[1:3], 5, replace = TRUE)
Xn  <- X ; Xn[5,1] <- NA
X2n <- X2; X2n[1]  <- NA
X3n <- X3; X3n[2]  <- NA
dftn <- data.frame(Xn, X2n, X3n)
dftn

##           Xn  X2n  X3n
## 1  0.1805229 <NA>    C
## 2  0.7847340    c <NA>
## 3 -1.3531646    c    C
## 4  1.9832982    a    A
## 5         NA    c    A

makeX(dfn, dftn, sparse = TRUE)

## $x
## 5 x 7 sparse Matrix of class "dgCMatrix"
##           Xn X2na X2nb X2nc X3nA X3nB X3nC
## 1 -0.3260365   NA   NA   NA    .    .    1
## 2  0.5524619    .    1    .    .    .    1
## 3         NA    .    .    1    .    1    .
## 4  0.2143595    .    .    1    .    .    1
## 5  0.3107692    1    .    .   NA   NA   NA
## 
## $xtest
## 5 x 7 sparse Matrix of class "dgCMatrix"
##            Xn X2na X2nb X2nc X3nA X3nB X3nC
## 6   0.1805229   NA   NA   NA    .    .    1
## 7   0.7847340    .    .    1   NA   NA   NA
## 8  -1.3531646    .    .    1    .    .    1
## 9   1.9832982    1    .    .    1    .    .
## 10         NA    .    .    1    1    .    .

fit <- glmnet(x, y, trace.it = TRUE)

fit <- cv.glmnet(x, y, trace.it = TRUE)

glmnet.control(itrace = 1)

Appendix 0: Convergence Criteria

Glmnet uses a convergence criterion that focuses not on coefficient change but rather the impact of the change on the fitted values, and hence the loss part of the objective. The net result is a weighted norm of the coefficient change vector.

For Gaussian models it uses the following. Suppose observation i has weight w_i. Let v_j be the (weighted) sum-of-squares for variable x_j: $$v_j=\sum_{i=1}^Nw_ix_{ij}^2.$$ If there is an intercept in the model, these x_j will be centered by the weighted mean, and hence this would be a weighted variance. After β̂_j^o has been updated to β̂_jⁿ, we compute Δ_j = v_j(β̂_j^o − β̂_jⁿ)². After a complete cycle of coordinate descent, we look at Δ_max = max_jΔ_j. Why this measure? We can write $$\Delta_j=\frac1N\sum_{i=1}^N w_i(x_{ij}\hat\beta_j^o-x_{ij}\hat\beta_j^n)^2,$$ which measures the weighted sum of squares of changes in fitted values for this term. This measures the impact of the change in this coefficient on the fit. If the largest such change is negligible, we stop.

For logistic regression and other non-Gaussian models it is similar for the inner loop, only now the weights for each observation are more complex. For example, for logistic regression the weights are those that arise from the current Newton step, i.e. w_i^* = w_ip̂_i(1 − p̂_i), where the p̂_i’s are the fitted probabilities as we entered the current inner loop. The intuition is the same: it measures the impact of the coefficient change on the current weighted least squares loss, or quadratic approximation to the log-likelihood loss.

What about outer-loop convergence?

• If the argument family was a character string, we use the same measure, except now β̂^o is the coefficient vector before we entered this inner loop, and β̂ⁿ the converged solution for this inner loop. Hence if this Newton step had no impact, we declare outer-loop convergence.

• If the argument family was a class “family” object, outer-loop convergence is determined by the change in the objective function value. If the fractional change in the objective function value is less than the epsnr control parameter, we declare outer-loop convergence. epsnr can be changed via a call to glmnet.control.

Appendix 1: Internal Parameters

Our package has a set of internal parameters which control some aspects of the computation of the path. The factory default settings are expected to serve in most cases, and users do not need to make changes unless there are special requirements.

There are several parameters that users can change:

• fdev - minimum fractional change in deviance for stopping path; factory default = 1.0e-5.

• devmax - maximum fraction of explained deviance for stopping path; factory default = 0.999.

• eps - minimum value of lambda.min.ratio (see glmnet documentation); factory default= 1.0e-6.

• big - large floating point number; factory default = 9.9e35. Inf in definition of upper.limits is set to big.

• mnlam - minimum number of path points (lambda values) allowed; factory default = 5.

• pmin - minimum null probability for any class; factory default = 1.0e-5.

• exmx - maximum allowed exponent; factory default = 250.0.

• prec - convergence threshold for multi-response bounds adjustment solution; factory default = 1.0e-10.

• mxit - maximum iterations for multi-response bounds adjustment solution; factory default = 100.

• epsnr: convergence threshold for the iteratively reweighted least squares loop (see “The family Argument for glmnet” vignette); factory default = 1e-08.

• mxitnr: maximum iterations for the iteratively reweighted least squares loop for each value of λ (see “The family Argument for glmnet” vignette); factory default = 25.

• factory - If TRUE, reset all the parameters to the factory default; default is FALSE.

We illustrate how to change these control parameters through an example. Note that any changes made hold for the duration of the R session, or unless they are changed by the user with a subsequent call to glmnet.control.

data(QuickStartExample)
x <- QuickStartExample$x
y <- QuickStartExample$y
fit <- glmnet(x, y)
length(fit$lambda)  # number of lambda values fit

## [1] 67

We can change the minimum fractional change in deviance for stopping path and compare the results. By setting fdev to be larger than the default, we see that the computation stopped earlier in the path.

glmnet.control(fdev = 0.1)
fit <- glmnet(x, y)
length(fit$lambda)  # number of lambda values fit

## [1] 11

Users can reset to the default settings with the following code:

glmnet.control(factory = TRUE)

To view current settings, call glmnet.control without any arguments:

glmnet.control()

## $fdev
## [1] 1e-05
## 
## $eps
## [1] 1e-06
## 
## $big
## [1] 9.9e+35
....

Appendix 2: Comparison with Other Packages

Some may want to use glmnet to solve the lasso or elastic net problem at a single λ. We compare here the solution by glmnet with other packages (such as CVX), and also as an illustration of parameter settings in this situation.

(Warning: Though such problems can be solved by glmnet, it is not recommended and is not the spirit of the package. glmnet fits the entire solution path for the lasso or elastic net problems efficiently with various techniques such as using warm starts and strong rules. Those advantages will disappear if the λ sequence is forced to be only one value.)

We illustrate with a typical example in linear models for the purpose of comparison. Given X and Y, we want to find β such that min_β∥Y − Xβ∥₂² + λ₀∥β∥₁, where, say, λ₀ = 8.

We first solve this using glmnet. Notice that there is no intercept term in the objective function, and the columns of X are not necessarily standardized. Corresponding parameters have to be set to make it work correctly. In addition, there is a 1/(2n) factor before the quadratic term by default, so we need to adjust λ accordingly. For the purpose of comparison, we set thresh = 1e-20. However, this is not necessary in many practical applications.

np <- dim(x); n <- np[1]; p <-np[2]

fit <- glmnet(x, y, intercept = F, standardize = F, 
              lambda = 8 / (2 * n), thresh = 1e-20)

We then extract the coefficients (with no intercept):

beta_glmnet <- as.matrix(predict(fit, type = "coefficients")[-1,])

Alternatively, a more stable and strongly recommended way to perform this task is to first fit the entire lasso or elastic net path without specifying lambda, but then provide the requested λ₀ to a predict call to extract the corresponding coefficients. (Remember to set exact = TRUE in the predict call to get the exact solution. Otherwise, it will be approximated by linear interpolation.)

fit <- glmnet(x, y, intercept = F, standardize = F, thresh = 1e-20)
beta_glmnet <- as.matrix(predict(fit, s = 8 / (2 * n), 
                                 type = "coefficients", 
                                 exact = TRUE, x = x, y = y)[-1,])

Next, we use CVX, a general convex optimization solver, to solve this specific lasso problem. CVX is implemented in the CVXR package on CRAN.

library(CVXR)
beta <- Variable(p)
loss <- sum((y-x%*%beta)^2)/(2*n)
lassoPenalty <- function(beta,lambda)lambda*p_norm(beta,1)
obj <- loss + lassoPenalty(beta, lambda = 8/(2*n))
prob <- Problem(Minimize(obj))
result <- solve(prob)
beta_CVX <- result$getValue(beta)

For convenience, the results were saved in CVXResult.RData, and we simply load in the results.

data(CVXResults)

Finally, we solve the same problem with the lars package:

library(lars)
fit_lars <- lars(x, y, type = "lasso", intercept = F, normalize = F)
beta_lars <- predict(fit_lars, s = 8 / 2, type = "coefficients", 
                     mode = "lambda")$coefficients

The results are listed below up to 6 decimal digits (due to convergence thresholds). We see that all three packages give the same result.

cmp <- round(cbind(beta_glmnet, beta_lars, beta_CVX), digits = 6)
colnames(cmp) <- c("beta_glmnet", "beta_lars", "beta_CVX")
cmp

##     beta_glmnet beta_lars  beta_CVX
## V1     1.389118  1.389118  1.389118
## V2     0.007991  0.007991  0.007991
## V3     0.731234  0.731234  0.731234
## V4     0.031119  0.031119  0.031119
## V5    -0.866793 -0.866793 -0.866793
## V6     0.564867  0.564867  0.564867
## V7     0.069678  0.069678  0.069678
## V8     0.358346  0.358346  0.358346
## V9     0.000000  0.000000  0.000000
## V10    0.070565  0.070565  0.070565
## V11    0.173464  0.173464  0.173464
## V12   -0.027472 -0.027472 -0.027472
## V13   -0.017960 -0.017960 -0.017960
## V14   -1.138053 -1.138053 -1.138053
## V15   -0.070990 -0.070990 -0.070990
## V16    0.000000  0.000000  0.000000
## V17    0.000000  0.000000  0.000000
## V18    0.000000  0.000000  0.000000
## V19    0.000000  0.000000  0.000000
## V20   -1.097528 -1.097528 -1.097528