glmnet (version 4.1-8)

cv.glmnet: Cross-validation for glmnet


Does k-fold cross-validation for glmnet, produces a plot, and returns a value for lambda (and gamma if relax=TRUE)


  weights = NULL,
  offset = NULL,
  lambda = NULL,
  type.measure = c("default", "mse", "deviance", "class", "auc", "mae", "C"),
  nfolds = 10,
  foldid = NULL,
  alignment = c("lambda", "fraction"),
  grouped = TRUE,
  keep = FALSE,
  parallel = FALSE,
  gamma = c(0, 0.25, 0.5, 0.75, 1),
  relax = FALSE, = 0,


an object of class "cv.glmnet" is returned, which is a list with the ingredients of the cross-validation fit. If the object was created with relax=TRUE then this class has a prefix class of "cv.relaxed".


the values of lambda used in the fits.


The mean cross-validated error - a vector of length length(lambda).


estimate of standard error of cvm.


upper curve = cvm+cvsd.


lower curve = cvm-cvsd.


number of non-zero coefficients at each lambda.


a text string indicating type of measure (for plotting purposes).

a fitted glmnet object for the full data.


value of lambda that gives minimum cvm.


largest value of lambda such that error is within 1 standard error of the minimum.


if keep=TRUE, this is the array of prevalidated fits. Some entries can be NA, if that and subsequent values of lambda are not reached for that fold


if keep=TRUE, the fold assignments used


a one column matrix with the indices of lambda.min and lambda.1se in the sequence of coefficients, fits etc.


if relax=TRUE, this additional item has the CV info for each of the mixed fits. In particular it also selects lambda, gamma pairs corresponding to the 1se rule, as well as the minimum error. It also has a component index, a two-column matrix which contains the lambda and gamma indices corresponding to the "min" and "1se" solutions.



x matrix as in glmnet.


response y as in glmnet.


Observation weights; defaults to 1 per observation


Offset vector (matrix) as in glmnet


Optional user-supplied lambda sequence; default is NULL, and glmnet chooses its own sequence. Note that this is done for the full model (master sequence), and separately for each fold. The fits are then alligned using the master sequence (see the allignment argument for additional details). Adapting lambda for each fold leads to better convergence. When lambda is supplied, the same sequence is used everywhere, but in some GLMs can lead to convergence issues.


loss to use for cross-validation. Currently five options, not all available for all models. The default is type.measure="deviance", which uses squared-error for gaussian models (a.k.a type.measure="mse" there), deviance for logistic and poisson regression, and partial-likelihood for the Cox model. type.measure="class" applies to binomial and multinomial logistic regression only, and gives misclassification error. type.measure="auc" is for two-class logistic regression only, and gives area under the ROC curve. type.measure="mse" or type.measure="mae" (mean absolute error) can be used by all models except the "cox"; they measure the deviation from the fitted mean to the response. type.measure="C" is Harrel's concordance measure, only available for cox models.


number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3


an optional vector of values between 1 and nfolds identifying what fold each observation is in. If supplied, nfolds can be missing.


This is an experimental argument, designed to fix the problems users were having with CV, with possible values "lambda" (the default) else "fraction". With "lambda" the lambda values from the master fit (on all the data) are used to line up the predictions from each of the folds. In some cases this can give strange values, since the effective lambda values in each fold could be quite different. With "fraction" we line up the predictions in each fold according to the fraction of progress along the regularization. If in the call a lambda argument is also provided, alignment="fraction" is ignored (with a warning).


This is an experimental argument, with default TRUE, and can be ignored by most users. For all models except the "cox", this refers to computing nfolds separate statistics, and then using their mean and estimated standard error to describe the CV curve. If grouped=FALSE, an error matrix is built up at the observation level from the predictions from the nfolds fits, and then summarized (does not apply to type.measure="auc"). For the "cox" family, grouped=TRUE obtains the CV partial likelihood for the Kth fold by subtraction; by subtracting the log partial likelihood evaluated on the full dataset from that evaluated on the on the (K-1)/K dataset. This makes more efficient use of risk sets. With grouped=FALSE the log partial likelihood is computed only on the Kth fold


If keep=TRUE, a prevalidated array is returned containing fitted values for each observation and each value of lambda. This means these fits are computed with this observation and the rest of its fold omitted. The foldid vector is also returned. Default is keep=FALSE. If relax=TRUE, then a list of such arrays is returned, one for each value of 'gamma'. Note: if the value 'gamma=1' is omitted, this case is included in the list since it corresponds to the original 'glmnet' fit.


If TRUE, use parallel foreach to fit each fold. Must register parallel before hand, such as doMC or others. See the example below.


The values of the parameter for mixing the relaxed fit with the regularized fit, between 0 and 1; default is gamma = c(0, 0.25, 0.5, 0.75, 1)


If TRUE, then CV is done with respect to the mixing parameter gamma as well as lambda. Default is relax=FALSE

If, then progress bars are displayed; useful for big models that take a long time to fit. Limited tracing if parallel=TRUE


Other arguments that can be passed to glmnet


Jerome Friedman, Trevor Hastie and Rob Tibshirani
Noah Simon helped develop the 'coxnet' function.
Jeffrey Wong and B. Narasimhan helped with the parallel option
Maintainer: Trevor Hastie


The function runs glmnet nfolds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The error is accumulated, and the average error and standard deviation over the folds is computed. Note that cv.glmnet does NOT search for values for alpha. A specific value should be supplied, else alpha=1 is assumed by default. If users would like to cross-validate alpha as well, they should call cv.glmnet with a pre-computed vector foldid, and then use this same fold vector in separate calls to cv.glmnet with different values of alpha. Note also that the results of cv.glmnet are random, since the folds are selected at random. Users can reduce this randomness by running cv.glmnet many times, and averaging the error curves.

If relax=TRUE then the values of gamma are used to mix the fits. If \(\eta\) is the fit for lasso/elastic net, and \(\eta_R\) is the relaxed fit (with unpenalized coefficients), then a relaxed fit mixed by \(\gamma\) is $$\eta(\gamma)=(1-\gamma)\eta_R+\gamma\eta.$$ There is practically no extra cost for having a lot of values for gamma. However, 5 seems sufficient for most purposes. CV then selects both gamma and lambda.


Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent (2010), Journal of Statistical Software, Vol. 33(1), 1-22, tools:::Rd_expr_doi("10.18637/jss.v033.i01").
Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, tools:::Rd_expr_doi("10.18637/jss.v039.i05").

See Also

glmnet and plot, predict, and coef methods for "cv.glmnet" and "cv.relaxed" objects.


Run this code

n = 1000
p = 100
nzc = trunc(p/10)
x = matrix(rnorm(n * p), n, p)
beta = rnorm(nzc)
fx = x[, seq(nzc)] %*% beta
eps = rnorm(n) * 5
y = drop(fx + eps)
px = exp(fx)
px = px/(1 + px)
ly = rbinom(n = length(px), prob = px, size = 1)
cvob1 = cv.glmnet(x, y)
predict(cvob1, newx = x[1:5, ], s = "lambda.min")
title("Gaussian Family", line = 2.5)
cvob1a = cv.glmnet(x, y, type.measure = "mae")
title("Gaussian Family", line = 2.5)
par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1))
cvob2 = cv.glmnet(x, ly, family = "binomial")
title("Binomial Family", line = 2.5)
cvob3 = cv.glmnet(x, ly, family = "binomial", type.measure = "class")
title("Binomial Family", line = 2.5)
if (FALSE) {
cvob1r = cv.glmnet(x, y, relax = TRUE)
predict(cvob1r, newx = x[, 1:5])
cvob3a = cv.glmnet(x, ly, family = "binomial", type.measure = "auc")
title("Binomial Family", line = 2.5)
mu = exp(fx/10)
y = rpois(n, mu)
cvob4 = cv.glmnet(x, y, family = "poisson")
title("Poisson Family", line = 2.5)

# Multinomial
n = 500
p = 30
nzc = trunc(p/10)
x = matrix(rnorm(n * p), n, p)
beta3 = matrix(rnorm(30), 10, 3)
beta3 = rbind(beta3, matrix(0, p - 10, 3))
f3 = x %*% beta3
p3 = exp(f3)
p3 = p3/apply(p3, 1, sum)
g3 = glmnet:::rmult(p3)
cvfit = cv.glmnet(x, g3, family = "multinomial")
title("Multinomial Family", line = 2.5)
# Cox
beta = rnorm(nzc)
fx = x[, seq(nzc)] %*% beta/3
hx = exp(fx)
ty = rexp(n, hx)
tcens = rbinom(n = n, prob = 0.3, size = 1)  # censoring indicator
y = cbind(time = ty, status = 1 - tcens)  # y=Surv(ty,1-tcens) with library(survival)
foldid = sample(rep(seq(10), length = n))
fit1_cv = cv.glmnet(x, y, family = "cox", foldid = foldid)
title("Cox Family", line = 2.5)
# Parallel
registerDoMC(cores = 4)
x = matrix(rnorm(1e+05 * 100), 1e+05, 100)
y = rnorm(1e+05)
system.time(cv.glmnet(x, y))
system.time(cv.glmnet(x, y, parallel = TRUE))

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