cv.grpnet: Cross-validation for grpnet

In adelie: Group Lasso and Elastic Net Solver for Generalized Linear Models

Cross-validation for grpnet

Description

Does k-fold cross-validation for grpnet

Usage

cv.grpnet(
  X,
  glm,
  n_folds = 10,
  foldid = NULL,
  min_ratio = 0.01,
  lmda_path_size = 100,
  offsets = NULL,
  progress_bar = FALSE,
  n_threads = 1,
  ...
)

Arguments

X	Feature matrix. Either a regualr R matrix, or else an adelie custom matrix class, or a concatination of such.
glm	GLM family/response object. This is an expression that represents the family, the reponse and other arguments such as weights, if present. The choices are glm.gaussian(), glm.binomial(), glm.poisson(), glm.multinomial(), glm.cox(), glm.multinomial(), and glm.multigaussian(). This is a required argument, and there is no default. In the simple example below, we use glm.gaussian(y).
n_folds	(default 10). Although n_folds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is n_folds=3.
foldid	An optional vector of values between 1 and n_folds identifying what fold each observation is in. If supplied, n_folds can be missing.
min_ratio	Ratio between smallest and largest value of lambda. Default is 1e-2.
lmda_path_size	Number of values for lambda, if generated automatically. Default is 100.
offsets	Offsets, default is NULL. If present, this is a fixed vector or matrix corresponding to the shape of the natural parameter, and is added to the fit.
progress_bar	Progress bar. Default is FALSE.
n_threads	Number of threads, default 1.
...	Other arguments that can be passed to grpnet

Details

The function runs grpnet n_folds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The out-of-fold deviance is accumulated, and the average deviance and standard deviation over the folds is computed. Note that cv.grpnet 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.grpnet with a pre-computed vector foldid, and then use this same foldid vector in separate calls to cv.grpnet with different values of alpha. Note also that the results of cv.grpnet are random, since the folds are selected at random. Users can reduce this randomness by running cv.grpnet many times, and averaging the error curves.

Value

an object of class "cv.grpnet" is returned, which is a list with the ingredients of the cross-validation fit.

lambda	the values of lambda used in the fits.
cvm	The mean cross-validated deviance - a vector of length length(lambda).
cvsd	estimate of standard error of cvm.
cvup	upper curve = cvm+cvsd.
cvlo	lower curve = cvm-cvsd.
nzero	number of non-zero coefficients at each lambda.
name	a text string indicating type of measure (for plotting purposes). Currently this is "deviance"
grpnet.fit	a fitted grpnet object for the full data.
lambda.min	value of lambda that gives minimum cvm.
lambda.1se	largest value of lambda such that mean deviance is within 1 standard error of the minimum.
index	a one column matrix with the indices of lambda.min and lambda.1se in the sequence of coefficients, fits etc.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [e-mail]

References

Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2405.08631")}.
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, \Sexpr[results=rd]{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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v039.i05")}.
Tibshirani,Robert, Bien, J., Friedman, J., Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2012) Strong Rules for Discarding Predictors in Lasso-type Problems, JRSSB, Vol. 74(2), 245-266, https://arxiv.org/abs/1011.2234.

Examples

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
fit <- grpnet(X, glm.gaussian(y))
print(fit)

adelie documentation built on Sept. 11, 2024, 6:36 p.m.