cv.sparsegl: Cross-validation for a 'sparsegl' object.

In sparsegl: Sparse Group Lasso

Cross-validation for a sparsegl object.

Description

Performs k-fold cross-validation for sparsegl(). This function is largely similar glmnet::cv.glmnet().

Usage

cv.sparsegl(
  x,
  y,
  group = NULL,
  family = c("gaussian", "binomial"),
  lambda = NULL,
  pred.loss = c("default", "mse", "deviance", "mae", "misclass"),
  nfolds = 10,
  foldid = NULL,
  weights = NULL,
  offset = NULL,
  ...
)

Arguments

x	Double. A matrix of predictors, of dimension n \times p; each row is a vector of measurements and each column is a feature. Objects of class Matrix::sparseMatrix are supported.
y	Double/Integer/Factor. The response variable. Quantitative for family="gaussian" and for other exponential families. If family="binomial" should be either a factor with two levels or a vector of integers taking 2 unique values. For a factor, the last level in alphabetical order is the target class.
group	Integer. A vector of consecutive integers describing the grouping of the coefficients (see example below).
family	Character or function. Specifies the generalized linear model to use. Valid options are: "gaussian" - least squares loss (regression, the default), "binomial" - logistic loss (classification) For any other type, a valid stats::family() object may be passed. Note that these will generally be much slower to estimate than the built-in options passed as strings. So for example, family = "gaussian" and family = gaussian() will produce the same results, but the first will be much faster.
lambda	A user supplied lambda sequence. The default, NULL results in an automatic computation based on nlambda, the smallest value of lambda that would give the null model (all coefficient estimates equal to zero), and lambda.factor. Supplying a value of lambda overrides this behaviour. It is likely better to supply a decreasing sequence of lambda values than a single (small) value. If supplied, the user-defined lambda sequence is automatically sorted in decreasing order.
pred.loss	Loss to use for cross-validation error. Valid options are: "default" the same as deviance (mse for regression and deviance otherwise) "mse" mean square error "deviance" the default (mse for Gaussian regression, and negative log-likelihood otherwise) "mae" mean absolute error, can apply to any family "misclass" for classification only, misclassification error.
nfolds	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.
foldid	An optional vector of values between 1 and nfolds identifying which fold each observation is in. If supplied, nfolds can be missing.
weights	Double vector. Optional observation weights. These can only be used with a stats::family() object.
offset	Double vector. Optional offset (constant predictor without a corresponding coefficient). These can only be used with a stats::family() object.
...	Additional arguments to sparsegl().

Details

The function runs sparsegl() 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 average error and standard error over the folds are computed.

Value

An object of class cv.sparsegl() is returned, which is a list with the components describing the cross-validation error.

lambda	The values of lambda used in the fits.
cvm	The mean cross-validated error - a vector of length length(lambda).
cvsd	Estimate of standard error of cvm.
cvupper	Upper curve = cvm + cvsd.
cvlower	Lower curve = cvm - cvsd.
name	A text string indicating type of measure (for plotting purposes).
nnzero	The number of non-zero coefficients for each lambda
active_grps	The number of active groups for each lambda
sparsegl.fit	A fitted sparsegl() object for the full data.
lambda.min	The optimal value of lambda that gives minimum cross validation error cvm.
lambda.1se	The largest value of lambda such that error is within 1 standard error of the minimum.
call	The function call.

References

Liang, X., Cohen, A., Sólon Heinsfeld, A., Pestilli, F., and McDonald, D.J. 2024. sparsegl: An R Package for Estimating Sparse Group Lasso. Journal of Statistical Software, Vol. 110(6): 1–23. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v110.i06")}.

See Also

sparsegl(), as well as plot(), predict(), and coef() methods for "cv.sparsegl" objects.

Examples

n <- 100
p <- 20
X <- matrix(rnorm(n * p), nrow = n)
eps <- rnorm(n)
beta_star <- c(rep(5, 5), c(5, -5, 2, 0, 0), rep(-5, 5), rep(0, (p - 15)))
y <- X %*% beta_star + eps
groups <- rep(1:(p / 5), each = 5)
cv_fit <- cv.sparsegl(X, y, groups)

sparsegl documentation built on Sept. 11, 2024, 7:23 p.m.