Skip to contents

Regularization paths for sparse group-lasso models

Source: R/sparsegl.R
sparsegl.Rd

Fits regularization paths for sparse group-lasso penalized learning problems at a sequence of regularization parameters lambda. Note that the objective function for least squares is $$RSS/(2n) + \lambda penalty$$ Users can also tweak the penalty by choosing a different penalty factor.

Usage

sparsegl(
  x,
  y,
  group = NULL,
  family = c("gaussian", "binomial"),
  nlambda = 100,
  lambda.factor = ifelse(nobs < nvars, 0.01, 1e-04),
  lambda = NULL,
  pf_group = sqrt(bs),
  pf_sparse = rep(1, nvars),
  intercept = TRUE,
  asparse = 0.05,
  standardize = TRUE,
  lower_bnd = -Inf,
  upper_bnd = Inf,
  weights = NULL,
  offset = NULL,
  warm = NULL,
  trace_it = 0,
  dfmax = as.integer(max(group)) + 1L,
  pmax = min(dfmax * 1.2, as.integer(max(group))),
  eps = 1e-08,
  maxit = 3e+06
)

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.

nlambda

The number of lambda values - default is 100.

lambda.factor

A multiplicative factor for the minimal lambda in the lambda sequence, where min(lambda) = lambda.factor * max(lambda). max(lambda) is the smallest value of lambda for which all coefficients are zero. The default depends on the relationship between \(n\) (the number of rows in the matrix of predictors) and \(p\) (the number of predictors). If \(n \geq p\), the default is 0.0001. If \(n < p\), the default is 0.01. A very small value of lambda.factor will lead to a saturated fit. This argument has no effect if there is user-defined lambda sequence.

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.

pf_group

Penalty factor on the groups, a vector of the same length as the total number of groups. Separate penalty weights can be applied to each group of \(\beta\)s to allow differential shrinkage. Can be 0 for some groups, which implies no shrinkage, and results in that group always being included in the model (depending on pf_sparse). Default value for each entry is the square-root of the corresponding size of each group. Because this default is typical, these penalties are not rescaled.

pf_sparse

Penalty factor on l1-norm, a vector the same length as the total number of columns in x. Each value corresponds to one predictor Can be 0 for some predictors, which implies that predictor will be receive only the group penalty. Note that these are internally rescaled so that the sum is the same as the number of predictors.

intercept

Whether to include intercept in the model. Default is TRUE.

asparse

The relative weight to put on the \(\ell_1\)-norm in sparse group lasso. Default is 0.05 (resulting in 0.95 on the \(\ell_2\)-norm).

standardize

Logical flag for variable standardization (scaling) prior to fitting the model. Default is TRUE.

lower_bnd

Lower bound for coefficient values, a vector in length of 1 or of length the number of groups. Must be non-positive numbers only. Default value for each entry is -Inf.

upper_bnd

Upper for coefficient values, a vector in length of 1 or of length the number of groups. Must be non-negative numbers only. Default value for each entry is Inf.

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.

warm

List created with make_irls_warmup(). These can only be used with a stats::family() object, and is not typically necessary even then.

trace_it

Scalar integer. Larger values print more output during the irls loop. Typical values are 0 (no printing), 1 (some printing and a progress bar), and 2 (more detailed printing). These can only be used with a stats::family() object.

dfmax

Limit the maximum number of groups in the model. Default is no limit.

pmax

Limit the maximum number of groups ever to be nonzero. For example once a group enters the model, no matter how many times it exits or re-enters model through the path, it will be counted only once.

eps

Convergence termination tolerance. Defaults value is 1e-8.

maxit

Maximum number of outer-loop iterations allowed at fixed lambda value. Default is 3e8. If models do not converge, consider increasing maxit.

Value

An object with S3 class "sparsegl". Among the list components:

  • call The call that produced this object.

  • b0 Intercept sequence of length length(lambda).

  • beta A p x length(lambda) sparse matrix of coefficients.

  • df The number of features with nonzero coefficients for each value of lambda.

  • dim Dimension of coefficient matrix.

  • lambda The actual sequence of lambda values used.

  • npasses Total number of iterations summed over all lambda values.

  • jerr Error flag, for warnings and errors, 0 if no error.

  • group A vector of consecutive integers describing the grouping of the coefficients.

  • nobs The number of observations used to estimate the model.

If sparsegl() was called with a stats::family() method, this may also contain information about the deviance and the family used in fitting.

See also

cv.sparsegl() and the plot(), predict(), and coef() methods for "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)
fit <- sparsegl(X, y, group = groups)

yp <- rpois(n, abs(X %*% beta_star))
fit_pois <- sparsegl(X, yp, group = groups, family = poisson())