regmest: Compute (Adaptive) Elastic Net M-Estimates of Regression

Description

Compute elastic net M-estimates along a grid of penalization levels with optional penalty loadings for adaptive elastic net.

Usage

regmest(
  x,
  y,
  alpha,
  nlambda = 50,
  lambda,
  lambda_min_ratio,
  scale,
  starting_points,
  penalty_loadings,
  intercept = TRUE,
  eff = 0.9,
  rho = "mopt",
  cc,
  eps = 1e-06,
  explore_solutions = 10,
  explore_tol = 0.1,
  max_solutions = 1,
  comparison_tol = sqrt(eps),
  sparse = FALSE,
  ncores = 1,
  standardize = TRUE,
  algorithm_opts = mm_algorithm_options(),
  add_zero_based = TRUE,
  mscale_bdp = 0.25,
  mscale_opts = mscale_algorithm_options()
)

Value

a list-like object with the following items

alpha

the sequence of alpha parameters.

lambda

a list of sequences of penalization levels, one per alpha parameter.

scale

the used scale of the residuals.

estimates

a list of estimates. Each estimate contains the following information:

intercept: intercept estimate.

beta

beta (slope) estimate.

lambda

penalization level at which the estimate is computed.

alpha

alpha hyper-parameter at which the estimate is computed.

objf_value

value of the objective function at the solution.

statuscode

if > 0 the algorithm experienced issues when computing the estimate.

status

optional status message from the algorithm.

call

the original call.

Arguments

x: n by p matrix of numeric predictors.
y: vector of response values of length n. For binary classification, y should be a factor with 2 levels.
alpha: elastic net penalty mixing parameter with \(0 \le \alpha \le 1\). alpha = 1 is the LASSO penalty, and alpha = 0 the Ridge penalty.
nlambda: number of penalization levels.
lambda: optional user-supplied sequence of penalization levels. If given and not NULL, nlambda and lambda_min_ratio are ignored.
lambda_min_ratio: Smallest value of the penalization level as a fraction of the largest level (i.e., the smallest value for which all coefficients are zero). The default depends on the sample size relative to the number of variables and alpha. If more observations than variables are available, the default is 1e-3 * alpha, otherwise 1e-2 * alpha.
scale: fixed scale of the residuals.
starting_points: a list of staring points, created by starting_point(). The starting points are shared among all penalization levels.
penalty_loadings: a vector of positive penalty loadings (a.k.a. weights) for different penalization of each coefficient. Only allowed for alpha > 0.
intercept: include an intercept in the model.
eff: the desired asymptotic efficiency of the M-estimator under the Normal model.
rho: which \(\rho\) function to use (see rho_function() for the list of supported options).
cc: manually specified cutoff constant for the chosen \(\rho\) function. If specified, overrides the eff argument.
eps: numerical tolerance.
explore_solutions: number of solutions to compute up to the desired precision eps.
explore_tol: numerical tolerance for exploring possible solutions. Should be (much) looser than eps to be useful.
max_solutions: only retain up to max_solutions unique solutions per penalization level.
comparison_tol: numeric tolerance to determine if two solutions are equal. The comparison is first done on the absolute difference in the value of the objective function at the solution. If this is less than comparison_tol, two solutions are deemed equal if the squared difference of the intercepts is less than comparison_tol and the squared \(L_2\) norm of the difference vector is less than comparison_tol.
sparse: use sparse coefficient vectors.
ncores: number of CPU cores to use in parallel. By default, only one CPU core is used. Not supported on all platforms, in which case a warning is given.
standardize: logical flag to standardize the x variables prior to fitting the M-estimates. Coefficients are always returned on the original scale. This can fail for variables with a large proportion of a single value (e.g., zero-inflated data). In this case, either compute with standardize = FALSE or standardize the data manually.
algorithm_opts: options for the MM algorithm to compute estimates. See mm_algorithm_options() for details.
add_zero_based: also consider the 0-based regularization path in addition to the given starting points.
mscale_bdp, mscale_opts: options for the M-scale estimate used to standardize the predictors (if standardize = TRUE).

Examples

Run this code

# Compute the PENSE regularization path for Freeny's revenue data
# (see ?freeny)
data(freeny)
x <- as.matrix(freeny[ , 2:5])

regpath <- regmest(x, freeny$y, alpha = c(0.5, 0.85), scale = 2)
plot(regpath)

# Extract the coefficients at a certain penalization level
coef(regpath, alpha = 0.85, lambda = regpath$lambda[[2]][[40]])

# What penalization level leads to good prediction performance?
set.seed(123)
cv_results <- regmest_cv(x, freeny$y, alpha = c(0.5, 0.85), scale = 2,
                         cv_repl = 2, cv_k = 4)
plot(cv_results, se_mult = 1)

# Print a summary of the fit and the cross-validation results.
summary(cv_results)

# Extract the coefficients at the penalization level with
# smallest prediction error ...
coef(cv_results)
# ... or at the penalization level with prediction error
# statistically indistinguishable from the minimum.
coef(cv_results, lambda = '1-se')

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Description

Usage

Value

Arguments

See Also

Examples