pense_cv: Cross-validation for (Adaptive) PENSE Estimates

In pense: Penalized Elastic Net S/MM-Estimator of Regression

Cross-validation for (Adaptive) PENSE Estimates

Description

Perform (repeated) K-fold cross-validation for pense().

adapense_cv() is a convenience wrapper to compute adaptive PENSE estimates.

Usage

pense_cv(
  x,
  y,
  standardize = TRUE,
  lambda,
  cv_k,
  cv_repl = 1,
  cv_metric = c("tau_size", "mape", "rmspe", "auroc"),
  fit_all = TRUE,
  fold_starts = c("full", "enpy", "both"),
  cl = NULL,
  ...
)

adapense_cv(x, y, alpha, alpha_preliminary = 0, exponent = 1, ...)

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.
standardize	whether to standardize the x variables prior to fitting the PENSE estimates. Can also be set to "cv_only", in which case the input data is not standardized, but the training data in the CV folds is scaled to match the scaling of the input data. 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.
lambda	optional user-supplied sequence of penalization levels. If given and not NULL, nlambda and lambda_min_ratio are ignored.
cv_k	number of folds per cross-validation.
cv_repl	number of cross-validation replications.
cv_metric	either a string specifying the performance metric to use, or a function to evaluate prediction errors in a single CV replication. If a function, the number of arguments define the data the function receives. If the function takes a single argument, it is called with a single numeric vector of prediction errors. If the function takes two or more arguments, it is called with the predicted values as first argument and the true values as second argument. The function must always return a single numeric value quantifying the prediction performance. The order of the given values corresponds to the order in the input data.
fit_all	If TRUE, fit the model for all penalization levels. Can also be any combination of "min" and "{x}-se", in which case only models at the penalization level with smallest average CV accuracy, or within {x} standard errors, respectively. Setting fit_all to FALSE is equivalent to "min". Applies to all alpha value.
fold_starts	how to determine starting values in the cross-validation folds. If "full" (default), use the best solution from the fit to the full data as starting value. This implies fit_all=TRUE. If "enpy" compute separate ENPY initial estimates in each fold. The option "both" uses both. These starts are in addition to the starts provided in other_starts.
cl	a parallel cluster. Can only be used in combination with ncores = 1.
...	Arguments passed on to pense nlambda number of penalization levels. 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. nlambda_enpy number of penalization levels where the EN-PY initial estimate is computed. penalty_loadings a vector of positive penalty loadings (a.k.a. weights) for different penalization of each coefficient. Only allowed for alpha > 0. enpy_lambda optional user-supplied sequence of penalization levels at which EN-PY initial estimates are computed. If given and not NULL, nlambda_enpy is ignored. other_starts a list of other staring points, created by starting_point(). If the output of enpy_initial_estimates() is given, the starting points will be shared among all penalization levels. Note that if a the starting point is specific to a penalization level, this penalization level is added to the grid of penalization levels (either the manually specified grid in lambda or the automatically generated grid of size nlambda). If standardize = TRUE, the starting points are also scaled. intercept include an intercept in the model. bdp desired breakdown point of the estimator, between 0.05 and 0.5. The actual breakdown point may be slightly larger/smaller to avoid instabilities of the S-loss. cc tuning constant for the S-estimator. Default is chosen based on the breakdown point bdp. This affects the estimated coefficients only if standardize=TRUE. Otherwise only the estimated scale of the residuals would be affected. eps numerical tolerance. explore_solutions number of solutions to compute up to the desired precision eps. explore_tol,explore_it numerical tolerance and maximum number of iterations for exploring possible solutions. The tolerance should be (much) looser than eps to be useful, and the number of iterations should also be much smaller than the maximum number of iterations given via algorithm_opts. 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. add_zero_based also consider the 0-based regularization path. See details for a description. enpy_specific use the EN-PY initial estimates only at the penalization level they are computed for. See details for a description. carry_forward carry the best solutions forward to the next penalty level. 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. algorithm_opts options for the MM algorithm to compute the estimates. See mm_algorithm_options() for details. mscale_opts options for the M-scale estimation. See mscale_algorithm_options() for details. enpy_opts options for the ENPY initial estimates, created with the enpy_options() function. See enpy_initial_estimates() for details. cv_k,cv_objective deprecated and ignored. See pense_cv() for estimating prediction performance via cross-validation.
alpha	elastic net penalty mixing parameter with 0 ≤ α ≤ 1. alpha = 1 is the LASSO penalty, and alpha = 0 the Ridge penalty. Can be a vector of several values, but alpha = 0 cannot be mixed with other values.
alpha_preliminary	alpha parameter for the preliminary estimate.
exponent	the exponent for computing the penalty loadings based on the preliminary estimate.

Details

The built-in CV metrics are

"tau_size"

τ-size of the prediction error, computed by tau_size() (default).

"mape"

Median absolute prediction error.

"rmspe"

Root mean squared prediction error.

"auroc"

Area under the receiver operator characteristic curve (actually 1 - AUROC). Only sensible for binary responses.

adapense_cv() is a convenience wrapper which performs 3 steps:

1. compute preliminary estimates via pense_cv(..., alpha = alpha_preliminary),

2. computes the penalty loadings from the estimate beta with best prediction performance by adapense_loadings = 1 / abs(beta)^exponent, and

3. compute the adaptive PENSE estimates via pense_cv(..., penalty_loadings = adapense_loadings).

Value

a list-like object with the same components as returned by pense(), plus the following:

cvres

data frame of average cross-validated performance.

a list-like object as returned by pense_cv() plus the following

preliminary

the CV results for the preliminary estimate.

exponent

exponent used to compute the penalty loadings.

penalty_loadings

penalty loadings used for the adaptive PENSE estimate.

See Also

pense() for computing regularized S-estimates without cross-validation.

coef.pense_cvfit() for extracting coefficient estimates.

plot.pense_cvfit() for plotting the CV performance or the regularization path.

Other functions to compute robust estimates with CV: pensem_cv(), regmest_cv()

Other functions to compute robust estimates with CV: pensem_cv(), regmest_cv()

Examples

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

## Either use the convenience function directly ...
set.seed(123)
ada_convenience <- adapense_cv(x, freeny$y, alpha = 0.5,
                               cv_repl = 2, cv_k = 4)

## ... or compute the steps manually:
# Step 1: Compute preliminary estimates with CV
set.seed(123)
preliminary_estimate <- pense_cv(x, freeny$y, alpha = 0,
                                 cv_repl = 2, cv_k = 4)
plot(preliminary_estimate, se_mult = 1)

# Step 2: Use the coefficients with best prediction performance
# to define the penalty loadings:
prelim_coefs <- coef(preliminary_estimate, lambda = 'min')
pen_loadings <- 1 / abs(prelim_coefs[-1])

# Step 3: Compute the adaptive PENSE estimates and estimate
# their prediction performance.
set.seed(123)
ada_manual <- pense_cv(x, freeny$y, alpha = 0.5,
                       cv_repl = 2, cv_k = 4,
                       penalty_loadings = pen_loadings)

# Visualize the prediction performance and coefficient path of
# the adaptive PENSE estimates (manual vs. automatic)
def.par <- par(no.readonly = TRUE)
layout(matrix(1:4, ncol = 2, byrow = TRUE))
plot(ada_convenience$preliminary)
plot(preliminary_estimate)
plot(ada_convenience)
plot(ada_manual)
par(def.par)

pense documentation built on Feb. 16, 2023, 9:36 p.m.