Predictions using Short-Stacking.

Predictions using short-stacking.

Usage

shortstacking(
  y,
  X,
  Z = NULL,
  learners,
  sample_folds = 2,
  ensemble_type = "average",
  custom_ensemble_weights = NULL,
  compute_insample_predictions = FALSE,
  subsamples = NULL,
  silent = FALSE,
  progress = NULL,
  auxiliary_X = NULL,
  shortstack_y = y
)

Arguments

y

The outcome variable.

X

A (sparse) matrix of predictive variables.

Z

Optional additional (sparse) matrix of predictive variables.

learners

May take one of two forms, depending on whether a single learner or stacking with multiple learners is used for estimation of the predictor. If a single learner is used, learners is a list with two named elements:

what The base learner function. The function must be such that it predicts a named input y using a named input X.
args Optional arguments to be passed to what.

If stacking with multiple learners is used, learners is a list of lists, each containing four named elements:

fun The base learner function. The function must be such that it predicts a named input y using a named input X.
args Optional arguments to be passed to fun.
assign_X An optional vector of column indices corresponding to predictive variables in X that are passed to the base learner.
assign_Z An optional vector of column indices corresponding to predictive in Z that are passed to the base learner.

Omission of the args element results in default arguments being used in fun. Omission of assign_X (and/or assign_Z) results in inclusion of all variables in X (and/or Z).

sample_folds

Number of cross-fitting folds.

ensemble_type

Ensemble method to combine base learners into final estimate of the conditional expectation functions. Possible values are:

"nnls" Non-negative least squares.
"nnls1" Non-negative least squares with the constraint that all weights sum to one.
"singlebest" Select base learner with minimum MSPE.
"ols" Ordinary least squares.
"average" Simple average over base learners.

Multiple ensemble types may be passed as a vector of strings.

custom_ensemble_weights

A numerical matrix with user-specified ensemble weights. Each column corresponds to a custom ensemble specification, each row corresponds to a base learner in learners (in chronological order). Optional column names are used to name the estimation results corresponding the custom ensemble specification.

compute_insample_predictions

Indicator equal to 1 if in-sample predictions should also be computed.

subsamples

List of vectors with sample indices for cross-fitting.

silent

Boolean to silence estimation updates.

progress

String to print before learner and cv fold progress.

auxiliary_X

An optional list of matrices of length sample_folds, each containing additional observations to calculate predictions for.

shortstack_y

Optional vector of the outcome variable to form short-stacking predictions for. Base learners are always trained on y.

Value

shortstack returns a list containing the following components:

oos_fitted: A matrix of out-of-sample predictions, each column corresponding to an ensemble type (in chronological order).
weights: An array, providing the weight assigned to each base learner (in chronological order) by the ensemble procedures.
is_fitted: When compute_insample_predictions = T. a list of matrices with in-sample predictions by sample fold.
auxiliary_fitted: When auxiliary_X is not NULL, a list of matrices with additional predictions.
oos_fitted_bylearner: A matrix of out-of-sample predictions, each column corresponding to a base learner (in chronological order).
is_fitted_bylearner: When compute_insample_predictions = T, a list of matrices with in-sample predictions by sample fold.
auxiliary_fitted_bylearner: When auxiliary_X is not NULL, a list of matrices with additional predictions for each learner.

Note that unlike crosspred, shortstack always computes out-of-sample predictions for each base learner (at no additional computational cost).

References

Ahrens A, Hansen C B, Schaffer M E, Wiemann T (2023). "ddml: Double/debiased machine learning in Stata." https://arxiv.org/abs/2301.09397

Wolpert D H (1992). "Stacked generalization." Neural Networks, 5(2), 241-259.

Examples

# Construct variables from the included Angrist & Evans (1998) data
y = AE98[, "worked"]
X = AE98[, c("morekids", "age","agefst","black","hisp","othrace","educ")]

# Compute predictions using shortstacking with base learners ols and lasso.
#     Two stacking approaches are simultaneously computed: Equally
#     weighted (ensemble_type = "average") and MSPE-minimizing with weights
#     in the unit simplex (ensemble_type = "nnls1"). Predictions for each
#     learner are also calculated.
shortstack_res <- shortstacking(y, X,
                                learners = list(list(fun = ols),
                                                list(fun = mdl_glmnet)),
                                ensemble_type = c("average",
                                                  "nnls1",
                                                  "singlebest"),
                                sample_folds = 2,
                                silent = TRUE)
dim(shortstack_res$oos_fitted) # = length(y) by length(ensemble_type)
#> [1] 5000    3
dim(shortstack_res$oos_fitted_bylearner) # = length(y) by length(learners)
#> [1] 5000    2

Usage

Arguments

Value

References

See also

Examples