crossval function

Estimator of the Mean Squared Prediction Error using Cross-Validation.

Estimator of the mean squared prediction error of different learners using cross-validation.

crossval(
  y,
  X,
  Z = NULL,
  learners,
  cv_folds = 5,
  cv_subsamples = NULL,
  silent = FALSE,
  progress = NULL
)

Arguments

• y: The outcome variable.

• X: A (sparse) matrix of predictive variables.

• Z: Optional additional (sparse) matrix of predictive variables.

• learners: 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 variables in X that are passed to the base learner.
  • assign_Z An optional vector of column indices corresponding to variables 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 predictive variables in X (and/or Z).

• cv_folds: Number of folds used for cross-validation.

• cv_subsamples: List of vectors with sample indices for cross-validation.

• silent: Boolean to silence estimation updates.

• progress: String to print before learner and cv fold progress.

Returns

crossval returns a list containing the following components:

• mspe: A vector of MSPE estimates, each corresponding to a base learners (in chronological order).
• oos_resid: A matrix of out-of-sample prediction errors, each column corresponding to a base learners (in chronological order).
• cv_subsamples: Pass-through of cv_subsamples. See above.

Examples

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

# Compare ols, lasso, and ridge using 4-fold cross-validation
cv_res <- crossval(y, X,
                   learners = list(list(fun = ols),
                                   list(fun = mdl_glmnet),
                                   list(fun = mdl_glmnet,
                                        args = list(alpha = 0))),
                   cv_folds = 4,
                   silent = TRUE)
cv_res$mspe

See Also

Other utilities: crosspred(), shortstacking()