cvFrobeniusLoss() evaluates the aggregated Frobenius loss over a fold object (from 'origami' (Coyle and Hejazi 2018) ).

cvFrobeniusLoss(fold, dat, estimator_funs, estimator_params = NULL)

Arguments

fold

A fold object (from make_folds()) over which the estimation procedure is to be performed.

dat

A data.frame containing the full (non-sample-split) data, on which the cross-validated procedure is performed.

estimator_funs

An expression corresponding to a vector of covariance matrix estimator functions to be applied to the training data.

estimator_params

A named list of arguments corresponding to the hyperparameters of covariance matrix estimators, estimator_funs. The name of each list element should be the name of an estimator passed to estimator_funs. Each element of the estimator_params is itself a named list, with names corresponding to an estimators' hyperparameter(s). These hyperparameters may be in the form of a single numeric or a numeric vector. If no hyperparameter is needed for a given estimator, then the estimator need not be listed.

Value

A tibble providing information on estimators, their hyperparameters (if any), and their scaled Frobenius loss evaluated on a given fold.

References

Coyle J, Hejazi N (2018). “origami: A Generalized Framework for Cross-Validation in R.” Journal of Open Source Software, 3(21), 512. doi: 10.21105/joss.00512 , https://doi.org/10.21105/joss.00512.

Examples

#> origami v1.0.3: Generalized Framework for Cross-Validation
library(rlang) # generate 10x10 covariance matrix with unit variances and off-diagonal # elements equal to 0.5 Sigma <- matrix(0.5, nrow = 10, ncol = 10) + diag(0.5, nrow = 10) # sample 50 observations from multivariate normal with mean = 0, var = Sigma dat <- mvrnorm(n = 50, mu = rep(0, 10), Sigma = Sigma) # generate a single fold using MC-cv resub <- make_folds(dat, fold_fun = folds_vfold, V = 2 )[[1]] cvFrobeniusLoss( fold = resub, dat = dat, estimator_funs = rlang::quo(c( linearShrinkEst, thresholdingEst, sampleCovEst )), estimator_params = list( linearShrinkEst = list(alpha = c(0, 1)), thresholdingEst = list(gamma = c(0, 1)) ) )
#> [[1]] #> # A tibble: 5 x 4 #> estimator hyperparameters loss fold #> <chr> <chr> <dbl> <int> #> 1 linearShrinkEst alpha = 0 55.2 1 #> 2 linearShrinkEst alpha = 1 64.5 1 #> 3 thresholdingEst gamma = 0 64.5 1 #> 4 thresholdingEst gamma = 1 58.5 1 #> 5 sampleCovEst hyperparameters = NA 64.5 1 #>