cvFrobeniusLoss {cvCovEst} | R Documentation |
cvFrobeniusLoss()
evaluates the aggregated Frobenius loss
over a fold
object (from 'origami'
(Coyle and Hejazi 2018)).
cvFrobeniusLoss( fold, dat, estimator_funs, estimator_params = NULL, true_cov_mat = NULL )
fold |
A |
dat |
A |
estimator_funs |
An |
estimator_params |
A named |
true_cov_mat |
A |
A tibble
providing information on estimators,
their hyperparameters (if any), and their scaled Frobenius loss evaluated
on a given fold
.
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.
library(MASS) library(origami) 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)) ) )