cvCovEst()
identifies the optimal covariance matrix
estimator from among a set of candidate estimators.
cvCovEst( dat, estimators = c(linearShrinkEst, thresholdingEst, sampleCovEst), estimator_params = list(linearShrinkEst = list(alpha = 0), thresholdingEst = list(gamma = 0)), cv_loss = cvMatrixFrobeniusLoss, cv_scheme = "v_fold", mc_split = 0.5, v_folds = 10L, center = TRUE, scale = FALSE, parallel = FALSE )
dat | A numeric |
---|---|
estimators | A |
estimator_params | A named |
cv_loss | A |
cv_scheme | A |
mc_split | A |
v_folds | An |
center | A |
scale | A |
parallel | A |
A list
of results containing the following elements:
estimate
- A matrix
corresponding to the estimate of
the optimal covariance matrix estimator.
estimator
- A character
indicating the optimal
estimator and corresponding hyperparameters, if any.
risk_df
- A tibble
providing the
cross-validated risk estimates of each estimator.
cv_df
- A tibble
providing each
estimators' loss over the folds of the cross-validated procedure.
args
- A named list
containing arguments passed to
cvCovEst
.
cvCovEst( dat = mtcars, estimators = c( linearShrinkLWEst, thresholdingEst, sampleCovEst ), estimator_params = list( thresholdingEst = list(gamma = seq(0.1, 0.3, 0.1)) ), center = TRUE, scale = TRUE )#> $estimate #> mpg cyl disp hp drat wt #> mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.68117191 -0.8676594 #> cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.69993811 0.7824958 #> disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.71021393 0.8879799 #> hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.44875912 0.6587479 #> drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.00000000 -0.7124406 #> wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.71244065 1.0000000 #> qsec 0.4186840 -0.5912421 -0.4336979 -0.7082234 0.09120476 -0.1747159 #> vs 0.6640389 -0.8108118 -0.7104159 -0.7230967 0.44027846 -0.5549157 #> am 0.5998324 -0.5226070 -0.5912270 -0.2432043 0.71271113 -0.6924953 #> gear 0.4802848 -0.4926866 -0.5555692 -0.1257043 0.69961013 -0.5832870 #> carb -0.5509251 0.5269883 0.3949769 0.7498125 -0.09078980 0.4276059 #> qsec vs am gear carb #> mpg 0.41868403 0.6640389 0.59983243 0.4802848 -0.55092507 #> cyl -0.59124207 -0.8108118 -0.52260705 -0.4926866 0.52698829 #> disp -0.43369788 -0.7104159 -0.59122704 -0.5555692 0.39497686 #> hp -0.70822339 -0.7230967 -0.24320426 -0.1257043 0.74981247 #> drat 0.09120476 0.4402785 0.71271113 0.6996101 -0.09078980 #> wt -0.17471588 -0.5549157 -0.69249526 -0.5832870 0.42760594 #> qsec 1.00000000 0.7445354 -0.22986086 -0.2126822 -0.65624923 #> vs 0.74453544 1.0000000 0.16834512 0.2060233 -0.56960714 #> am -0.22986086 0.1683451 1.00000000 0.7940588 0.05753435 #> gear -0.21268223 0.2060233 0.79405876 1.0000000 0.27407284 #> carb -0.65624923 -0.5696071 0.05753435 0.2740728 1.00000000 #> #> $estimator #> [1] "sampleCovEst, hyperparameters = NA" #> #> $risk_df #> # A tibble: 5 x 3 #> estimator hyperparameters cv_risk #> <chr> <chr> <dbl> #> 1 sampleCovEst hyperparameters = NA 65.9 #> 2 thresholdingEst gamma = 0.3 66.0 #> 3 thresholdingEst gamma = 0.1 66.1 #> 4 linearShrinkLWEst hyperparameters = NA 66.2 #> 5 thresholdingEst gamma = 0.2 67.0 #> #> $cv_df #> # A tibble: 50 x 4 #> estimator hyperparameters loss fold #> <chr> <chr> <dbl> <int> #> 1 linearShrinkLWEst hyperparameters = NA 112. 1 #> 2 thresholdingEst gamma = 0.1 107. 1 #> 3 thresholdingEst gamma = 0.2 107. 1 #> 4 thresholdingEst gamma = 0.3 106. 1 #> 5 sampleCovEst hyperparameters = NA 106. 1 #> 6 linearShrinkLWEst hyperparameters = NA 21.3 2 #> 7 thresholdingEst gamma = 0.1 20.9 2 #> 8 thresholdingEst gamma = 0.2 21.3 2 #> 9 thresholdingEst gamma = 0.3 20.6 2 #> 10 sampleCovEst hyperparameters = NA 20.4 2 #> # … with 40 more rows #> #> $args #> $args$cv_loss #> <quosure> #> expr: ^cvMatrixFrobeniusLoss #> env: 0x7fa3ef508758 #> #> $args$cv_scheme #> [1] "v_fold" #> #> $args$mc_split #> [1] 0.5 #> #> $args$v_folds #> [1] 10 #> #> $args$center #> [1] TRUE #> #> $args$scale #> [1] TRUE #> #> $args$parallel #> [1] FALSE #> #> #> attr(,"class") #> [1] "cvCovEst"