Select optimal penalty parameter by leave-one-out cross-validation

This function is now deprecated. Please use optPenalty.kCV instead.

optPenalty.LOOCV(
  Y,
  lambdaMin,
  lambdaMax,
  step,
  type = "Alt",
  cor = FALSE,
  target = default.target(covML(Y)),
  output = "light",
  graph = TRUE,
  verbose = TRUE
)

Arguments

Y: Data matrix. Variables assumed to be represented by columns.
lambdaMin: A numeric giving the minimum value for the penalty parameter.
lambdaMax: A numeric giving the maximum value for the penalty parameter.
step: An integer determining the number of steps in moving through the grid [lambdaMin, lambdaMax].
type: A character indicating the type of ridge estimator to be used. Must be one of: "Alt", "ArchI", "ArchII".
cor: A logical indicating if the evaluation of the LOOCV score should be performed on the correlation scale.
target: A target matrix (in precision terms) for Type I ridge estimators.
output: A character indicating if the output is either heavy or light. Must be one of: "all", "light".
graph: A logical indicating if the grid search for the optimal penalty parameter should be visualized.
verbose: A logical indicating if information on progress should be printed on screen.

Value

An object of class list:

optLambda: A numeric giving the optimal value of the penalty parameter.
optPrec: A matrix representing the precision matrix of the chosen type (see ridgeP) under the optimal value of the penalty parameter.
lambdas: A numeric vector representing all values of the penalty parameter for which cross-validation was performed; Only given when output = "all".
LLs: A numeric vector representing the mean of cross-validated negative log-likelihoods for each value of the penalty parameter given in lambdas; Only given when output = "all".

Details

Function that selects the optimal penalty parameter for the ridgeP call by usage of leave-one-out cross-validation. Its output includes (a.o.) the precision matrix under the optimal value of the penalty parameter.

The function calculates a cross-validated negative log-likelihood score (using a regularized ridge estimator for the precision matrix) for each value of the penalty parameter contained in the search grid by way of leave-one-out cross-validation. The value of the penalty parameter that achieves the lowest cross-validated negative log-likelihood score is deemed optimal. The penalty parameter must be positive such that lambdaMin must be a positive scalar. The maximum allowable value of lambdaMax depends on the type of ridge estimator employed. For details on the type of ridge estimator one may use (one of: "Alt", "ArchI", "ArchII") see ridgeP. The ouput consists of an object of class list (see below). When output = "light" (default) only the optLambda and optPrec elements of the list are given.

Note

When cor = TRUE correlation matrices are used in the computation of the (cross-validated) negative log-likelihood score, i.e., the leave-one-out sample covariance matrix is a matrix on the correlation scale. When performing evaluation on the correlation scale the data are assumed to be standardized. If cor = TRUE and one wishes to used the default target specification one may consider using target = default.target(covML(Y, cor = TRUE)). This gives a default target under the assumption of standardized data.

Author

Carel F.W. Peeters <carel.peeters@wur.nl>, Wessel N. van Wieringen

Examples


## Obtain some (high-dimensional) data
p = 25
n = 10
set.seed(333)
X = matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X)[1:25] = letters[1:25]

## Obtain regularized precision under optimal penalty
OPT  <- optPenalty.LOOCV(X, lambdaMin = .5, lambdaMax = 30, step = 100); OPT
#> Perform input checks... 
#> Calculating cross-validated negative log-likelihoods...
#> lambda = 0.5 done 
#> lambda = 0.521112064796442 done 
#> lambda = 0.543115568152822 done 
#> lambda = 0.56604815028642 done 
#> lambda = 0.589949040739926 done 
#> lambda = 0.614859125489326 done 
#> lambda = 0.640821016885354 done 
#> lambda = 0.667879126548165 done 
#> lambda = 0.696079741339917 done 
#> lambda = 0.725471102545235 done 
#> lambda = 0.756103488394997 done 
#> lambda = 0.788029300074619 done 
#> lambda = 0.821303151363959 done 
#> lambda = 0.855981962062195 done 
#> lambda = 0.89212505535748 done 
#> lambda = 0.929794259307953 done 
#> lambda = 0.969054012607691 done 
#> lambda = 1.00997147481854 done 
#> lambda = 1.0526166412564 done 
#> lambda = 1.09706246272843 done 
#> lambda = 1.14338497032617 done 
#> lambda = 1.19166340548777 done 
#> lambda = 1.24198035555219 done 
#> lambda = 1.29442189503684 done 
#> lambda = 1.34907773288074 done 
#> lambda = 1.40604136590477 done 
#> lambda = 1.46541023875169 done 
#> lambda = 1.52728591057948 done 
#> lambda = 1.59177422879317 done 
#> lambda = 1.65898551011235 done 
#> lambda = 1.72903472928405 done 
#> lambda = 1.80204171576394 done 
#> lambda = 1.87813135870213 done 
#> lambda = 1.95743382058443 done 
#> lambda = 2.04008475989428 done 
#> lambda = 2.12622556317653 done 
#> lambda = 2.21600358689979 done 
#> lambda = 2.30957240953135 done 
#> lambda = 2.40709209425555 done 
#> lambda = 2.5087294627854 done 
#> lambda = 2.61465838073554 done 
#> lambda = 2.72506005504483 done 
#> lambda = 2.84012334395744 done 
#> lambda = 2.96004508009247 done 
#> lambda = 3.08503040715507 done 
#> lambda = 3.21529313086478 done 
#> lambda = 3.35105608470152 done 
#> lambda = 3.49255151109498 done 
#> lambda = 3.64002145870927 done 
#> lambda = 3.79371819650269 done 
#> lambda = 3.9539046452707 done 
#> lambda = 4.12085482741052 done 
#> lambda = 4.29485433567656 done 
#> lambda = 4.47620082172873 done 
#> lambda = 4.66520450530918 done 
#> lambda = 4.86218870491866 done 
#> lambda = 5.0674903909002 done 
#> lambda = 5.28146076187626 done 
#> lambda = 5.50446584552545 done 
#> lambda = 5.73688712472652 done 
#> lambda = 5.97912219014072 done 
#> lambda = 6.23158542034891 done 
#> lambda = 6.49470869070685 done 
#> lambda = 6.76894211213128 done 
#> lambda = 7.05475480108065 done 
#> lambda = 7.3526356820475 done 
#> lambda = 7.66309432393553 done 
#> lambda = 7.98666181175188 done 
#> lambda = 8.32389165510583 done 
#> lambda = 8.67536073506814 done 
#> lambda = 9.04167029101067 done 
#> lambda = 9.42344694911443 done 
#> lambda = 9.8213437943055 done 
#> lambda = 10.2360414874525 done 
#> lambda = 10.6682494297369 done 
#> lambda = 11.1187069761873 done 
#> lambda = 11.5881847004551 done 
#> lambda = 12.0774857129934 done 
#> lambda = 12.587447034895 done 
#> lambda = 13.11894102974 done 
#> lambda = 13.6728768959011 done 
#> lambda = 14.2502022218612 done 
#> lambda = 14.8519046072019 done 
#> lambda = 15.4790133520375 done 
#> lambda = 16.1326012177839 done 
#> lambda = 16.813786262274 done 
#> lambda = 17.5237337523593 done 
#> lambda = 18.2636581572701 done 
#> lambda = 19.0348252261428 done 
#> lambda = 19.8385541532693 done 
#> lambda = 20.6762198347724 done 
#> lambda = 21.5492552205668 done 
#> lambda = 22.4591537656302 done 
#> lambda = 23.4074719847766 done 
#> lambda = 24.3958321153036 done 
#> lambda = 25.4259248920664 done 
#> lambda = 26.499512439728 done 
#> lambda = 27.6184312871313 done 
#> lambda = 28.7845955089513 done 
#> lambda = 30 done 

#> $optLambda
#> [1] 13.67288
#> 
#> $optPrec
#> A 25 x 25 ridge precision matrix estimate with lambda = 13.672877
#>              a            b           c            d            e            f …
#> a  0.754982779 -0.002998480 -0.04858850 -0.002401803  0.013760663  0.001119408 …
#> b -0.002998480  0.805562115 -0.01556835 -0.006423874 -0.024053290  0.005685832 …
#> c -0.048588503 -0.015568354  0.75903211 -0.029985881  0.020314633  0.012666613 …
#> d -0.002401803 -0.006423874 -0.02998588  0.800435062  0.019768171  0.001763517 …
#> e  0.013760663 -0.024053290  0.02031463  0.019768171  0.766978241 -0.005623105 …
#> f  0.001119408  0.005685832  0.01266661  0.001763517 -0.005623105  0.810330655 …
#> … 19 more rows and 19 more columns
#> 
OPT$optLambda  # Optimal penalty
#> [1] 13.67288
OPT$optPrec    # Regularized precision under optimal penalty
#> A 25 x 25 ridge precision matrix estimate with lambda = 13.672877
#>              a            b           c            d            e            f …
#> a  0.754982779 -0.002998480 -0.04858850 -0.002401803  0.013760663  0.001119408 …
#> b -0.002998480  0.805562115 -0.01556835 -0.006423874 -0.024053290  0.005685832 …
#> c -0.048588503 -0.015568354  0.75903211 -0.029985881  0.020314633  0.012666613 …
#> d -0.002401803 -0.006423874 -0.02998588  0.800435062  0.019768171  0.001763517 …
#> e  0.013760663 -0.024053290  0.02031463  0.019768171  0.766978241 -0.005623105 …
#> f  0.001119408  0.005685832  0.01266661  0.001763517 -0.005623105  0.810330655 …
#> … 19 more rows and 19 more columns

## Another example with standardized data
X <- scale(X, center = TRUE, scale = TRUE)
OPT  <- optPenalty.LOOCV(X, lambdaMin = .5, lambdaMax = 30, step = 100, cor = TRUE,
                         target = default.target(covML(X, cor = TRUE))); OPT
#> Perform input checks... 
#> Calculating cross-validated negative log-likelihoods...
#> lambda = 0.5 done 
#> lambda = 0.521112064796442 done 
#> lambda = 0.543115568152822 done 
#> lambda = 0.56604815028642 done 
#> lambda = 0.589949040739926 done 
#> lambda = 0.614859125489326 done 
#> lambda = 0.640821016885354 done 
#> lambda = 0.667879126548165 done 
#> lambda = 0.696079741339917 done 
#> lambda = 0.725471102545235 done 
#> lambda = 0.756103488394997 done 
#> lambda = 0.788029300074619 done 
#> lambda = 0.821303151363959 done 
#> lambda = 0.855981962062195 done 
#> lambda = 0.89212505535748 done 
#> lambda = 0.929794259307953 done 
#> lambda = 0.969054012607691 done 
#> lambda = 1.00997147481854 done 
#> lambda = 1.0526166412564 done 
#> lambda = 1.09706246272843 done 
#> lambda = 1.14338497032617 done 
#> lambda = 1.19166340548777 done 
#> lambda = 1.24198035555219 done 
#> lambda = 1.29442189503684 done 
#> lambda = 1.34907773288074 done 
#> lambda = 1.40604136590477 done 
#> lambda = 1.46541023875169 done 
#> lambda = 1.52728591057948 done 
#> lambda = 1.59177422879317 done 
#> lambda = 1.65898551011235 done 
#> lambda = 1.72903472928405 done 
#> lambda = 1.80204171576394 done 
#> lambda = 1.87813135870213 done 
#> lambda = 1.95743382058443 done 
#> lambda = 2.04008475989428 done 
#> lambda = 2.12622556317653 done 
#> lambda = 2.21600358689979 done 
#> lambda = 2.30957240953135 done 
#> lambda = 2.40709209425555 done 
#> lambda = 2.5087294627854 done 
#> lambda = 2.61465838073554 done 
#> lambda = 2.72506005504483 done 
#> lambda = 2.84012334395744 done 
#> lambda = 2.96004508009247 done 
#> lambda = 3.08503040715507 done 
#> lambda = 3.21529313086478 done 
#> lambda = 3.35105608470152 done 
#> lambda = 3.49255151109498 done 
#> lambda = 3.64002145870927 done 
#> lambda = 3.79371819650269 done 
#> lambda = 3.9539046452707 done 
#> lambda = 4.12085482741052 done 
#> lambda = 4.29485433567656 done 
#> lambda = 4.47620082172873 done 
#> lambda = 4.66520450530918 done 
#> lambda = 4.86218870491866 done 
#> lambda = 5.0674903909002 done 
#> lambda = 5.28146076187626 done 
#> lambda = 5.50446584552545 done 
#> lambda = 5.73688712472652 done 
#> lambda = 5.97912219014072 done 
#> lambda = 6.23158542034891 done 
#> lambda = 6.49470869070685 done 
#> lambda = 6.76894211213128 done 
#> lambda = 7.05475480108065 done 
#> lambda = 7.3526356820475 done 
#> lambda = 7.66309432393553 done 
#> lambda = 7.98666181175188 done 
#> lambda = 8.32389165510583 done 
#> lambda = 8.67536073506814 done 
#> lambda = 9.04167029101067 done 
#> lambda = 9.42344694911443 done 
#> lambda = 9.8213437943055 done 
#> lambda = 10.2360414874525 done 
#> lambda = 10.6682494297369 done 
#> lambda = 11.1187069761873 done 
#> lambda = 11.5881847004551 done 
#> lambda = 12.0774857129934 done 
#> lambda = 12.587447034895 done 
#> lambda = 13.11894102974 done 
#> lambda = 13.6728768959011 done 
#> lambda = 14.2502022218612 done 
#> lambda = 14.8519046072019 done 
#> lambda = 15.4790133520375 done 
#> lambda = 16.1326012177839 done 
#> lambda = 16.813786262274 done 
#> lambda = 17.5237337523593 done 
#> lambda = 18.2636581572701 done 
#> lambda = 19.0348252261428 done 
#> lambda = 19.8385541532693 done 
#> lambda = 20.6762198347724 done 
#> lambda = 21.5492552205668 done 
#> lambda = 22.4591537656302 done 
#> lambda = 23.4074719847766 done 
#> lambda = 24.3958321153036 done 
#> lambda = 25.4259248920664 done 
#> lambda = 26.499512439728 done 
#> lambda = 27.6184312871313 done 
#> lambda = 28.7845955089513 done 
#> lambda = 30 done 

#> $optLambda
#> [1] 1.729035
#> 
#> $optPrec
#> A 25 x 25 ridge precision matrix estimate with lambda = 1.729035
#>              a            b           c            d           e            f …
#> a  0.870558731  0.005799602 -0.10887022  0.020620932  0.02747321 -0.030649123 …
#> b  0.005799602  0.858543312 -0.05826257 -0.060673303 -0.10951470  0.013668617 …
#> c -0.108870222 -0.058262566  0.92295982 -0.108621345  0.04811302  0.035537758 …
#> d  0.020620932 -0.060673303 -0.10862134  0.870451897  0.05721630 -0.008132546 …
#> e  0.027473206 -0.109514703  0.04811302  0.057216296  0.90403666 -0.041437493 …
#> f -0.030649123  0.013668617  0.03553776 -0.008132546 -0.04143749  0.861873688 …
#> … 19 more rows and 19 more columns
#> 
OPT$optLambda  # Optimal penalty
#> [1] 1.729035
OPT$optPrec    # Regularized precision under optimal penalty
#> A 25 x 25 ridge precision matrix estimate with lambda = 1.729035
#>              a            b           c            d           e            f …
#> a  0.870558731  0.005799602 -0.10887022  0.020620932  0.02747321 -0.030649123 …
#> b  0.005799602  0.858543312 -0.05826257 -0.060673303 -0.10951470  0.013668617 …
#> c -0.108870222 -0.058262566  0.92295982 -0.108621345  0.04811302  0.035537758 …
#> d  0.020620932 -0.060673303 -0.10862134  0.870451897  0.05721630 -0.008132546 …
#> e  0.027473206 -0.109514703  0.04811302  0.057216296  0.90403666 -0.041437493 …
#> f -0.030649123  0.013668617  0.03553776 -0.008132546 -0.04143749  0.861873688 …
#> … 19 more rows and 19 more columns