Description Details Author(s) References See Also Examples

The package implements multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation or marginal likelihood maximization. It allows for multiple high-dimensional data types that require penalization, as well as unpenalized variables. Moreover, it allows a paired penalty for paired data types, and preferential data types can be specified.

The DESCRIPTION file:
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`betasout`

: Coefficient estimates from (converged) IWLS fit

`createXXblocks`

: Creates list of (unscaled) sample covariance matrices

`CVscore`

: Cross-validated score for given penalty parameters

`dataXXmirmeth`

: Example data

`doubleCV`

: Double cross-validation for estimating performance

`fastCV2`

: Fast cross-validation per data block; no dependency

`IWLSCoxridge`

: Iterative weighted least squares algorithm for Cox ridge regression

`IWLSridge`

: Iterative weighted least squares algorithm for linear and logistic ridge regression

`mlikCV`

: Cross-validation for estimating performance of marginal likelihood estimation

`optLambdasWrap`

: Find optimal ridge penalties by cross-validation

`optLambdas_mgcvWrap`

: Find optimal ridge penalties in terms of marginal likelihood

`predictIWLS`

: Predictions from ridge fits

`setupParallel`

: Setting up parallel computing

`SigmaFromBlocks`

: Create penalized sample cross-product matrix

Mark A. van de Wiel (mark.vdwiel@amsterdamumc.nl)

Mark A. van de Wiel, Mirrelijn van Nee, Armin Rauschenberger (2021). Fast cross-validation for high-dimensional ridge regression. J Comp Graph Stat

A full demo and data are available from:

https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
data(dataXXmirmeth)
resp <- dataXXmirmeth[[1]]
XXmirmeth <- dataXXmirmeth[[2]]
# Find initial lambdas: fast CV per data block separately.
cvperblock2 <- fastCV2(XXblocks=XXmirmeth,Y=resp,kfold=10,fixedfolds = TRUE)
lambdas <- cvperblock2$lambdas
# Create (repeated) CV-splits of the data.
leftout <- CVfolds(Y=resp,kfold=10,nrepeat=3,fixedfolds = TRUE)
# Compute cross-validated score for initial lambdas
CVscore(penalties=lambdas, XXblocks=XXmirmeth,Y=resp,folds=leftout,
score="loglik")
# Optimizes cross-validate criterion (default: log-lik)
# Increase the number of iterations for optimal results
jointlambdas <- optLambdasWrap(penaltiesinit=lambdas, XXblocks=XXmirmeth,Y=resp,
folds=leftout,score="loglik",save=T, maxItropt1=5, maxItropt2=5)
# Alternatively: optimize by using marginal likelihood criterion
## Not run:
jointlambdas2 <- optLambdas_mgcvWrap(penaltiesinit=lambdas, XXblocks=XXmirmeth,
Y=resp)
## End(Not run)
# Optimal lambdas
optlambdas <- jointlambdas$optpen
# Prepare fitting for the optimal lambdas.
XXT <- SigmaFromBlocks(XXmirmeth,penalties=optlambdas)
# Fit. fit$etas contains the n linear predictors
fit <- IWLSridge(XXT,Y=resp)
``` |

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