optLambdasWrap: Find optimal ridge penalties with sequential optimization.

In multiridge: Fast Cross-Validation for Multi-Penalty Ridge Regression

Find optimal ridge penalties with sequential optimization.

Description

Sequentially optimizes a cross-validated score w.r.t. ridge penalties for multiple data blocks. Also implements preferential ridge, which allows to first optimize for the preferential data types.

Usage

optLambdasWrap(penaltiesinit = NULL, XXblocks, Y, X1 = NULL, pairing = NULL,
  folds, intercept = ifelse(is(Y, "Surv"), FALSE, TRUE), frac1 = NULL,
  score = "loglik", model = NULL, epsIWLS = 0.001, maxItrIWLS = 25,
  traceCV = TRUE, reltol = 1e-04, optmethod1 = "SANN", optmethod2 =
  ifelse(length(penaltiesinit) == 1, "Brent", "Nelder-Mead"), maxItropt1 = 10,
  maxItropt2 = 25, save = FALSE, parallel = FALSE, pref = NULL, fixedpen = NULL)

Arguments

penaltiesinit	Numeric vector. Initial values for penaltyparameters. May be obtained from fastCV2.
XXblocks	List of nxn matrices. Usually output of createXXblocks.
Y	Response vector: numeric, binary, factor or survival.
X1	Matrix. Dimension n x p_0, p_0 < n, representing unpenalized covariates
pairing	Numerical vector of length 3 or NULL when pairs are absent. Represents the indices (in XXblocks) of the two data blocks involved in pairing, plus the index of the paired block.
folds	List, containing the splits of the samples. Usually obtained by CVfolds
intercept	Boolean. Should an intercept be included?
frac1	Scalar. Prior fraction of cases. Only relevant for model=" logistic".
score	Character. See Details.
model	Character. Any of c("linear", "logistic", "cox"). Is inferred from Y when NULL.
epsIWLS	Scalar. Numerical bound for IWLS convergence.
maxItrIWLS	Integer. Maximum number of iterations used in IWLS.
traceCV	Boolean. Should the output of the IWLS algorithm be traced?
reltol	Scalar. Relative tolerance for optimization methods.
optmethod1	Character. First, global search method. Any of the methods c("Brent", "Nelder-Mead", "Sann") may be used, but simulated annealing by "Sann" is recommended to search a wide landscape. Other unconstrained methods offered by optim may also be used, but have not been tested.
optmethod2	Character. Second, local search method. Any of the methods c("Brent", "Nelder-Mead", "Sann") may be used, but "Nelder-Mead" is generally recommended. Other unconstrained methods offered by optim may also be used, but have not been tested.
maxItropt1	Integer. Maximum number of iterations for optmethod1.
maxItropt2	Integer. Maximum number of iterations for optmethod2.
save	Boolean. If TRUE appends the penalties and resulting CVscore to global variable allscores
parallel	Boolean. Should computation be done in parallel? If TRUE, requires to run setupParallel first.
pref	Integer vector or NULL. Contains indices of data types in XXblocks that are preferential.
fixedpen	Integer vector or NULL. Contains indices of data types of which penalty is fixed to the corresponding value in penaltiesinit.

Details

As opposed to optLambdas this function first searches globally, then locally. Hence, more time-consuming, but better guarded against multiple local optima.

See Scoring for details on score. We highly recommend to use smooth scoring functions, in particular "loglik". For ranking-based criteria like "auc" and "cindex" we advise to use repeated CV (see CVfolds) to avoid ending up in any of the many local optima.

Value

List, with components:

res	Outputs of all optimizers used
lambdas	List of penalties found by the optimizers
optpen	Numerical vector with final, optimal penalties

See Also

optLambdas for one-pass optimization. fastCV2 for initialization of penalties.A full demo and data are available from:
https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4

Examples

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)

# 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)

multiridge documentation built on June 13, 2022, 5:07 p.m.