optLambdasWrap {multiridge}

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)