optLambdas_mgcvWrap: Find optimal ridge penalties with sequential optimization.

Description

Sequentially optimizes a marginal likelihood score w.r.t. ridge penalties for multiple data blocks.

Usage

optLambdas_mgcvWrap(penaltiesinit=NULL, XXblocks,Y, pairing=NULL, model=NULL, reltol=1e-4,
  optmethod1= "SANN", optmethod2 =ifelse(length(penaltiesinit)==1,"Brent", "Nelder-Mead"),
  maxItropt1=10,maxItropt2=25,tracescore=TRUE,fixedseed =TRUE, pref=NULL, fixedpen=NULL,
  sigmasq = 1, opt.sigma=ifelse(model=="linear",TRUE, FALSE))

Arguments

penaltiesinit

Numeric vector. Initial values for penaltyparameters. May be obtained from fastCV2.

XXblocks

List of nxn matrices. Usually output of createXXblocks.

Response vector: numeric, binary, factor or survival.

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.

model

Character. Any of c("linear", "logistic", "cox"). Is inferred from Y when NULL.

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.

tracescore

Boolean. Should the output of the scores be traced?

fixedseed

Boolean. Should the initialization be fixed? For reproducibility.

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.

sigmasq

Default error variance.

opt.sigma

Boolean. Should the error variance be optimized as well? Only relevant for model="linear".

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

Details

As opposed to optLambdas_mgcv this function first searches globally, then locally. Hence, more time-consuming, but better guarded against multiple local optima. See gam for details on how the marginal likelihood is computed.

References