R: Find optimal ridge penalties with sequential optimization.

optLambdas_mgcvWrap {multiridge}

R Documentation

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`.
`Y`	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"`.

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.

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

References

Wood, S. N. (2011), Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, J. Roy. Statist. Soc., B 73(1), 3-36.