optLambdas: Find optimal ridge penalties.

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

Find optimal ridge penalties.

Description

Optimizes a cross-validated score w.r.t. ridge penalties for multiple data blocks.

Usage

optLambdas(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,
  optmethod = ifelse(length(penaltiesinit) == 1, "Brent", "Nelder-Mead"), maxItropt = 500,
  save = FALSE, parallel = FALSE, fixedpen = NULL, fixedseed = TRUE)

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.
optmethod	Character. Optimization 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.
maxItropt	Integer. Maximum number of iterations for optmethod.
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.
fixedpen	Integer vector or NULL. Contains indices of data types of which penalty is fixed to the corresponding value in penaltiesinit.
fixedseed	Boolean. Should the initialization be fixed? For reproducibility.

Details

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:

optres	Output of the optimizer
optpen	Vector with determined optimal penalties
allsc	Matrix with CV scores for all penalty parameter configurations used by the optimizer

See Also

optLambdasWrap for i) (recommended) optimization in two steps: first global, then local; and ii) sequential optimization when some data types are preferred over others. 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)

# One-pass optimization
# Increase the number of iterations for optimal results
jointlambdas <- optLambdas(penaltiesinit=lambdas, XXblocks=XXmirmeth,Y=resp,
folds=leftout,score="loglik",save=T,maxItropt=5)

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