porridge (version 0.3.3)

optPenaltyPmultiT.kCVauto: Automatic search for optimal penalty parameter (ridge precision with multi-targets).

Description

Function that determines the optimal penalty parameters through maximization of the k-fold cross-validated log-likelihood score, assuming that variates are grouped and penalized group-wise.

Usage

optPenaltyPmultiT.kCVauto(Y, lambdaMin, lambdaMax, 
                          lambdaInit=(lambdaMin+lambdaMax)/2,
                          fold=nrow(Y), targetList)

Value

The function returns a numeric containing the cross-validated optimal positive penalty parameters.

Arguments

Y

Data matrix with samples as rows and variates as columns.

lambdaMin

A numeric giving the minimum value for the penalty parameters. One value per target. Values should be specified in the same order as the target's appearance in targetList.

lambdaMax

A numeric giving the maximum value for the penalty parameters. One value per group. Values should be specified in the same order as the target's appearance in targetList.

lambdaInit

A numeric giving the initial (starting) value for the penalty parameters. One value per group. Values should be specified in the same order as the target's appearance in targetList.

fold

A numeric or integer specifying the number of folds to apply in the cross-validation.

targetList

A list of semi-positive definite target matrices towards which the precision matrix is potentially shrunken.

Author

W.N. van Wieringen.

References

van Wieringen, W.N., Stam, K.A., Peeters, C.F.W., van de Wiel, M.A. (2020), "Updating of the Gaussian graphical model through targeted penalized estimation", Journal of Multivariate Analysis, 178, Article 104621.

See Also

ridgePmultiT

Examples

# set dimension and sample size
p <- 10
n <- 10

# specify vector of penalty parameters
lambda       <- c(2, 1)

# generate precision matrix
T1       <- matrix(0.7, p, p)
diag(T1) <- 1
T2       <- diag(rep(2, p))

# generate precision matrix
Omega       <- matrix(0.4, p, p)
diag(Omega) <- 2
Sigma       <- solve(Omega)

# data 
Y <- mvtnorm::rmvnorm(n, mean=rep(0,p), sigma=Sigma)
S <- cov(Y)

# find optimal penalty parameters through cross-validation
lambdaOpt <- optPenaltyPmultiT.kCVauto(Y, rep(10^(-10), 2), 
                          rep(10^(10), 2), rep(1, 2), 
                          targetList=list(T1=T1, T2=T2)) 

# unpenalized diagonal estimate
Phat <- ridgePmultiT(S, lambdaOpt, list(T1=T1, T2=T2))