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Initial coefficients

Source: R/group-lasso.R
dot-estim_initial_coefs.Rd

Estimate initial coefficients.

Usage

.estim_initial_coefs(x, y, family, alpha_init, group, foldid, nfolds, lambda)

Arguments

x

\(n_0 \times p\) predictor matrix, where \(n_0\) is the number of observations used for model training and \(p\) is the number of variables

y

\(n_0\)-dimensional response vector, where \(n_0\) is the number of observations used for model training

family

character string "gaussian", "binomial", "poisson", or "cox"

alpha_init

elastic net mixing parameter (\(0 \leq\) alpha_init \(\leq 1\)) for initial regression (default: ridge penalisation with alpha_init=0); alternative choices are "pearson", "spearman", or "kendall" to use initial correlation coefficients (not implemented for family="cox"), "multiridge" for multi-penalty ridge regression with one penalty for each group (not implemented for family="poisson" or overlapping groups), or NA to set all initial coefficients equal to 1

group

group structure (three options):

  • \(p\)-dimensional vector of group indices (in \(\{1, \ldots, q\}\)) or labels,

  • list with \(q\) slots containing the variable indices (in \(\{1, \ldots, p\}\)) or labels,

  • \(p \times p\) matrix, where the entry in the \(j^{\text{th}}\) row and the \(k^{\text{th}}\) column indicates whether information should be transferred from the \(j^{\text{th}}\) to the \(k^{\text{th}}\) variable

foldid

\(n_0\)-dimensional vector containing the fold identifiers

nfolds

integer specifying the number of folds

lambda

numeric scalar, or NULL (determined by cross-validation)

Details

This function is called by corila(). It calls glmnet::cv.glmnet() or glmnet::glmnet() for an initial lasso, ridge, or elastic net regression, multiridge() for an initial multi-penalty ridge regression, or stats::cor() for initial correlation coefficients.

Examples

n <- 20
p <- 10
x <- matrix(rnorm(n * p), nrow = n, ncol = p)
beta <- rbinom(n = p, size = 1, prob = 0.5) * rnorm(p)
y <- drop(x %*% beta)
corila:::.estim_initial_coefs(x = x,
                              y = y,
                              family = "gaussian",
                              alpha_init = "spearman",
                              group = NULL,
                              foldid = NULL,
                              nfolds = 10,
                              lambda = NULL)
#> $coef
#>  [1] -0.50676692  0.04060150  0.48872180  0.05714286 -0.26466165  0.41353383
#>  [7]  0.04360902  0.23909774  0.49774436 -0.31127820
#> 
#> $lambda
#> NULL
#>