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Argument check

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

Checks arguments of functions corila() and cv.corila().

Usage

.validate(
  x,
  y,
  group,
  primary,
  family,
  hyper,
  alpha_init,
  alpha_final,
  cor,
  foldid,
  nfolds,
  lambda_init
)

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

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

primary

\(p\)-dimensional logical vector indicating whether a predictor may be included in the final model (TRUE for "primary predictors") or must be excluded from the final model (FALSE for "auxiliary predictors")

family

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

hyper

list of \(m\)-dimensional vectors or a data frame with \(m\) rows containing candidate values for the regularisation and mixing hyperparameters

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

alpha_final

elastic net mixing parameter for final regression (default: lasso penalisation with alpha_final=1)

cor

character string "pearson", "spearman" (default), or "kendall"; or \(p \times p\) correlation matrix

foldid

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

nfolds

integer specifying the number of folds

lambda_init

regularisation hyperparameter(s), or NULL (cross-validation)

Value

Returns a list with slots n, p, and q or an error message.

Details

This function is called by corila() and cv.corila(). It repeatedly calls .assert().

See also

Use .assert() to validate individual arguments.