cvm_priorityelasticnet: priorityelasticnet with several block specifications

Description

Runs priorityelasticnet for a list of block specifications and gives the best results in terms of cv error.

Usage

cvm_priorityelasticnet(
  X,
  Y,
  weights,
  family,
  type.measure,
  blocks.list,
  max.coef.list = NULL,
  block1.penalization = TRUE,
  lambda.type = "lambda.min",
  standardize = TRUE,
  nfolds = 10,
  foldid,
  cvoffset = FALSE,
  cvoffsetnfolds = 10,
  alpha = 1,
  ...
)

Value

object of class cvm_priorityelasticnet with the following elements. If these elements are lists, they contain the results for each penalized block of the best result.

lambda.ind: list with indices of lambda for lambda.type.
lambda.type: type of lambda which is used for the predictions.
lambda.min: list with values of lambda for lambda.type.
min.cvm: list with the mean cross-validated errors for lambda.type.
nzero: list with numbers of non-zero coefficients for lambda.type.
glmnet.fit: list of fitted glmnet objects.
name: a text string indicating type of measure.
block1unpen: if block1.penalization = FALSE, the results of either the fitted glm or coxph object.
best.blocks: character vector with the indices of the best block specification.
best.blocks.indices: list with the indices of the best block specification ordered by best to worst.
best.max.coef: vector with the number of maximal coefficients corresponding to best.blocks.
best.model: complete priorityelasticnet model of the best solution.
coefficients: coefficients according to the results obtained with best.blocks.
call: the function call.

Arguments

X: A numeric matrix of predictors.
Y: A response vector. For family = "multinomial", Y should be a factor with more than two levels.
weights: Optional observation weights. Default is NULL.
family: A character string specifying the model type. Options are "gaussian", "binomial", "cox", and "multinomial". Default is "gaussian".
type.measure: Loss function for cross-validation. Options are "mse", "deviance", "class", "auc". Default depends on the family.
blocks.list: list of the format list(list(bp1=...,bp2=...,), list(bp1=,...,bp2=...,), ...). For the specification of the entries, see priorityelasticnet.
max.coef.list: list of max.coef vectors. The first entries are omitted if block1.penalization = FALSE. Default is NULL.
block1.penalization: Logical. If FALSE, the first block will not be penalized. Default is TRUE.
lambda.type: Type of lambda to select. Options are "lambda.min" or "lambda.1se". Default is "lambda.min".
standardize: Logical flag for variable standardization, prior to fitting the model. Default is TRUE.
nfolds: Number of folds for cross-validation. Default is 10.
foldid: Optional vector of values between 1 and nfolds identifying what fold each observation is in. Default is NULL.
cvoffset: Logical. If TRUE, a cross-validated offset is used. Default is FALSE.
cvoffsetnfolds: Number of folds for cross-validation of the offset. Default is 10.
alpha: Elastic net mixing parameter. The elastic net penalty is defined as $$(1 - \alpha)/2||\beta||_2^2 + \alpha||\beta||_1$$ Defaults to 1 (lasso penalty).
...: other arguments that can be passed to the function priorityelasticnet.