A function that sets up a lambda sequence for a sparse group penalty.
process.lambda(
X,
y,
group,
Z,
type,
alpha,
lambda.min,
log.lambda,
nlambda,
group.weight,
ada_mult
)A vector with values for lambda.
The design matrix without intercept with the variables to be selected.
The response vector.
A vector indicating the group membership of each variable in X.
The design matrix of the variables to be included in the model without penalization.
A string indicating the type of regression model (linear or binomial).
Tuning parameter for the mixture of penalties at group and variable level. A value of 0 results in a selection at group level, a value of 1 results in a selection at variable level and everything in between is bi-level selection.
An integer multiplied by the maximum lambda to define the end of the lambda sequence.
A Boolean value that specifies whether the values of the lambda sequence should be on the log scale.
An integer that specifies the length of the lambda sequence.
A vector specifying weights that are multiplied by the group penalty to account for different group sizes.
An integer that defines the multiplier for adjusting the convergence threshold.