msgl (version 0.1.3)

msgl.lambda.seq: Computes a lambda sequence for the regularization path

Description

Computes a decreasing lambda sequence of length d. The sequence ranges from a data determined maximal lambda $\lambda_\textrm{max}$ to the user inputed lambda.min.

Usage

msgl.lambda.seq(x, classes,
    sampleWeights = rep(1/length(classes), length(classes)),
    grouping = NULL, groupWeights = NULL,
    parameterWeights = NULL, alpha = 0.5, d = 100L,
    standardize = TRUE, lambda.min, sparse.data = FALSE,
    algorithm.config = sgl.standard.config)

Arguments

x

design matrix, matrix of size $N \times p$.

classes

classes, factor of length $N$.

sampleWeights

sample weights, a vector of length $N$.

grouping

grouping of covariates, a vector of length $p$. Each element of the vector specifying the group of the covariate.

groupWeights

the group weights, a vector of length $m+1$ (the number of groups). The first element of the vector is the intercept weight. If groupWeights = NULL default weights will be used. Default weights are 0 for the intercept and $$\sqrt{K\cd

parameterWeights

a matrix of size $K \times (p+1)$. The first column of the matrix is the intercept weights. Default weights are is 0 for the intercept weights and 1 for all other weights.

alpha

the $\alpha$ value 0 for group lasso, 1 for lasso, between 0 and 1 gives a sparse group lasso penalty.

d

the length of lambda sequence

standardize

if TRUE the covariates are standardize before fitting the model. The model parameters are returned in the original scale.

lambda.min

the smallest lambda value in the computed sequence.

sparse.data

if TRUE x will be treated as sparse, if x is a sparse matrix it will be treated as sparse by default.

algorithm.config

the algorithm configuration to be used.

Value

• a vector of length d containing the compute lambda sequence.

Examples

data(SimData)
x <- sim.data$x
classes <- sim.data$classes
lambda <- msgl.lambda.seq(x, classes, alpha = .5, d = 100L, lambda.min = 0.01)