msgl (version 0.1.3)

msgl.cv: Multinomial sparse group lasso cross validation using multiple possessors

Description

Multinomial sparse group lasso cross validation using multiple possessors

Usage

msgl.cv(x, classes, sampleWeights = NULL,
    grouping = NULL, groupWeights = NULL,
    parameterWeights = NULL, alpha = 0.5,
    standardize = TRUE, lambda, fold = 10L,
    cv.indices = list(), sparse.data = FALSE,
    max.threads = 2L, seed = 331L,
    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.

standardize

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

lambda

the lambda sequence for the regularization path.

fold

the fold of the cross validation, an integer larger than $1$ and less than $N+1$. Ignored if cv.indices != NULL. If fold$\le$max(table(classes)) then the data will be split into fold disjoint sub

cv.indices

a list of indices of a cross validation splitting. If cv.indices = NULL then a random splitting will be generated using the fold argument.

sparse.data

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

max.threads

the maximal number of threads to be used

seed

the seed used for generating the random cross validation splitting, only used if fold$\le$max(table(classes)).

algorithm.config

the algorithm configuration to be used.

Value

• link
  the linear predictors -- a list of length length(lambda) one item for each lambda value, with each item a matrix of size $K \times N$ containing the linear predictors.
• response
  the estimated probabilities - a list of length length(lambda) one item for each lambda value, with each item a matrix of size $K \times N$ containing the probabilities.
• classes
  the estimated classes - a matrix of size $N \times d$ with $d=$length(lambda).
• cv.indices
  the cross validation splitting used.
• features
  average number of features used in the models.
• parameters
  average number of parameters used in the models.

Examples

data(SimData)
x <- sim.data$x
classes <- sim.data$classes
lambda <- msgl.lambda.seq(x, classes, alpha = .5, d = 25L, lambda.min = 0.03)
fit.cv <- msgl.cv(x, classes, alpha = .5, lambda = lambda)

# Missclassification count
colSums(fit.cv$classes != classes)