msgl (version 0.1.3)

msgl.subsampling: Multinomial sparse group lasso generic subsampling procedure

Description

Support the use of multiple processors.

Usage

msgl.subsampling(x, classes,
    sampleWeights = rep(1/length(classes), length(classes)),
    grouping = NULL, groupWeights = NULL,
    parameterWeights = NULL, alpha = 0.5,
    standardize = TRUE, lambda, training, test,
    sparse.data = FALSE, max.threads = 2L,
    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.

training

a list of training samples, each item of the list corresponding to a subsample. Each item in the list must be a vector with the indices of the training samples for the corresponding subsample. The length of the list must equal the length of the

test

a list of test samples, each item of the list corresponding to a subsample. Each item in the list must be vector with the indices of the test samples for the corresponding subsample. The length of the list must equal the length of the traini

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

algorithm.config

the algorithm configuration to be used.

Value

• link
  the linear predictors -- a list of length length(test) with each element of the list another 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(test) with each element of the list another 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 list of length length(test) with each element of the list a matrix of size $N \times d$ with $d=$length(lambda).
• features
  number of features used in the models.
• parameters
  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 = 100L, lambda.min = 0.03)

test <- replicate(5, sample(1:length(classes))[1:20], simplify = FALSE)
train <- lapply(test, function(s) (1:length(classes))[-s])

fit.sub <- msgl.subsampling(x, classes, alpha = .5, lambda = lambda,
 training = train, test = test)

# Missclassification count of second subsample
colSums(fit.sub$classes[[2]] != classes[test[[2]]])