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,
intercept = TRUE, sparse.data = is(x, "sparseMatrix"),
collapse = FALSE, max.threads = 2L,
algorithm.config = msgl.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 features (covariates), a
vector of length $p$. Each element of the vector
specifying the group of the feature.
groupWeights
the group weights, a vector of length
$m$ (the number of groups). If groupWeights =
NULL default weights will be used. Default weights are 0
for the intercept and $$\sqrt{K\cdot\textrm{number of
features in the group}}$$ for all other
parameterWeights
a matrix of size $K \times
p$. If parameterWeights = NULL default weights
will be used. 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 features 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
intercept
should the model include intercept
parameters
sparse.data
if TRUE x will be treated as
sparse, if x is a sparse matrix it will be treated
as sparse by default.
collapse
if TRUE the results for each
subsample will be collapse into one result (this is
useful if the subsamples are not overlapping)
max.threads
the maximal number of threads to be
used
algorithm.config
the algorithm configuration to be
used.