lambda | R Documentation |
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
.
lambda(x, classes, sampleWeights = NULL, grouping = NULL,
groupWeights = NULL, parameterWeights = NULL, alpha = 0.5,
d = 100L, standardize = TRUE, lambda.min, intercept = TRUE,
sparse.data = is(x, "sparseMatrix"), lambda.min.rel = FALSE,
algorithm.config = msgl.standard.config)
x |
design matrix, matrix of size |
classes |
classes, factor of length |
sampleWeights |
sample weights, a vector of length |
grouping |
grouping of features, a vector of length |
groupWeights |
the group weights, a vector of length
for all other weights. |
parameterWeights |
a matrix of size |
alpha |
the |
d |
the length of lambda sequence |
standardize |
if TRUE the features 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. |
intercept |
should the model include intercept parameters |
sparse.data |
if TRUE |
lambda.min.rel |
is lambda.min relative to lambda.max ? (i.e. actual lambda min used is |
algorithm.config |
the algorithm configuration to be used. |
a vector of length d
containing the computed lambda sequence.
Martin Vincent
data(SimData)
# A quick look at the data
dim(x)
table(classes)
lambda <- msgl::lambda(x, classes, alpha = .5, d = 100, lambda.min = 0.01)
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