Fit the multiclass regression estimator

multiclass_reg(
  Y,
  X,
  weighting = FALSE,
  weights = NULL,
  wtype = "size",
  initestim = NULL,
  normalize = TRUE,
  x_names = NULL,
  lambdas = NULL
)

Arguments

Y: list of size K. Its kth entry is a vector of size n_k
X: list of size K. Its kth entry is a matrix of size n_kxp
weighting: logical. If TRUE: use weighted regularization parameter
weights: provide vector of weights
wtype: either "size" (default, weighting based on group samle sizes), "init" (weighting based on initial estimator), "combi" (weighting based on group sample sizes and initial estimator)
initestim: pK-dimensional vector with estimated coefficients of initial estimator
normalize: logical. Whether or not to normalize the weights (currently divides by maximum).

Value

A list with the following components

fit: fitted genlasso object
coef: matrix of estimated coefficients
lambda: vector of regularization parameters along knots of regularization path
K: number of classes
p: number of predictors per class
n: vector of sample sizes for each class
Y: composite Y vector (stacked over the classes)
X: block diagonal X matrix (each block coresponds to a class)
var_indicator: matrix of variable indicators

Examples

p = 7
k = 2
n = 20
beta = c(1,2)
set.seed(1)
X = list(matrix(rnorm(p*n), ncol = p), matrix(rnorm(p*n), ncol = p))
Y = list(rnorm(n), rnorm(n))
# no missing data
res = multiclass_reg(Y, X)