Description Usage Arguments Details Value References Examples
Computes lasso, group lasso, scaled lasso, or scaled group lasso solution.
The outputs are coefficientestimate and subgradient. If type = "slasso"
or type = "sgrlasso"
, the output will include estimated standard deviation.
1 2 
X 
predictor matrix. 
Y 
response vector. 
type 
type of penalty. Must be specified to be one of the following:

lbd 
penalty term of lasso. By letting this argument be 
group 

weights 
weight vector with length equal to the number of groups. Default is

verbose 
logical. Only available for 
... 
auxiliary arguments for 
Computes lasso, group lasso, scaled lasso, or scaled group lasso solution. Users can specify the value of lbd or choose to run crossvalidation to get optimal lambda in term of mean squared error. Coordinate decent algorithm is used to fit scaled lasso and scaled group lasso models.
B0 
coefficient estimator. 
S0 
subgradient. 
sigmaHat 
estimated standard deviation. 
lbd, weights, group 
same as input arguments. 
Mitra, R. and Zhang, C. H. (2016), "The benefit of group sparsity in group inference with debiased scaled group lasso," Electronic Journal of Statistics, 10, 18291873.
Yang, Y. and Zou, H. (2015), “A Fast Unified Algorithm for Computing GroupLasso Penalized Learning Problems,” Statistics and Computing, 25(6), 11291141.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  set.seed(123)
n < 50
p < 10
X < matrix(rnorm(n*p), n)
Y < X %*% c(1, 1, rep(0, p2)) + rnorm(n)
#
# lasso
#
lassoFit(X = X, Y = Y, type = "lasso", lbd = .5)
#
# group lasso
#
lassoFit(X = X, Y = Y, type = "grlasso", lbd = .5, weights = rep(1,2),
group = rep(1:2, each=5))
#
# scaled lasso
#
lassoFit(X = X, Y = Y, type = "slasso", lbd = .5)
#
# scaled group lasso
#
lassoFit(X = X, Y = Y, type = "sgrlasso", lbd = .5, weights = rep(1,2),
group = rep(1:2, each=5))

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