init.EM.lasso | R Documentation |
init.EM.lasso
does the following regression :
||Y_data-T.delta||_(Sigma_YY^(-1)) + lambda |delta|_1
using the function glmnet::glmnet
of package glmnet
,
through function lasso_regression_K_fixed
.
T is the incidence matrix of the tree, and
delta the vectorial representation of the shifts (see functions
incidence.matrix
and shifts.list_to_vector
for further details).
init.EM.lasso(
phylo,
Y_data,
Y_data_imp = Y_data,
Y_data_vec_known = as.vector(Y_data),
process,
times_shared = compute_times_ca(phylo),
distances_phylo,
nbr_of_shifts,
K_lag_init = 0,
use_sigma = TRUE,
params_sigma = NULL,
variance.init = diag(1, p, p),
random.init = FALSE,
value.root.init = rep(0, p),
exp.root.init = rep(1, p),
var.root.init = diag(1, p, p),
edges.init = NULL,
values.init = matrix(0, p, length(edges.init)),
relativeTimes.init = NULL,
selection.strength.init = 1,
optimal.value.init = rep(0, p),
T_tree = incidence.matrix(phylo),
subtree.list = NULL,
miss = FALSE,
sBM_variance = FALSE,
stationary.root.init = FALSE,
masque_data,
independent = FALSE,
...
)
Y_data |
data at the tips. |
times_shared |
(matrix) : times of shared ancestry, result of function
|
distances_phylo |
(matrix) : phylogenetic distance, result of function
|
nbr_of_shifts |
number of shifts used in the EM algorithm |
A Cholesky decomposition of function Sigma_YY^(-1) is used. lambda is chosen so that delta has the right number of non zero components.
params_init the list of initial parameters to be used, in the right format.
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