runValse {valse} | R Documentation |
runValse
Description
Main function
Usage
runValse(
X,
Y,
procedure = "LassoMLE",
selecMod = "DDSE",
gamma = 1,
mini = 10,
maxi = 50,
eps = 1e-04,
kmin = 2,
kmax = 3,
rank.min = 1,
rank.max = 5,
ncores_outer = 1,
ncores_inner = 1,
thresh = 1e-08,
grid_lambda = numeric(0),
size_coll_mod = 50,
fast = TRUE,
verbose = FALSE,
plot = TRUE
)
Arguments
X |
matrix of covariates (of size n*p) |
Y |
matrix of responses (of size n*m) |
procedure |
among 'LassoMLE' or 'LassoRank' |
selecMod |
method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC' |
gamma |
integer for the power in the penaly, by default = 1 |
mini |
integer, minimum number of iterations in the EM algorithm, by default = 10 |
maxi |
integer, maximum number of iterations in the EM algorithm, by default = 100 |
eps |
real, threshold to say the EM algorithm converges, by default = 1e-4 |
kmin |
integer, minimum number of clusters, by default = 2 |
kmax |
integer, maximum number of clusters, by default = 10 |
rank.min |
integer, minimum rank in the low rank procedure, by default = 1 |
rank.max |
integer, maximum rank in the low rank procedure, by default = 5 |
ncores_outer |
Number of cores for the outer loop on k |
ncores_inner |
Number of cores for the inner loop on lambda |
thresh |
real, threshold to say a variable is relevant, by default = 1e-8 |
grid_lambda |
a vector with regularization parameters if known, by default numeric(0) |
size_coll_mod |
(Maximum) size of a collection of models, by default 50 |
fast |
TRUE to use compiled C code, FALSE for R code only |
verbose |
TRUE to show some execution traces |
plot |
TRUE to plot the selected models after run |
Value
The selected model (except if the collection of models has less than 11 models, the function returns the collection as it can not select one using Capushe)
Examples
n = 50; m = 10; p = 5
beta = array(0, dim=c(p,m,2))
beta[,,1] = 1
beta[,,2] = 2
data = generateXY(n, c(0.4,0.6), rep(0,p), beta, diag(0.5, p), diag(0.5, m))
X = data$X
Y = data$Y
res = runValse(X, Y, kmax = 5, plot=FALSE)
X <- matrix(runif(100), nrow=50)
Y <- matrix(runif(100), nrow=50)
res = runValse(X, Y, plot=FALSE)