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update valse.R
[valse.git]
/
R
/
gridLambda.R
diff --git
a/R/gridLambda.R
b/R/gridLambda.R
index
2c66e4c
..
e7946ae
100644
(file)
--- a/
R/gridLambda.R
+++ b/
R/gridLambda.R
@@
-1,8
+1,11
@@
#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
#' @param phiInit value for phi
#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
#' @param phiInit value for phi
-#' @param rhoIn
t
value for rho
+#' @param rhoIn
it
value for rho
#' @param piInit value for pi
#' @param gamInit value for gamma
#' @param piInit value for pi
#' @param gamInit value for gamma
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param gamma power of weights in the penalty
#' @param mini minimum number of iterations in EM algorithm
#' @param maxi maximum number of iterations in EM algorithm
#' @param tau threshold to stop EM algorithm
#' @param mini minimum number of iterations in EM algorithm
#' @param maxi maximum number of iterations in EM algorithm
#' @param tau threshold to stop EM algorithm
@@
-16,7
+19,7
@@
gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi
m = dim(phiInit)[2]
k = dim(phiInit)[3]
m = dim(phiInit)[2]
k = dim(phiInit)[3]
- list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+ list_EMG = .Call("EMGLLF
_core
",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
grid = array(0, dim=c(p,m,k))
for (i in 1:p)
grid = array(0, dim=c(p,m,k))
for (i in 1:p)