#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
#'
#' @param phiInit value for phi
-#' @param rhoInit\tvalue for rho
-#' @param piInit\tvalue for pi
+#' @param rhoInit for rho
+#' @param piInit 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)
list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
X, Y, tau, fast)
grid <- array(0, dim = c(p, m, k))
- for (i in 1:p)
+ for (j in 1:p)
{
- for (j in 1:m)
- grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
+ for (mm in 1:m)
+ grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma)
}
sort(unique(grid))
}