fix for m==1

[valse.git] / pkg / R / selectVariables.R

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index f717cae..d863a4b 100644 (file)
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -23,7 +23,7 @@
 #' @export
 #'
 selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, 
-  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
+  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
 {
   if (ncores > 1) {
     cl <- parallel::makeCluster(ncores, outfile = "")
@@ -37,29 +37,40 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
     params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
       X, Y, eps, fast)
 
-    p <- dim(phiInit)[1]
-    m <- dim(phiInit)[2]
+    p <- ncol(X)
+    m <- ncol(Y)
 
     # selectedVariables: list where element j contains vector of selected variables
     # in [1,m]
     selectedVariables <- lapply(1:p, function(j) {
       # from boolean matrix mxk of selected variables obtain the corresponding boolean
       # m-vector, and finally return the corresponding indices
-      seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
+      if (m>1) {
+        seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
+      } else {
+        if (any(params$phi[j, , ] > thresh))
+          1
+        else
+          numeric(0)
+      }
     })
 
     list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
   }
 
   # For each lambda in the grid, we compute the coefficients
-  out <- if (ncores > 1) 
-    parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
+  out <-
+    if (ncores > 1) {
+      parLapply(cl, glambda, computeCoefs)
+    } else {
+      lapply(glambda, computeCoefs)
+    }
   if (ncores > 1) 
     parallel::stopCluster(cl)
   # Suppress models which are computed twice En fait, ca ca fait la comparaison de
   # tous les parametres On veut juste supprimer ceux qui ont les memes variables
-  # sélectionnées sha1_array <- lapply(out, digest::sha1) out[
-  # duplicated(sha1_array) ]
+  # sélectionnées
+  # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
   selec <- lapply(out, function(model) model$selected)
   ind_dup <- duplicated(selec)
   ind_uniq <- which(!ind_dup)