Refactor nb_param for ZIPLNfit

PLN-team · Feb 20, 2024 · c8eb7c3 · c8eb7c3
1 parent bbc5737
commit c8eb7c3
Showing 1 changed file with 26 additions and 45 deletions.
diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R
@@ -501,16 +501,21 @@ ZIPLNfit <- R6Class(
     d = function() {nrow(private$B)},
     #' @field d0 number of covariates in the ZI part
     d0 = function() {nrow(private$B0)},
-    #' @field nb_param number of parameters in the current PLN model
+    #' @field nb_param_zi number of parameters in the ZI part of the model
+    nb_param_zi   = function() {
+      as.integer(switch(private$ziparam,
+                        "single" = 1L,
+                        "row"    = self$n,
+                        "col"    =  self$p,
+                        "covar"  =  self$p * self$d))
+    },
+    #' @field nb_param_pln number of parameters in the PLN part of the model
+    nb_param_pln   = function() {
+      as.integer(self$p * self$d + self$p * (self$p + 1L) / 2L)
+    },
+    #' @field nb_param number of parameters in the ZIPLN model
     nb_param   = function() {
-      as.integer(
-        self$p * self$d + self$p * (self$p + 1L)/2L +
-          switch(private$ziparam,
-                 "single" = 1L,
-                 "row"    = self$n,
-                 "col"    =  self$p,
-                 "covar"  =  self$p * self$d)
-      )
+      self$nb_param_zi + self$nb_param_pln
     },
     #' @field model_par a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant)
     model_par  = function() {list(B = private$B, B0 = private$B0, Pi = private$Pi, Omega = private$Omega, Sigma = private$Sigma)},
@@ -583,15 +588,9 @@ ZIPLNfit_diagonal <- R6Class(
     }
   ),
   active = list(
-    #' @field nb_param number of parameters in the current PLN model
-    nb_param   = function() {
-      res <-  self$p * self$d + self$p +
-        switch(private$ziparam,
-               "single" = 1L,
-               "row"    = self$n,
-               "col"    =  self$p,
-               "covar"  =  self$p * self$d)
-      as.integer(res)
+    #' @field nb_param_pln number of parameters in the PLN part of the current model
+    nb_param_pln   = function() {
+      as.integer(self$p * self$d + self$p)
     },
     #' @field vcov_model character: the model used for the residual covariance
     vcov_model = function() {"diagonal"}
@@ -632,15 +631,9 @@ ZIPLNfit_spherical <- R6Class(
     }
   ),
   active = list(
-    #' @field nb_param number of parameters in the current PLN model
-    nb_param   = function() {
-      res <-  self$p * self$d + 1L +
-        switch(private$ziparam,
-               "single" = 1L,
-               "row"    = self$n,
-               "col"    =  self$p,
-               "covar"  =  self$p * self$d)
-      as.integer(res)
+    #' @field nb_param_pln number of parameters in the PLN part of the current model
+    nb_param_pln   = function() {
+      as.integer(self$p * self$d + 1L)
     },
     #' @field vcov_model character: the model used for the residual covariance
     vcov_model = function() {"spherical"}
@@ -686,15 +679,9 @@ ZIPLNfit_fixed <- R6Class(
     }
   ),
   active = list(
-    #' @field nb_param number of parameters in the current PLN model
-    nb_param   = function() {
-      res <-  self$p * self$d +
-        switch(private$ziparam,
-               "single" = 1L,
-               "row"    = self$n,
-               "col"    =  self$p,
-               "covar"  =  self$p * self$d)
-      as.integer(res)
+    #' @field nb_param_pln number of parameters in the PLN part of the current model
+    nb_param_pln   = function() {
+      as.integer(self$p * self$d + 0L)
     },
     #' @field vcov_model character: the model used for the residual covariance
     vcov_model = function() {"fixed"}
@@ -813,15 +800,9 @@ ZIPLNfit_sparse <- R6Class(
     penalty_weights = function() {private$rho},
     #' @field n_edges number of edges if the network (non null coefficient of the sparse precision matrix)
     n_edges         = function() {sum(private$Omega[upper.tri(private$Omega, diag = FALSE)] != 0)},
-    #' @field nb_param number of parameters in the current PLN model
-    nb_param   = function() {
-      res <-  self$p * self$d + self$n_edges +
-        switch(private$ziparam,
-               "single" = 1L,
-               "row"    = self$n,
-               "col"    =  self$p,
-               "covar"  =  self$p * self$d)
-      as.integer(res)
+    #' @field nb_param_pln number of parameters in the PLN part of the current model
+    nb_param_pln   = function() {
+      as.integer(self$p * self$d + self$n_edges)
     },
     #' @field vcov_model character: the model used for the residual covariance
     vcov_model = function() {"sparse"},