added styling using styler package

R/evaluateTree.R

@@ -1,11 +1,11 @@
 #' Evaluate Tree -2LL
-#' 
+#'
 #' A helper function to evaluate the negative two log-likelihood (-2LL) of leaf (terminal) nodes for a
 #' dataset. When given a \code{\link{semtree}} and a unique dataset, the model
 #' estimates -2LL for the tree parameters and data subsets that fit the tree
 #' branching criteria.
-#' 
-#' 
+#'
+#'
 #' @param tree A fitted \code{\link{semtree}} object
 #' @param test_set Dataset to fit to a fitted \code{\link{semtree}} object
 #' @param data_type type of data ("raw", "cov", "cor")
@@ -26,32 +26,29 @@ evaluateTree <-
   function(tree,
            test_set,
            data_type = "raw",
-           leaf_ids = NULL)
-  {
+           leaf_ids = NULL) {
     # get a mapping of dataset rows to leaf ids
     if (is.null(leaf_ids)) {
       leaf_ids <- traverse(tree, test_set)
     }
-    
+
     # for each leaf, calculate deviance of each data row
     dev <- 0
     for (leaf_id in unique(leaf_ids))
     {
-      temp_set <- test_set[leaf_ids == leaf_id,]
-      
-      
+      temp_set <- test_set[leaf_ids == leaf_id, ]
+
+
       leaf <- getNodeById(tree, leaf_id)
-      
+
       # add up log-likelihoods
       dev <-
         dev + evaluateDataLikelihood(leaf$model, temp_set[, , drop = F], data_type)
     }
-    
+
     result <- list()
     result$deviance <- dev
     result$num_models <- length(unique(leaf_ids))
-    
+
     return(result)
-
-
   }

R/evaluateTreeFocus.R

@@ -4,60 +4,59 @@
 # computes the difference in log-likelihoods considering focus parameters
 #
 evaluateTreeFocus <-
-  function(tree, test_set, data_type="raw", leaf_ids=NULL)
-  {
-
+  function(tree, test_set, data_type = "raw", leaf_ids = NULL) {
     # get a mapping of dataset rows to leaf ids
     if (is.null(leaf_ids)) {
       leaf_ids <- traverse(tree, test_set)
     }
-    
+
     # for each leaf, calculate deviance of each data row
     dev <- 0
     for (leaf_id in unique(leaf_ids))
     {
       # get all data rows from current leaf
-      temp_set <- test_set[leaf_ids==leaf_id, ];
-      
+      temp_set <- test_set[leaf_ids == leaf_id, ]
+
       # get the leaf object
-      leaf <- getNodeById( tree, leaf_id)
-      
+      leaf <- getNodeById(tree, leaf_id)
+
       # test if node has a focus model
       if (is.null(leaf$focus.model)) {
         ui_warn("No focus model available!")
         return(NA)
       }
-      
+
       # evaluate log-likelihood from baseline and focus model
-      #baseline = evaluateDataLikelihood(leaf$model, temp_set[,,drop=F], data_type )
-      ll.focus = evaluateDataLikelihood(leaf$focus.model, 
-                                              temp_set[,,drop=F], data_type )
-
+      # baseline = evaluateDataLikelihood(leaf$model, temp_set[,,drop=F], data_type )
+      ll.focus <- evaluateDataLikelihood(
+        leaf$focus.model,
+        temp_set[, , drop = F], data_type
+      )
+
       # evaluate log-likelihood after permutation
-      
-      
+
+
       # add up log-likelihoods
       dev <- dev + ll.focus
     }
-    
+
     result <- list()
     result$deviance <- dev
     result$num_models <- length(unique(leaf_ids))
-
-    return(result);
-
+
+    return(result)
   }
 
 # TODO: finish this block
 # TODO: remove earlier computation of baseline ll
 # compute influence of focus parameter before permutation
-#ll.baseline <- eval.fun(tree, oob.data)$deviance
-#ll.baseline <- fitSubmodels(tree$model, subset1, subset2, 
+# ll.baseline <- eval.fun(tree, oob.data)$deviance
+# ll.baseline <- fitSubmodels(tree$model, subset1, subset2,
 #                            control, invariance=constraints$focus.parameters)
 # compute misfit of focus parameter after permutation
-#ll.permuted <- eval.fun(tree, oob.data.permuted)$deviance
-#ll.diff <- -ll.baseline + ll.permuted
-#ui_warn("Unfinished implementation!")
+# ll.permuted <- eval.fun(tree, oob.data.permuted)$deviance
+# ll.diff <- -ll.baseline + ll.permuted
+# ui_warn("Unfinished implementation!")
 
 
-#}
+# }