v1.0.1

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: netrics
 Title: Many Marks, Measures, Memberships, and Motifs for Networks
-Version: 1.0.0
+Version: 1.0.1
 Description: Many tools for calculating network, node, or tie
    marks, measures, motifs and memberships of many different types of networks.
    Marks identify structural positions, measures quantify network properties,

NEWS.md

@@ -1,3 +1,9 @@
+# netrics 1.0.1
+
+## Methods
+
+- Fixed `cluster_cosine()` to cluster nodes and not census features (thanks @Kaladani)
+
 # netrics 1.0.0
 
 ## Package

R/method_cluster.R

@@ -51,7 +51,7 @@ cluster_hierarchical <- function(motif, distance){
 #'   and this is given to `stats::hclust` to enable dendrogram construction etc.
 #' @export
 cluster_cosine <- function(motif, distance){
-  cosines <- manynet::to_cosine(motif)
+  cosines <- manynet::to_cosine(t(motif))
   dissimilarity <- 1 - cosines
   distances <- stats::dist(dissimilarity, method = distance)
   hc <- stats::hclust(distances)

tests/testthat/test-model_cluster.R

@@ -2,7 +2,17 @@ test_that("cluster_cosine works", {
   expect_s3_class(cluster_cosine(node_x_triad(ison_monks), distance = "euclidean"), "hclust")
 })
 
-test_that("cluster_cosine works", {
+test_that("cluster_cosine clusters nodes and not census features", {
+  # `ison_algebra` gives a 16 x 96 census,
+  # so a membership of the wrong length is visible here
+  expect_equal(length(node_in_structural(ison_algebra, cluster = "cosine")),
+               c(net_nodes(ison_algebra)))
+  expect_equal(nrow(as.matrix(cluster_cosine(node_x_tie(ison_algebra),
+                                             distance = "euclidean")$distances)),
+               c(net_nodes(ison_algebra)))
+})
+
+test_that("cluster_concor works", {
   unlab_2mode <- generate_random(c(6,6))
   expect_s3_class(cluster_concor(unlab_2mode, node_x_tetrad(unlab_2mode)), "hclust")
 })