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--- krackhardt_datasets [2019/12/04 08:47] – [Using cutree] hkimscil
+++ krackhardt_datasets [2019/12/04 09:01] – [Using cutree] hkimscil
@@ Line 1654: / Line 1654: @@
 </code>
 {{:pasted:20191204-081317.png}}
+===== decision on # of clusters  =====
 <code>
 # From a visual inspection of the correlation matrix, we can
 # decide on the proper number of clusters in this network.
-# For this network, we'll use 4. (Note that the 1-cluster
+# For this network, we'll use 6. (Note that the 1-cluster
 # solution doesn't appear on the plot because its correlation
 # with the observed correlation matrix is undefined.)
 num_clusters = 4
-clusters <- cutree(krack_reports_to_advice_hclust, k = num_clusters)
+clusters <- cutree(krack_friend_advice_hclust, k = num_clusters)
 clusters
-cluster_cor_mat <- clusterCorr(krack_reports_to_advice_cors,
+cluster_cor_mat <- clusterCorr(krack_friend_advice_cors, clusters)
-                                            clusters)
+round(cluster_cor_mat,2)
-cluster_cor_mat
 # Let's look at the correlation between this cluster configuration
 # and the observed correlation matrix. This should match the
 # corresponding value from clustered_observed_cors above.
-gcor(cluster_cor_mat, krack_reports_to_advice_cors)
+gcor(cluster_cor_mat, krack_friend_advice_cors)
+</code>
+<code>
 #####################
 # Questions:
@@ Line 1680: / Line 1684: @@
 #####################
 ### NOTE ON DEDUCTIVE CLUSTERING
@@ Line 1698: / Line 1700: @@
 # You could then examine the fitness of this cluster configuration
 # as follows:
-deductive_cluster_cor_mat <- generate_cluster_cor_mat(
+deductive_cluster_cor_mat <- generate_cluster_cor_mat(krack_friend_advice_cors, deductive_clusters)
-  krack_reports_to_advice_cors,
-  deductive_clusters)
 deductive_cluster_cor_mat
-gcor(deductive_cluster_cor_mat, krack_reports_to_advice_cors)
+gcor(deductive_cluster_cor_mat, krack_friend_advice_cors)
 ### END NOTE ON DEDUCTIVE CLUSTERING
@@ Line 1710: / Line 1710: @@
 # networks.
-# Task valued
+# Friendship valued
-task_mean <- mean(as.matrix(krack_reports_to_matrix_row_to_col)_
+friend_mean <- mean(as.matrix(krack_friend_matrix_row_to_col))
-task_mean
+friend_mean
-task_valued_blockmodel <- blockmodel(krack_reports_to_matrix_row_to_col, clusters)
+friend_valued_blockmodel <- blockmodel(krack_friend_matrix_row_to_col, clusters)
-task_valued_blockmodel
+friend_valued_blockmodel
-# Task binary
+# friend binary
-task_density <- graph.density(krack_reports_to)
+friend_density <- graph.density(krack_friend)
-task_density
+friend_density
-task_binary_blockmodel <- blockmodel(as.matrix(krack_reports_to_matrix_row_to_col_bin), clusters)
+friend_binary_blockmodel <- blockmodel(as.matrix(krack_friend_matrix_row_to_col_bin), clusters)
-task_binary_blockmodel
+friend_binary_blockmodel
-# Social valued
+# advice valued
 advice_mean <- mean(as.matrix(krack_advice_matrix_row_to_col))
 advice_mean
@@ Line 1732: / Line 1732: @@
 advice_valued_blockmodel
-# Social binary
+# advice binary
 advice_density <- graph.density(krack_advice)
 advice_density
@@ Line 1738: / Line 1738: @@
 advice_binary_blockmodel <- blockmodel(as.matrix(krack_advice_matrix_row_to_col_bin), clusters)
 advice_binary_blockmodel
 # We can also permute the network to examine the within- and
@@ Line 1744: / Line 1746: @@
 cluster_cor_mat_per <- permute_matrix(clusters, cluster_cor_mat)
 cluster_cor_mat_per
+</code>
+<code>
 #####################
 # Questions: