User Tools

Site Tools


krackhardt_datasets

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
krackhardt_datasets [2019/12/04 08:17]
hkimscil [Using cutree]
krackhardt_datasets [2019/12/04 08:31] (current)
hkimscil [Using cutree]
Line 1654: Line 1654:
 </​code>​ </​code>​
 {{:​pasted:​20191204-081317.png}} {{:​pasted:​20191204-081317.png}}
 +
 +===== decision on # of clusters ​ =====
  
 <​code> ​ <​code> ​
 # From a visual inspection of the correlation matrix, we can  # From a visual inspection of the correlation matrix, we can 
 # decide on the proper number of clusters in this network. ​ # 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 ​ # solution doesn'​t appear on the plot because its correlation ​
 # with the observed correlation matrix is undefined.) # with the observed correlation matrix is undefined.)
 num_clusters = 4 num_clusters = 4
-clusters <- cutree(krack_reports_to_advice_hclust, k = num_clusters)+clusters <- cutree(krack_friend_advice_hclust, k = num_clusters)
 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 ​ # Let's look at the correlation between this cluster configuration ​
 # and the observed correlation matrix. This should match the  # and the observed correlation matrix. This should match the 
 # corresponding value from clustered_observed_cors above. # 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: # Questions:
Line 1680: Line 1684:
 #####################​ #####################​
   ​   ​
- 
- 
 ### NOTE ON DEDUCTIVE CLUSTERING ### NOTE ON DEDUCTIVE CLUSTERING
  
Line 1698: Line 1700:
 # You could then examine the fitness of this cluster configuration # You could then examine the fitness of this cluster configuration
 # as follows: # 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 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 ### END NOTE ON DEDUCTIVE CLUSTERING
Line 1710: Line 1710:
 # networks. # 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 <- mean(as.matrix(krack_advice_matrix_row_to_col))
 advice_mean advice_mean
Line 1732: Line 1732:
 advice_valued_blockmodel advice_valued_blockmodel
  
-Social ​binary+advice ​binary
 advice_density <- graph.density(krack_advice) advice_density <- graph.density(krack_advice)
 advice_density advice_density
Line 1738: Line 1738:
 advice_binary_blockmodel <- blockmodel(as.matrix(krack_advice_matrix_row_to_col_bin),​ clusters) advice_binary_blockmodel <- blockmodel(as.matrix(krack_advice_matrix_row_to_col_bin),​ clusters)
 advice_binary_blockmodel advice_binary_blockmodel
 +
 +
  
 # We can also permute the network to examine the within- and  # 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 <- permute_matrix(clusters,​ cluster_cor_mat)
 cluster_cor_mat_per cluster_cor_mat_per
 +</​code>​
  
 +<​code>​
 #####################​ #####################​
 # Questions: # Questions:
krackhardt_datasets.txt · Last modified: 2019/12/04 08:31 by hkimscil