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krackhardt_datasets [2019/12/04 08:47] – [Using cutree] hkimscilkrackhardt_datasets [2019/12/13 14:11] (current) – hkimscil
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 <code> <code>
-krack_friend <- delete.edges(krack_full, E(krack_full)[E(krack_full)$friendship_tie==0])+krack_friend <- delete_edges(krack_full, E(krack_full)[E(krack_full)$friendship_tie==0])
 summary(krack_friend) summary(krack_friend)
 krack_friend[] krack_friend[]
  
-krack_advice <- delete.edges(krack_full, E(krack_full)[E(krack_full)$advice_tie==0])+krack_advice <- delete_edges(krack_full, E(krack_full)[E(krack_full)$advice_tie==0])
 summary(krack_advice) summary(krack_advice)
 krack_advice[] krack_advice[]
    
-krack_reports_to <- delete.edges(krack_full, E(krack_full)[E(krack_full)$reports_to_tie==0])+krack_reports_to <- delete_edges(krack_full, E(krack_full)[E(krack_full)$reports_to_tie==0])
 summary(krack_reports_to) summary(krack_reports_to)
 krack_reports_to[] krack_reports_to[]
Line 213: Line 213:
  
 <code> <code>
-# Next, we'll use the same procedure to add social-interaction+# Next, we'll use the same procedure to add advice
 # information. # information.
 krack_advice_matrix_row_to_col <- get.adjacency(krack_advice, attr='advice_tie') krack_advice_matrix_row_to_col <- get.adjacency(krack_advice, attr='advice_tie')
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 krack_advice_matrix <- rbind(krack_advice_matrix_row_to_col, krack_advice_matrix_col_to_row) krack_advice_matrix <- rbind(krack_advice_matrix_row_to_col, krack_advice_matrix_col_to_row)
 krack_advice_matrix krack_advice_matrix
- +</code> 
 + 
 + 
 +<code> 
 +krack_friend_matrix_row_to_col <- get.adjacency(krack_friend, attr='friendship_tie') 
 +krack_friend_matrix_row_to_col 
 + 
 +# To operate on a binary graph, simply leave off the "attr"  
 +# parameter: 
 +krack_friend_matrix_row_to_col_bin <- get.adjacency(krack_friend) 
 +krack_friend_matrix_row_to_col_bin 
 + 
 +# For this lab, we'll use the valued graph. The next step is to  
 +# concatenate it with its transpose in order to capture both  
 +# incoming and outgoing task interactions. 
 +krack_friend_matrix_col_to_row <- t(as.matrix(krack_friend_matrix_row_to_col)) 
 +krack_friend_matrix_col_to_row 
 + 
 +krack_friend_matrix <- rbind(krack_friend_matrix_row_to_col, krack_friend_matrix_col_to_row) 
 +krack_friend_matrix 
 +</code> 
 + 
 + 
 +<code>  
 +# ra (ar)
 krack_reports_to_advice_matrix <- rbind(krack_reports_to_matrix, krack_advice_matrix) krack_reports_to_advice_matrix <- rbind(krack_reports_to_matrix, krack_advice_matrix)
 krack_reports_to_advice_matrix krack_reports_to_advice_matrix
 +
 +# fa
 +krack_friend_advice_matrix <- rbind(krack_friend_matrix, krack_advice_matrix)
 +krack_friend_advice_matrix
 +
 +# fr
 +krack_friend_reports_to_matrix <- rbind(krack_friend_matrix, krack_reprots_to_matrix)
 +krack_friend_reports_to_matrix
 +
 +
 +# far
 +krack_friend_advice_reports_to_matrix <- rbind(krack_friend_advice_matrix, krack_reports_to_matrix)
 +krack_friend_advice_reports_to_matrix
 </code> </code>
 +
  
 <code> <code>
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 krack_reports_to_advice_cors <- cor(as.matrix(krack_reports_to_advice_matrix)) krack_reports_to_advice_cors <- cor(as.matrix(krack_reports_to_advice_matrix))
 krack_reports_to_advice_cors krack_reports_to_advice_cors
 +
 +krack_friend_advice_cors <- cor(as.matrix(krack_friend_advice_matrix))
 +krack_friend_advice_cors
 +
 +krack_friend_reports_to_cors <- cor(as.matrix(krack_friend_reports_to_matrix))
 +krack_friend_reports_to_cors
 +
 +krack_friend_advice_reports_to_cors <- cor(as.matrix(krack_friend_advice_reports_to_matrix))
 +krack_friend_advice_reports_to_cors
 +
 +
 </code> </code>
  
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 # or equal to 0; thus, highly dissimilar (i.e., negatively  # or equal to 0; thus, highly dissimilar (i.e., negatively 
 # correlated) actors have higher values. # correlated) actors have higher values.
-dissimilarity <- 1 - krack_reports_to_advice_cors +dissimilarity_ra <- 1 - krack_reports_to_advice_cors 
-krack_reports_to_dist <- as.dist(dissimilarity) +krack_reports_to_advice_dist <- as.dist(dissimilarity_ra) 
-krack_reports_to_dist+krack_reports_to_advice_dist
    
 +dissimilarity_fa <- 1 - krack_friend_advice_cors
 +krack_friend_advice_dist <- as.dist(dissimilarity_fa)
 +krack_friend_advice_dist
 +
 +dissimilarity_rf <- 1 - krack_reports_to_friend_cors
 +krack_reports_to_friend_dist <- as.dist(dissimilarity_rf)
 +krack_reports_to_friend_dist
 +
 +dissimilarity_far <- 1 - krack_friend_advice_reports_to_cors
 +krack_friend_advice_reports_to_dist <- as.dist(dissimilarity_far)
 +krack_friend_advice_reports_to_dist
 +
 +
 +
 # Note that it is also possible to use dist() directly on the  # Note that it is also possible to use dist() directly on the 
 # matrix. However, since cor() looks at associations between  # matrix. However, since cor() looks at associations between 
Line 1654: Line 1717:
 </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 1747:
 ##################### #####################
      
- 
- 
 ### NOTE ON DEDUCTIVE CLUSTERING ### NOTE ON DEDUCTIVE CLUSTERING
  
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 # 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 1773:
 # 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
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 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
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 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 
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 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: 2019/12/13 14:11 by hkimscil
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