Differences

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

--- krackhardt_datasets [2019/12/04 08:25] – [correlation matrix out of the combined matrix (friend and advice)] hkimscil
+++ krackhardt_datasets [2019/12/04 08:37] – [correlation matrix out of the combined matrix (friend and advice)] hkimscil
@@ Line 1180: / Line 1180: @@
   0.18  0.19  0.09 0.25 -0.04  0.29  0.43 0.21  0.24  0.15  0.16  0.07  0.04 0.18 -0.05 0.21 -0.04 0.17  0.05  0.19  1.00
 >
 </code>
+===== Clustering with NetCluster =====
 <code>
 # To use correlation values in hierarchical NetCluster, they must
@@ Line 1189: / Line 1190: @@
 # or equal to 0; thus, highly dissimilar (i.e., negatively
 # correlated) actors have higher values.
-dissimilarity <- 1 - krack_reports_to_advice_cors
+dissimilarity <- 1 - krack_friend_advice_cors
-krack_reports_to_dist <- as.dist(dissimilarity)
+krack_friend_advice_dist <- as.dist(dissimilarity)
-krack_reports_to_dist
+krack_friend_advice_dist
+round(krack_friend_advice_dist, 2)
+</code>
+<code>
+> dissimilarity <- 1 - krack_friend_advice_cors
+> krack_friend_advice_dist <- as.dist(dissimilarity)
+> krack_friend_advice_dist
+         2         3         4         5         6         7         8         9        10        11        12        13        14        15        16
+  0.6979613
+  0.8575249 0.9962906
+  0.7363116 0.9270011 0.6283369
+  0.8780978 1.0673967 0.3531712 0.7059318
+  0.7795668 0.7501665 1.0674575 0.8827631 0.9687869
+  0.8449566 0.7162230 0.7017487 0.7191440 0.9152088 0.7750394
+  0.6278958 0.6088479 0.5888968 0.6645331 0.8072928 0.7750394 0.7000000
+  0.8219822 0.9080535 0.3936414 0.5843941 0.4553035 1.0129148 0.6200874 0.6200874
+0.7363116 1.1280473 0.8924630 0.7668898 0.8574762 0.8827631 0.8283658 0.8829767 0.8610256
+0.7777078 0.8190932 0.9794953 0.9585057 0.8983957 0.6192001 1.0231249 0.8612508 1.0019523 0.8069614
+0.8301584 0.4678936 0.9264149 0.9287819 0.9577800 0.5892800 0.9391419 0.5131355 0.8612755 1.1566799 0.7888998
+0.8612123 1.0175629 0.6601910 0.8497716 0.7376356 1.0739934 0.8334598 0.7605984 0.7460383 0.7815598 0.8724283 1.0950186
+0.6603169 0.6359272 0.7498106 0.9857564 0.8451932 0.5892800 0.6957097 0.6348516 0.6763096 0.9287819 0.6200197 0.6984127 0.8669740
+1.0353837 1.1750350 0.4113190 0.8148328 0.4652129 1.2382176 0.9087129 0.8554621 0.6686027 0.7151275 1.0070367 1.1111111 0.6389294 1.0000000
+0.6927451 0.7213478 0.8003192 0.6779039 0.8260258 0.7969077 0.8807289 0.3669458 0.8001599 0.8582777 0.7072142 0.6984887 0.6619397 0.7654912 0.9246222
+0.8264421 0.8723774 1.0621885 1.0011802 1.0944501 0.6910049 1.1512723 0.7806552 1.0753499 0.9516135 0.6536831 0.6409607 0.8598644 0.8066711 1.0138092 0.8334547
+0.8071821 0.8034186 0.7891129 0.8834539 0.8299012 0.8491973 0.9241684 0.7118399 0.8457098 0.6846748 0.9772033 0.9030858 0.7829389 0.8477062 0.7992491 0.8205011
+1.0024553 1.1396764 0.6955481 0.8288879 0.7125260 1.0334876 1.0158362 0.8495566 0.7158944 0.7251088 0.7637953 1.1301073 0.4799463 0.7831546 0.6675037 0.7079633
+0.7599978 0.8471748 0.4444666 0.6344915 0.5639677 0.9162762 0.5471372 0.6047743 0.4654338 0.6884506 0.8838517 0.8947696 0.6306140 0.7143745 0.6692757 0.7053813
+0.8176768 0.8114184 0.9095172 0.7456235 1.0370823 0.7065985 0.5723820 0.7861910 0.7608265 0.8457060 0.8393101 0.9302929 0.9610540 0.8187614 1.0487950 0.7940285
+        18        19        20
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+0.9873817
+0.8047610 0.8877177
+1.0685056 0.7908302 0.8154942
+1.0404292 0.8257051 0.9534437 0.8107489
+> round(krack_friend_advice_dist, 2)
+    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20
+  0.70
+  0.86 1.00
+  0.74 0.93 0.63
+  0.88 1.07 0.35 0.71
+  0.78 0.75 1.07 0.88 0.97
+  0.84 0.72 0.70 0.72 0.92 0.78
+  0.63 0.61 0.59 0.66 0.81 0.78 0.70
+  0.82 0.91 0.39 0.58 0.46 1.01 0.62 0.62
+0.74 1.13 0.89 0.77 0.86 0.88 0.83 0.88 0.86
+0.78 0.82 0.98 0.96 0.90 0.62 1.02 0.86 1.00 0.81
+0.83 0.47 0.93 0.93 0.96 0.59 0.94 0.51 0.86 1.16 0.79
+0.86 1.02 0.66 0.85 0.74 1.07 0.83 0.76 0.75 0.78 0.87 1.10
+0.66 0.64 0.75 0.99 0.85 0.59 0.70 0.63 0.68 0.93 0.62 0.70 0.87
+1.04 1.18 0.41 0.81 0.47 1.24 0.91 0.86 0.67 0.72 1.01 1.11 0.64 1.00
+0.69 0.72 0.80 0.68 0.83 0.80 0.88 0.37 0.80 0.86 0.71 0.70 0.66 0.77 0.92
+0.83 0.87 1.06 1.00 1.09 0.69 1.15 0.78 1.08 0.95 0.65 0.64 0.86 0.81 1.01 0.83
+0.81 0.80 0.79 0.88 0.83 0.85 0.92 0.71 0.85 0.68 0.98 0.90 0.78 0.85 0.80 0.82 0.99
+1.00 1.14 0.70 0.83 0.71 1.03 1.02 0.85 0.72 0.73 0.76 1.13 0.48 0.78 0.67 0.71 0.80 0.89
+0.76 0.85 0.44 0.63 0.56 0.92 0.55 0.60 0.47 0.69 0.88 0.89 0.63 0.71 0.67 0.71 1.07 0.79 0.82
+0.82 0.81 0.91 0.75 1.04 0.71 0.57 0.79 0.76 0.85 0.84 0.93 0.96 0.82 1.05 0.79 1.04 0.83 0.95 0.81
+>
+>
+</code>
+<code>
 # Note that it is also possible to use dist() directly on the
 # matrix. However, since cor() looks at associations between
@@ Line 1200: / Line 1277: @@
 # A variety of distance metrics are available; Euclidean
 # is the default.
-#m182_task_social_dist <- dist(t(m182_task_social_matrix))
+krack_friend_advice_dist2 <- dist(t(krack_friend_advice_matrix))
-#m182_task_social_dist
+krack_friend_advice_dist2
+</code>
+<code>
+> krack_friend_advice_dist2 <- dist(t(as.matrix(krack_friend_advice_matrix)))
+> krack_friend_advice_dist2
+        2        3        4        5        6        7        8        9       10       11       12       13       14       15       16       17       18
+  5.291503
+  5.744563 6.244998
+  5.385165 6.082763 4.898979
+  5.916080 6.557439 3.741657 5.291503
+  5.385165 5.385165 6.000000 5.656854 6.000000
+  5.656854 5.291503 5.000000 5.196152 5.916080 5.000000
+  4.898979 4.898979 4.582576 5.000000 5.567764 5.000000 4.898979
+  5.567764 5.916080 3.741657 4.690416 4.242641 5.656854 4.582576 4.582576
+5.385165 6.708204 5.830952 5.477226 5.830952 5.656854 5.567764 5.744563 5.656854
+5.567764 5.744563 6.164414 6.164414 6.000000 4.898979 6.244998 5.744563 6.164414 5.656854
+5.567764 4.358899 5.656854 5.830952 6.000000 4.242641 5.567764 4.123106 5.291503 6.480741 5.477226
+5.567764 6.082763 4.690416 5.477226 5.291503 5.291503 5.000000 4.795832 4.690416 5.291503 5.656854 5.477226
+5.000000 5.000000 5.099020 6.000000 5.656854 4.242641 4.795832 4.582576 4.690416 5.830952 4.898979 4.690416 4.898979
+6.480741 6.928203 4.123106 5.744563 4.358899 6.855655 6.000000 5.830952 5.196152 5.385165 6.403124 6.557439 5.196152 6.244998
+5.099020 5.291503 5.196152 5.000000 5.567764 4.795832 5.291503 3.464102 5.000000 5.567764 5.196152 4.582576 4.123106 4.795832 6.000000
+5.830952 6.000000 6.557439 6.403124 6.708204 5.385165 6.782330 5.656854 6.557439 6.244998 5.196152 5.196152 5.916080 5.744563 6.480741 5.830952
+5.744563 5.744563 5.656854 6.000000 5.830952 5.830952 6.082763 5.385165 5.830952 5.291503 6.324555 6.000000 5.656854 5.830952 5.744563 5.744563 6.403124
+6.244998 6.708204 5.099020 5.656854 5.291503 6.000000 6.082763 5.567764 5.099020 5.291503 5.477226 6.324555 4.242641 5.291503 5.196152 5.000000 5.744563 6.000000
+5.385165 5.744563 4.000000 4.898979 4.690416 5.477226 4.358899 4.582576 4.000000 5.099020 5.830952 5.477226 4.472136 4.898979 5.196152 4.795832 6.557439 5.656854
+5.744563 5.744563 6.000000 5.477226 6.480741 5.291503 4.795832 5.567764 5.477226 5.830952 5.830952 6.000000 6.000000 5.656854 6.557439 5.567764 6.557439 5.830952
+       20
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+5.477226
+6.164414 5.656854
+> round(krack_friend_advice_dist2,2)
+    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20
+  5.29
+  5.74 6.24
+  5.39 6.08 4.90
+  5.92 6.56 3.74 5.29
+  5.39 5.39 6.00 5.66 6.00
+  5.66 5.29 5.00 5.20 5.92 5.00
+  4.90 4.90 4.58 5.00 5.57 5.00 4.90
+  5.57 5.92 3.74 4.69 4.24 5.66 4.58 4.58
+5.39 6.71 5.83 5.48 5.83 5.66 5.57 5.74 5.66
+5.57 5.74 6.16 6.16 6.00 4.90 6.24 5.74 6.16 5.66
+5.57 4.36 5.66 5.83 6.00 4.24 5.57 4.12 5.29 6.48 5.48
+5.57 6.08 4.69 5.48 5.29 5.29 5.00 4.80 4.69 5.29 5.66 5.48
+5.00 5.00 5.10 6.00 5.66 4.24 4.80 4.58 4.69 5.83 4.90 4.69 4.90
+6.48 6.93 4.12 5.74 4.36 6.86 6.00 5.83 5.20 5.39 6.40 6.56 5.20 6.24
+5.10 5.29 5.20 5.00 5.57 4.80 5.29 3.46 5.00 5.57 5.20 4.58 4.12 4.80 6.00
+5.83 6.00 6.56 6.40 6.71 5.39 6.78 5.66 6.56 6.24 5.20 5.20 5.92 5.74 6.48 5.83
+5.74 5.74 5.66 6.00 5.83 5.83 6.08 5.39 5.83 5.29 6.32 6.00 5.66 5.83 5.74 5.74 6.40
+6.24 6.71 5.10 5.66 5.29 6.00 6.08 5.57 5.10 5.29 5.48 6.32 4.24 5.29 5.20 5.00 5.74 6.00
+5.39 5.74 4.00 4.90 4.69 5.48 4.36 4.58 4.00 5.10 5.83 5.48 4.47 4.90 5.20 4.80 6.56 5.66 5.48
+5.74 5.74 6.00 5.48 6.48 5.29 4.80 5.57 5.48 5.83 5.83 6.00 6.00 5.66 6.56 5.57 6.56 5.83 6.16 5.66
+>
+</code>
+===== Using hclust =====
+<code>
 # hclust() performs a hierarchical agglomerative NetCluster
 # operation based on the values in the dissimilarity matrix
@@ Line 1212: / Line 1362: @@
 # the "method" parameter.
-krack_reports_to_advice_hclust <- hclust(krack_reports_to_dist)
+krack_friend_advice_hclust <- hclust(krack_friend_advice_dist)
-plot(krack_reports_to_advice_hclust)
+plot(krack_friend_advice_hclust)
+</code>
+{{:pasted:20191204-080711.png}}
+===== Using cutree =====
+<code>
 # cutree() allows us to use the output of hclust() to set
 # different numbers of clusters and assign vertices to clusters