krackhardt_datasets
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krackhardt_datasets [2019/12/04 08:25] – [correlation matrix out of the combined matrix (friend and advice)] hkimscil | krackhardt_datasets [2019/12/13 14:07] – hkimscil | ||
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Line 65: | Line 65: | ||
< | < | ||
- | krack_friend <- delete.edges(krack_full, | + | krack_friend <- delete_edges(krack_full, |
summary(krack_friend) | summary(krack_friend) | ||
krack_friend[] | krack_friend[] | ||
- | krack_advice <- delete.edges(krack_full, | + | krack_advice <- delete_edges(krack_full, |
summary(krack_advice) | summary(krack_advice) | ||
krack_advice[] | krack_advice[] | ||
- | krack_reports_to <- delete.edges(krack_full, | + | krack_reports_to <- delete_edges(krack_full, |
summary(krack_reports_to) | summary(krack_reports_to) | ||
krack_reports_to[] | krack_reports_to[] | ||
Line 213: | Line 213: | ||
< | < | ||
- | # 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, | krack_advice_matrix_row_to_col <- get.adjacency(krack_advice, | ||
Line 226: | Line 226: | ||
krack_advice_matrix <- rbind(krack_advice_matrix_row_to_col, | krack_advice_matrix <- rbind(krack_advice_matrix_row_to_col, | ||
krack_advice_matrix | krack_advice_matrix | ||
- | + | </ | |
+ | |||
+ | |||
+ | < | ||
+ | krack_friend_matrix_row_to_col <- get.adjacency(krack_friend, | ||
+ | krack_friend_matrix_row_to_col | ||
+ | |||
+ | # To operate on a binary graph, simply leave off the " | ||
+ | # 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 | ||
+ | </ | ||
+ | |||
+ | |||
+ | < | ||
+ | # ra (ar) | ||
krack_reports_to_advice_matrix <- rbind(krack_reports_to_matrix, | krack_reports_to_advice_matrix <- rbind(krack_reports_to_matrix, | ||
krack_reports_to_advice_matrix | krack_reports_to_advice_matrix | ||
+ | |||
+ | # fa | ||
+ | krack_friend_advice_matrix <- rbind(krack_friend_matrix, | ||
+ | krack_friend_advice_matrix | ||
+ | |||
+ | # far | ||
+ | krack_friend_advice_reports_to_matrix <- rbind(krack_friend_advice_matrix, | ||
+ | krack_friend_advice_reports_to_matrix | ||
</ | </ | ||
+ | |||
< | < | ||
Line 238: | Line 271: | ||
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_advice_reports_to_cors <- cor(as.matrix(krack_friend_advice_reports_to_matrix)) | ||
+ | krack_friend_advice_reports_to_cors | ||
+ | |||
+ | |||
</ | </ | ||
Line 246: | Line 287: | ||
# 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 | + | dissimilarity_ra |
- | krack_reports_to_dist | + | krack_reports_to_advice_dist |
- | 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 1180: | Line 1235: | ||
21 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 | 21 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 | ||
> | > | ||
- | |||
</ | </ | ||
+ | |||
+ | ===== Clustering with NetCluster ===== | ||
< | < | ||
# To use correlation values in hierarchical NetCluster, they must | # To use correlation values in hierarchical NetCluster, they must | ||
Line 1189: | Line 1245: | ||
# 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 <- 1 - krack_friend_advice_cors |
- | krack_reports_to_dist | + | krack_friend_advice_dist |
- | krack_reports_to_dist | + | krack_friend_advice_dist |
- | + | round(krack_friend_advice_dist, | |
+ | </ | ||
+ | |||
+ | < | ||
+ | > dissimilarity <- 1 - krack_friend_advice_cors | ||
+ | > krack_friend_advice_dist <- as.dist(dissimilarity) | ||
+ | > krack_friend_advice_dist | ||
+ | | ||
+ | 2 0.6979613 | ||
+ | 3 0.8575249 0.9962906 | ||
+ | 4 0.7363116 0.9270011 0.6283369 | ||
+ | 5 0.8780978 1.0673967 0.3531712 0.7059318 | ||
+ | 6 0.7795668 0.7501665 1.0674575 0.8827631 0.9687869 | ||
+ | 7 0.8449566 0.7162230 0.7017487 0.7191440 0.9152088 0.7750394 | ||
+ | 8 0.6278958 0.6088479 0.5888968 0.6645331 0.8072928 0.7750394 0.7000000 | ||
+ | 9 0.8219822 0.9080535 0.3936414 0.5843941 0.4553035 1.0129148 0.6200874 0.6200874 | ||
+ | 10 0.7363116 1.1280473 0.8924630 0.7668898 0.8574762 0.8827631 0.8283658 0.8829767 0.8610256 | ||
+ | 11 0.7777078 0.8190932 0.9794953 0.9585057 0.8983957 0.6192001 1.0231249 0.8612508 1.0019523 0.8069614 | ||
+ | 12 0.8301584 0.4678936 0.9264149 0.9287819 0.9577800 0.5892800 0.9391419 0.5131355 0.8612755 1.1566799 0.7888998 | ||
+ | 13 0.8612123 1.0175629 0.6601910 0.8497716 0.7376356 1.0739934 0.8334598 0.7605984 0.7460383 0.7815598 0.8724283 1.0950186 | ||
+ | 14 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 | ||
+ | 15 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 | ||
+ | 16 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 | ||
+ | 17 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 | ||
+ | 18 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 | ||
+ | 19 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 | ||
+ | 20 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 | ||
+ | 21 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 | ||
+ | 17 18 19 20 | ||
+ | 2 | ||
+ | 3 | ||
+ | 4 | ||
+ | 5 | ||
+ | 6 | ||
+ | 7 | ||
+ | 8 | ||
+ | 9 | ||
+ | 10 | ||
+ | 11 | ||
+ | 12 | ||
+ | 13 | ||
+ | 14 | ||
+ | 15 | ||
+ | 16 | ||
+ | 17 | ||
+ | 18 0.9873817 | ||
+ | 19 0.8047610 0.8877177 | ||
+ | 20 1.0685056 0.7908302 0.8154942 | ||
+ | 21 1.0404292 0.8257051 0.9534437 0.8107489 | ||
+ | > round(krack_friend_advice_dist, | ||
+ | 1 2 3 4 5 6 7 8 9 | ||
+ | 2 0.70 | ||
+ | 3 0.86 1.00 | ||
+ | 4 0.74 0.93 0.63 | ||
+ | 5 0.88 1.07 0.35 0.71 | ||
+ | 6 0.78 0.75 1.07 0.88 0.97 | ||
+ | 7 0.84 0.72 0.70 0.72 0.92 0.78 | ||
+ | 8 0.63 0.61 0.59 0.66 0.81 0.78 0.70 | ||
+ | 9 0.82 0.91 0.39 0.58 0.46 1.01 0.62 0.62 | ||
+ | 10 0.74 1.13 0.89 0.77 0.86 0.88 0.83 0.88 0.86 | ||
+ | 11 0.78 0.82 0.98 0.96 0.90 0.62 1.02 0.86 1.00 0.81 | ||
+ | 12 0.83 0.47 0.93 0.93 0.96 0.59 0.94 0.51 0.86 1.16 0.79 | ||
+ | 13 0.86 1.02 0.66 0.85 0.74 1.07 0.83 0.76 0.75 0.78 0.87 1.10 | ||
+ | 14 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 | ||
+ | 15 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 | ||
+ | 16 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 | ||
+ | 17 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 | ||
+ | 18 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 | ||
+ | 19 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 | ||
+ | 20 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 | ||
+ | 21 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 | ||
+ | > | ||
+ | > | ||
+ | </ | ||
+ | |||
+ | |||
+ | < | ||
# 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 1200: | Line 1332: | ||
# A variety of distance metrics are available; Euclidean | # A variety of distance metrics are available; Euclidean | ||
# is the default. | # is the default. | ||
- | # | + | krack_friend_advice_dist2 |
- | # | + | krack_friend_advice_dist2 |
- | + | </ | |
+ | |||
+ | < | ||
+ | > krack_friend_advice_dist2 <- dist(t(as.matrix(krack_friend_advice_matrix))) | ||
+ | > krack_friend_advice_dist2 | ||
+ | 1 2 3 4 5 6 7 8 9 | ||
+ | 2 5.291503 | ||
+ | 3 5.744563 6.244998 | ||
+ | 4 5.385165 6.082763 4.898979 | ||
+ | 5 5.916080 6.557439 3.741657 5.291503 | ||
+ | 6 5.385165 5.385165 6.000000 5.656854 6.000000 | ||
+ | 7 5.656854 5.291503 5.000000 5.196152 5.916080 5.000000 | ||
+ | 8 4.898979 4.898979 4.582576 5.000000 5.567764 5.000000 4.898979 | ||
+ | 9 5.567764 5.916080 3.741657 4.690416 4.242641 5.656854 4.582576 4.582576 | ||
+ | 10 5.385165 6.708204 5.830952 5.477226 5.830952 5.656854 5.567764 5.744563 5.656854 | ||
+ | 11 5.567764 5.744563 6.164414 6.164414 6.000000 4.898979 6.244998 5.744563 6.164414 5.656854 | ||
+ | 12 5.567764 4.358899 5.656854 5.830952 6.000000 4.242641 5.567764 4.123106 5.291503 6.480741 5.477226 | ||
+ | 13 5.567764 6.082763 4.690416 5.477226 5.291503 5.291503 5.000000 4.795832 4.690416 5.291503 5.656854 5.477226 | ||
+ | 14 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 | ||
+ | 15 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 | ||
+ | 16 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 | ||
+ | 17 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 | ||
+ | 18 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 | ||
+ | 19 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 | ||
+ | 20 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 | ||
+ | 21 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 | ||
+ | | ||
+ | 2 | ||
+ | 3 | ||
+ | 4 | ||
+ | 5 | ||
+ | 6 | ||
+ | 7 | ||
+ | 8 | ||
+ | 9 | ||
+ | 10 | ||
+ | 11 | ||
+ | 12 | ||
+ | 13 | ||
+ | 14 | ||
+ | 15 | ||
+ | 16 | ||
+ | 17 | ||
+ | 18 | ||
+ | 19 | ||
+ | 20 5.477226 | ||
+ | 21 6.164414 5.656854 | ||
+ | > round(krack_friend_advice_dist2, | ||
+ | 1 2 3 4 5 6 7 8 9 | ||
+ | 2 5.29 | ||
+ | 3 5.74 6.24 | ||
+ | 4 5.39 6.08 4.90 | ||
+ | 5 5.92 6.56 3.74 5.29 | ||
+ | 6 5.39 5.39 6.00 5.66 6.00 | ||
+ | 7 5.66 5.29 5.00 5.20 5.92 5.00 | ||
+ | 8 4.90 4.90 4.58 5.00 5.57 5.00 4.90 | ||
+ | 9 5.57 5.92 3.74 4.69 4.24 5.66 4.58 4.58 | ||
+ | 10 5.39 6.71 5.83 5.48 5.83 5.66 5.57 5.74 5.66 | ||
+ | 11 5.57 5.74 6.16 6.16 6.00 4.90 6.24 5.74 6.16 5.66 | ||
+ | 12 5.57 4.36 5.66 5.83 6.00 4.24 5.57 4.12 5.29 6.48 5.48 | ||
+ | 13 5.57 6.08 4.69 5.48 5.29 5.29 5.00 4.80 4.69 5.29 5.66 5.48 | ||
+ | 14 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 | ||
+ | 15 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 | ||
+ | 16 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 | ||
+ | 17 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 | ||
+ | 18 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 | ||
+ | 19 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 | ||
+ | 20 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 | ||
+ | 21 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 | ||
+ | > | ||
+ | </ | ||
+ | |||
+ | ===== Using hclust ===== | ||
+ | < | ||
# hclust() performs a hierarchical agglomerative NetCluster | # hclust() performs a hierarchical agglomerative NetCluster | ||
# operation based on the values in the dissimilarity matrix | # operation based on the values in the dissimilarity matrix | ||
Line 1212: | Line 1417: | ||
# the " | # the " | ||
- | krack_reports_to_advice_hclust | + | krack_friend_advice_hclust |
- | plot(krack_reports_to_advice_hclust) | + | plot(krack_friend_advice_hclust) |
- | + | </ | |
+ | |||
+ | {{: | ||
+ | |||
+ | |||
+ | ===== Using cutree ===== | ||
+ | < | ||
# cutree() allows us to use the output of hclust() to set | # cutree() allows us to use the output of hclust() to set | ||
# different numbers of clusters and assign vertices to clusters | # different numbers of clusters and assign vertices to clusters | ||
# as appropriate. For example: | # as appropriate. For example: | ||
- | cutree(krack_reports_to_advice_hclust, k=2) | + | cutree(krack_friend_advice_hclust, k=2) |
- | + | </ | |
+ | |||
+ | < | ||
+ | > cutree(krack_friend_advice_hclust, | ||
+ | | ||
+ | | ||
+ | > | ||
+ | > | ||
+ | </ | ||
+ | |||
+ | < | ||
# Now we'll try to figure out the number of clusters that best | # Now we'll try to figure out the number of clusters that best | ||
# describes the underlying data. To do this, we'll loop through | # describes the underlying data. To do this, we'll loop through | ||
Line 1243: | Line 1464: | ||
# set a variable for our number of vertices. | # set a variable for our number of vertices. | ||
clustered_observed_cors = vector() | clustered_observed_cors = vector() | ||
- | num_vertices = length(V(krack_reports_to)) | + | num_vertices = length(V(krack_advice)) |
- | + | ||
# Next, we loop through the different possible cluster | # Next, we loop through the different possible cluster | ||
# configurations, | # configurations, | ||
Line 1251: | Line 1473: | ||
# pdf(" | # pdf(" | ||
- | clustered_observed_cors < | + | clustered_observed_cors < |
clustered_observed_cors | clustered_observed_cors | ||
plot(clustered_observed_cors$correlations) | plot(clustered_observed_cors$correlations) | ||
# dev.off() | # dev.off() | ||
- | + | ||
clustered_observed_cors$correlations | clustered_observed_cors$correlations | ||
+ | </ | ||
+ | |||
+ | < | ||
+ | > clustered_observed_cors < | ||
+ | Warning message: | ||
+ | In cor(as.vector(d[g1[i], | ||
+ | 표준편차가 0입니다 | ||
+ | > clustered_observed_cors | ||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] NA | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.4896211 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.5944114 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.6398013 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.6538231 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.6723019 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.7019599 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.727137 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.7743714 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.7919439 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.8093965 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.8445199 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.8700886 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.8844067 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.9115517 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.9403353 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.9502702 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.9633198 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.9762881 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 0.9895545 | ||
+ | |||
+ | $label | ||
+ | [1] " | ||
+ | |||
+ | $clusters | ||
+ | | ||
+ | | ||
+ | |||
+ | $correlation | ||
+ | [1] 1 | ||
+ | |||
+ | $correlations | ||
+ | | ||
+ | [17] 0.9502702 0.9633198 0.9762881 0.9895545 1.0000000 | ||
+ | |||
+ | > plot(clustered_observed_cors$correlations) | ||
+ | > | ||
+ | > clustered_observed_cors$correlations | ||
+ | | ||
+ | [17] 0.9502702 0.9633198 0.9762881 0.9895545 1.0000000 | ||
+ | > | ||
+ | </ | ||
+ | {{: | ||
+ | |||
+ | ===== decision on # of clusters | ||
+ | |||
+ | < | ||
# 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' | # solution doesn' | ||
# 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) |
- | | + | 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, | + | gcor(cluster_cor_mat, |
- | + | ||
+ | </ | ||
+ | |||
+ | < | ||
##################### | ##################### | ||
# Questions: | # Questions: | ||
Line 1281: | Line 1739: | ||
##################### | ##################### | ||
| | ||
- | |||
- | |||
### NOTE ON DEDUCTIVE CLUSTERING | ### NOTE ON DEDUCTIVE CLUSTERING | ||
Line 1299: | Line 1755: | ||
# 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_cluster_cor_mat | deductive_cluster_cor_mat | ||
- | gcor(deductive_cluster_cor_mat, | + | gcor(deductive_cluster_cor_mat, |
### END NOTE ON DEDUCTIVE CLUSTERING | ### END NOTE ON DEDUCTIVE CLUSTERING | ||
Line 1311: | Line 1765: | ||
# networks. | # networks. | ||
- | # Task valued | + | # Friendship |
- | task_mean | + | friend_mean |
- | task_mean | + | friend_mean |
- | task_valued_blockmodel | + | friend_valued_blockmodel |
- | task_valued_blockmodel | + | friend_valued_blockmodel |
- | # Task binary | + | # friend |
- | task_density | + | friend_density |
- | task_density | + | friend_density |
- | task_binary_blockmodel | + | friend_binary_blockmodel |
- | task_binary_blockmodel | + | friend_binary_blockmodel |
- | # Social | + | # advice |
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 1333: | Line 1787: | ||
advice_valued_blockmodel | advice_valued_blockmodel | ||
- | # Social | + | # advice |
advice_density <- graph.density(krack_advice) | advice_density <- graph.density(krack_advice) | ||
advice_density | advice_density | ||
Line 1339: | Line 1793: | ||
advice_binary_blockmodel <- blockmodel(as.matrix(krack_advice_matrix_row_to_col_bin), | advice_binary_blockmodel <- blockmodel(as.matrix(krack_advice_matrix_row_to_col_bin), | ||
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 1345: | Line 1801: | ||
cluster_cor_mat_per <- permute_matrix(clusters, | cluster_cor_mat_per <- permute_matrix(clusters, | ||
cluster_cor_mat_per | cluster_cor_mat_per | ||
+ | </ | ||
+ | < | ||
##################### | ##################### | ||
# Questions: | # Questions: |
krackhardt_datasets.txt · Last modified: 2019/12/13 14:11 by hkimscil