Function reduces an object of class `ClusterAlertTable“ to the final set of clusters and locations. The idea of this function is to retain only the most significant, non-overlapping clusters from the cluster alert table. The surrogate for significance is 'alertGap', or log(observed/expected) minus the threshold that the spline assigns to the observed value. The logic in this function keeps two running tables, the table `dt_keep` of clusters to be kept, in descending order of `alertGap`, and `dt_clust`, the remaining rows of the cluster alert table, which are reduced each time a cluster is accepted into `dt_keep`. Each row of the cluster alert table represents a candidate cluster, with a column `target`, which is the cluster center, and a column `location`, the most distant location from the center. Each time a cluster is accepted into `dt_keep`, the remaining rows of `dt_clust` are dropped if either `target` or `location` is the center of the newly accepted cluster. in `dt_keep`
Value
an object of class `clusters`, which is simply a a list including a a data.frame of clusters and another frame of individual location counts
Examples
case_grid <- generate_case_grids(
example_count_data, example_count_data[, max(date)]
)
nci <- gen_nearby_case_info(
cg = case_grid,
distance_matrix = county_distance_matrix("OH")[["distance_matrix"]],
distance_limit = 25
)
obs_exp_grid <- generate_observed_expected(
nearby_counts = nci,
case_grid = case_grid
)
cla <- add_spline_threshold(oe_grid = obs_exp_grid)
compress_clusters(
cluster_alert_table = cla,
distance_matrix = county_distance_matrix("OH")[["distance_matrix"]]
)
#> $cluster_alert_table
#> target observed spl_thresh date test_totals location base_clust_sums
#> <char> <int> <num> <Date> <int> <char> <num>
#> 1: 39009 215 0.2769749 2025-01-30 9673 39009 560
#> 2: 39005 166 0.3304438 2025-01-30 9673 39005 489
#> 3: 39003 335 0.2113664 2025-01-30 9673 39003 1263
#> 4: 39015 80 0.4995393 2025-02-04 2858 39025 862
#> 5: 39109 399 0.1928263 2025-02-04 2858 39021 5866
#> 6: 39061 287 0.2297259 2025-02-02 5553 39061 2270
#> 7: 39081 37 0.8068677 2025-02-04 2858 39081 326
#> 8: 39141 160 0.3385027 2025-01-31 8267 39129 794
#> 9: 39017 280 0.2330866 2025-01-30 9673 39017 1383
#> 10: 39039 67 0.5758212 2025-02-01 6898 39039 335
#> distance_value count count_sum detect_date baseline_total expected
#> <num> <int> <int> <Date> <num> <num>
#> 1: 0.00000 59 215 2025-02-05 63803 84.90008
#> 2: 0.00000 44 166 2025-02-05 63803 74.13597
#> 3: 0.00000 67 335 2025-02-05 63803 191.48001
#> 4: 17.32111 26 59 2025-02-05 63803 38.61254
#> 5: 24.96767 11 25 2025-02-05 63803 262.76238
#> 6: 0.00000 79 287 2025-02-05 63803 197.56610
#> 7: 0.00000 20 37 2025-02-05 63803 14.60289
#> 8: 22.46182 10 59 2025-02-05 63803 102.87914
#> 9: 0.00000 46 280 2025-02-05 63803 209.67288
#> 10: 0.00000 14 67 2025-02-05 63803 36.21820
#> log_obs_exp min_dist alert_ratio alert_gap nr_locs
#> <num> <num> <num> <num> <int>
#> 1: 0.9291630 0.00000 3.354683 0.65218806 1
#> 2: 0.8060870 0.00000 2.439407 0.47564318 1
#> 3: 0.5593471 0.00000 2.646338 0.34798069 1
#> 4: 0.7284495 17.32111 1.458243 0.22891024 2
#> 5: 0.4177113 24.96767 2.166257 0.22488502 5
#> 6: 0.3734090 0.00000 1.625454 0.14368309 1
#> 7: 0.9296987 0.00000 1.152232 0.12283098 1
#> 8: 0.4416189 22.46182 1.304625 0.10311621 3
#> 9: 0.2892410 0.00000 1.240916 0.05615438 1
#> 10: 0.6151308 0.00000 1.068267 0.03930963 1
#>
#> $clust_loc_list
#> $clust_loc_list[[1]]
#> [1] "39009"
#>
#> $clust_loc_list[[2]]
#> [1] "39005"
#>
#> $clust_loc_list[[3]]
#> [1] "39003"
#>
#> $clust_loc_list[[4]]
#> [1] "39015" "39025"
#>
#> $clust_loc_list[[5]]
#> [1] "39021" "39109" "39037" "39113" "39149"
#>
#> $clust_loc_list[[6]]
#> [1] "39061"
#>
#> $clust_loc_list[[7]]
#> [1] "39081"
#>
#> $clust_loc_list[[8]]
#> [1] "39131" "39129" "39141"
#>
#> $clust_loc_list[[9]]
#> [1] "39017"
#>
#> $clust_loc_list[[10]]
#> [1] "39039"
#>
#>
#> attr(,"class")
#> [1] "list" "clusters"