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Table 4 Comparison of model selection in terms of spatial precision

From: Spatial cluster detection using dynamic programming

Frequency range Coverage DP precision GR precision SR precision (DP-GR) 95% confidence interval limits p-value
Low < 16% 0.09365 0.11775 0.07022 - 0.02412 - 0.02408 < 0.00001
  16-34% 0.28551 0.33207 0.27251 - 0.04659 - 0.04654 < 0.00001
  ≥ 35% 0.57974 0.62428 0.60255 - 0.04462 - 0.04447 < 0.00001
Mid < 16% 0.31987 0.34414 0.11212 - 0.02430 - 0.02425 < 0.00001
  16-34% 0.67969 0.66288 0.31934 0.01679 0.01683 < 0.00001
  ≥ 35% 0.92269 0.86046 0.55602 0.06211 0.06235 < 0.00001
High < 16% 0.50417 0.53018 0.15127 - 0.02604 - 0.02599 < 0.00001
  16-34% 0.84815 0.78839 0.34174 0.05974 0.05978 < 0.00001
  ≥ 35% 0.91292 0.83541 0.54478 0.07740 0.07763 < 0.00001
Very high < 16% 0.66221 0.51208 0.14081 0.15010 0.15017 < 0.00001
  16-34% 0.84584 0.44728 0.26127 0.39854 0.39859 < 0.00001
  ≥ 35% 0.92306 0.66563 0.48019 0.25728 0.25758 < 0.00001
  1. Comparison of the DP, GR, and SR spatial precisions obtained from model selection. The table shows the mean precision for each algorithm, the lower and upper 95% confidence limits for the difference in precision (DP-GR) and the associated t-test p-values