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Table 4 Coefficients of the models of the pruning threshold τ across variables

From: Detecting and diagnosing hotspots for the enhanced management of hospital emergency departments in Queensland, Australia

  Age Sex Triage category Departure status Facility Disease group Disease subgroup
(Intercept) 3.7598 3.6926 3.9562 3.9712 3.3100 4.1558 3.5891
μ 0.0000 -0.0022 -0.0012 -0.0007 0.0017 -0.0012 -0.0015
depth -0.1481 -0.1772 -0.2327 -0.2209 -0.0675 -0.2605 -0.1022
1/ μ 0.6342 1.2781 0.6233 0.5121 0.8856 0.9699 0.0327
depth2 0.0060 0.0076 0.0121 0.0108 0.0015 0.0121 0.0038
1/depth -0.9182 -0.8120 -1.0811 -1.0864 -0.4693 -1.2520 -1.3850
μ*depth -0.0006 -0.0001 -0.0003 -0.0003 -0.0008 -0.0002 -0.0006
μ*(1/depth) 0.0004 0.0021 0.0014 0.0010 -0.0009 0.0013 0.0043
  1. Given here are the results of the quantile regression used to choose a threshold for each variable such that each variable is equally likely to signal; signalling is independent of node expected value μ and depth in the tree; and the overall false alarm rate is approximately 3 times per year.