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Table 2 Linear regression models for time to proportion-resistant equilibrium of the three behavior modes

From: Expanding behavior pattern sensitivity analysis with model selection and survival analysis

Behavior Mode Behavior Pattern Measure Most Parsimonious Model Full Model
Standardized Input Parameters
Coefficient (Standard Error)
Fit Statistics Fit Statistics
p r start r λ in λ out δ MIC s H i V LI η LI γ LI AIC BIC Adj R2 AIC BIC Adj R2
Increasing Equilibrium time 1868 (347) − 883 (313)   − 1085 (352)    − 1526 (352) − 884 (321)   − 1141 (292) 427 437 0.733 436* 464* −0.706*
Decreasing Equilibrium Time p r − 1527 (514) 1233 (207) − 3488 (396)   − 750 (175) − 1107 (180)   − 664 (179)    2141 2172 0.561 2149 2226 0.583
p r 2 805 (408)
p r 3 552 (331)
p r 4 − 410 (156)
Peaked Equilibrium Time 810 (175) − 667 (174) − 1807 (279) 660 (139) − 781 (123) − 1393 (154)   − 842 (145) − 628 (146)   3812 3846 0.423 3824 3918 0.436
  1. The example mathematical model was for the proportion of tetracycline-resistant enteric Escherichia coli in a beef steer during and after administration of oral chlortetracycline. A separate linear regression model was built for each behavior pattern measure of each behavior mode. The behavior pattern measure was the dependent variable in the linear regression models. Equilibrium was reached by 36% of increasing behavior simulations, 38% of decreasing behavior simulations and 24% of peaked behavior simulations after chlortetracycline administration ended and before the end of the simulation period. Simulations that did not reach equilibrium were excluded from these models. Coefficients and standard errors are listed for the standardized parameters that were included in each most parsimonious linear regression model. Full model refers to a linear regression model including all the parameters listed in Table 6 as independent variables. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted R2 are given for the most parsimonious and the full model. *Excludes log10(βir), log10(βsr), γs and MICi to prevent overfitting