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Table 4 Linear regression models for proportion-resistant inflection and maximum times of decreasing and peaked behaviors, respectively

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 p i start r start i λ out MIC s η LI AIC BIC Adj. R2 AIC BIC Adj. R2
Decreasing Inflection Time4 −3.2e14 (4.5e13)   3.4e14 (4.6e13)    2.2e14 (4e13)   14,373 14,389 0.301 14,367 14,460 0.387
Peaked Max Time 310.43 (31.71) −226.84 (30.33) − 282.98 (31.6) 115.5 (30.3) − 102.45 (30.31) 270.5 (31.02) 61.23 (30.63) 10,014 10,054 0.348 9853 9982 0.512
  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. Inflection points occurred during chlortetracycline administration in 65% of decreasing behavior simulations. Simulations that did not have an inflection point were excluded from the inflection level model. A maximum proportion resistant during chlortetracycline administration could be calculated for all peaked behavior simulations. Coefficients and standard errors are listed for the standardized parameters that were included in each most parsimonious linear regression model. For the inflection time model, the dependent variable was inflection time raised to the fourth power. 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