Exact(16)
To make sure whether the optimum model best explained the data, goodness of fit test statistics using pseudo R 2, Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) were used, while measures of association were checked through likelihood ratio (Campbell-Smith et al. 2010).
Metrics for life stretch from infant mortality through likelihood of being murdered, raped or assaulted, to life expectancy.
All analyses were performed using SAS/STAT software, Version 9.1.3 of the SAS System for Microsoft Windows (Copyright ©2007 SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks of SAS Institute Inc., Cary, NC, USA), and significance of terms in the models was determined through Likelihood Ratio Statistics for Type III ANOVAs.
§P values derived through likelihood ratio tests for trend.
Model fitting was performed by first evaluating the random effects through likelihood ratio tests.
The statistical significance of the associations was assessed through likelihood ratio tests based on a P value < 0.05.
Similar(44)
The models are compared through likelihood-ratio tests (degree of freedom = total number of branches − 1).
The models are compared through likelihood-ratio tests (LRT) (degree of freedom = total number of branches − 1).
The fit of nested models was compared to the fit of less-restricted models through likelihood-ratio tests.
In order to explore the role of potential effect modifiers, statistical significance of appropriate interaction terms were evaluated through likelihood-ratio tests.
Estimation in population count models is, therefore, completed through likelihood-based approaches, requiring approximation of the integral over the random effects.
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