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Location-scale mixed models and goodness-of-fit assessment applied to insect ecology
R A Moral1, J Hinde2, E M M Ortega3
1Department of Mathematics and Statistics, Maynooth University, Maynooth, Ireland.
Journal of Applied Statistics
|June 16, 2022
Summary
This study introduces a goodness-of-fit tool for survival models, finding the exponentiated-Weibull model superior for analyzing insect predation time-until-event data, even with random effects.
Area of Science:
- Biostatistics
- Ecology
- Entomology
Background:
- Survival models are crucial for time-until-event data analysis.
- Extended models account for overdispersion, mixtures, and flexible response functions.
- Assessing model fit is essential for reliable conclusions.
Purpose of the Study:
- To demonstrate the utility of half-normal plots with simulated envelopes for survival model goodness-of-fit.
- To compare the fit of Weibull and exponentiated-Weibull location-scale models for insect predation data.
- To evaluate the impact of incorporating random effects.
Main Methods:
- Applied survival models (Weibull, exponentiated-Weibull location-scale) to time-until-event data from a biological control experiment.
- Utilized the `hnp` package in R for half-normal plots of deviance residuals.
- Performed variable selection and included random effects for correlated observations.
Main Results:
- The exponentiated-Weibull location-scale model provided a better fit to the insect predation data than the Weibull model.
- Half-normal plots with simulated envelopes effectively assessed goodness-of-fit.
- The inclusion of random effects was explored for correlated data.
Conclusions:
- The exponentiated-Weibull location-scale model, assessed using half-normal plots, is a valuable tool for analyzing time-until-event data in ecological studies.
- The methodology can be extended to models with random effects.
- Findings have implications for biological control experiments and statistical modeling.
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