Dealing with Separation in Logistic Regression Models

Rainey, Carlisle

doi:10.1093/pan/mpw014

Cited by 70 publications

(63 citation statements)

References 48 publications

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“…For maximum‐likelihood estimation methods, a finite maximum likelihood would not exist and standard calculation of standard errors would fail. This is less of an issue for models using a Bayesian framework, as using prior information switches the focus of parameter estimation to posterior distribution summaries (Rainey, ). LPR had convergence issues for these datasets with default priors, but the use of more informative priors led to performance similar to the other JSDMs (see Supporting Information Appendix ), highlighting the importance of careful consideration of priors.…”

Section: Discussionmentioning

confidence: 99%

A comparison of joint species distribution models for presence–absence data

et al. 2018

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Joint species distribution models (JSDMs) account for biotic interactions and missing environmental predictors in correlative species distribution models. Several different JSDMs have been proposed in the literature, but the use of different or conflicting nomenclature and statistical notation potentially obscures similarities and differences among them. Furthermore, new JSDM implementations have been illustrated with different case studies, preventing direct comparisons of computational and statistical performance. We aim to resolve these outstanding issues by (a) highlighting similarities among seven presence–absence JSDMs using a clearly defined, singular notation; and (b) evaluating the computational and statistical performance of each JSDM using six datasets that vary widely in numbers of sites, species, and environmental covariates considered. Our singular notation shows that many of the JSDMs are very similar, and in turn parameter estimates of different JSDMs are moderate to strongly, positively correlated. In contrast, the different JSDMs clearly differ in computational efficiency and memory limitations. Our framework will allow ecologists to make educated decisions about the JSDM that best suits their objective, and enable wider uptake of JSDM methods among the ecological community.

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Section: Discussionmentioning

confidence: 99%

A comparison of joint species distribution models for presence–absence data

et al. 2018

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“…In such a case, the maximum likelihood fitting method provides implausible estimates. To circumvent this problem we used Markov chain Monte Carlo (MCMC) simulations to fit the GLMs [23]. We also used the probabilistic programming language Stan [24] and the rethinking [19] package as interface to fit the GLMs.…”

Section: G Glm Creation and Selectionmentioning

confidence: 99%

Assessing Dynamic Balance Performance During Exergaming Based on Speed and Curvature of Body Movements

Aguilar

Gronde

Lamoth

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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Improving balance performance among the elderly is of utmost importance because of the increasing number of injuries and fatalities caused by fall incidences. Digital games controlled by body movements (exergames) have been proposed as a way to improve balance among older people. However, the assessment of balance performance in real-time during exergaming remains a challenging task. This assessment could be used to provide instantaneous feedback and automatically adjust the exergame difficulty. Such features could potentially increase the motivation of the player, thus augmenting the effectiveness of exergames. As clear differences in balance performance have been identified between older and younger people, distinguishing between older and younger adults can help identifying measures of balance performance. We used generalized linear models to investigate whether the assessment of balance performance based on movement speed can be improved by incorporating curvature of the movement trajectory into the analysis. Indeed, our results indicated that curvature improves the performance of the models. Five-fold cross validation indicated that our method is promising for the assessment of balance performance in real-time by showing more than 90% classification accuracy. Finally, this method could be valuable not only for exergaming, but also for real-time assessment of body movements in sports, rehabilitation, and medicine.

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“…This is also known as the 'separation' effect, where Frequentist computation (e.g. via maximum likelihood) may fail or 'provides implausible estimates' (Rainey, 2016). However, if the resulting 95% confidence interval had been calculated to fall in the same range of [−3, +1], then its interpretation is different, in fact the logical reverse (Sedimeier and Gigerenzer, 2001), and much less straightforward (Wasserstein and Lazar, 2016), and often confusing (Greenland et al, 2016).…”

Section: Under the Hood: Effect Sizes Uncertainty And Interpretationmentioning

confidence: 99%

Using Bayesian statistical modelling as a bridge between quantitative and qualitative analyses: illustrated via analysis of an online teaching tool

Low‐Choy

Riley

Alston-Knox

2017

Educational Media International

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Bayesian methods provide a more general approach to statistical analysis that mathematically includes Null Hypothesis Significance Testing (NHST) and classical statistical modelling as special cases. This expanded, Bayesian, approach provides several benefits, which we illustrate using a case study about decision-making by teachers. We focus on a relatively unexplored topic: the way in which a Bayesian approach provides a 'bridge' between qual/quant methods. We highlight five bridges, illustrated using the case study: (1) visualization of the conceptual framework, (2) generalization via randomization and alternatives, (3) stories for interpretation, (4) computation that is flexible, and (5) continual learning, through priors. This work illustrates these bridges using a case study on a digital tool that wove together: a behavioural study to investigate decision-making, with an inbuilt perceptual component to probe rationale for specific decisions, and an interview component. A mixed method was therefore a natural choice for integrating learnings across these data sources. This online tool inhabits the digital realm of education which enables 'virtual' assessment, and sits midway between theoretical, written pieces and the fully immersed practicums in the classroom. Adopting a similar tool for training in a virtual classroom could provide greater accessibility and privacy, and enhanced feedback through the in situ qual/quant analytics. Thus the digital learning sphere provides a context for raising awareness of the potential that Bayesian statistical paradigm offers researchers who wish to connect qual/quant methods.

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Dealing with Separation in Logistic Regression Models

Cited by 70 publications

References 48 publications

A comparison of joint species distribution models for presence–absence data

A comparison of joint species distribution models for presence–absence data

Assessing Dynamic Balance Performance During Exergaming Based on Speed and Curvature of Body Movements

Using Bayesian statistical modelling as a bridge between quantitative and qualitative analyses: illustrated via analysis of an online teaching tool

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