cylcop: An R Package for Circular-Linear Copulae with Angular Symmetry

Fh, Hodel; Fieberg,

doi:10.1101/2021.07.14.452253

Cited by 13 publications

(39 citation statements)

References 56 publications

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“…García-Portugués et al, 2013). We have implemented this copula using a von Mises density for g (ω) (see cyl_vonmises in our cylcop package (Hodel and Fieberg, 2021)). However, there is no obvious way to obtain symmetric joint densities using this approach, which makes it less useful for movement data.…”

Section: Constructing Circular-linear Copulaementioning

confidence: 99%

“…For the sake of brevity, we will refer to copulae having the property in equation 9 as "symmetric in u", or just as "symmetric", even though there are many other possible symmetries for copulae (see Nelsen 2006 andHodel andFieberg 2021). Finally, note that any copula that is "symmetric" will also be "periodic".…”

Section: Circular-linear Copulaementioning

confidence: 99%

“…In some special cases, orthogonal reflections can also be seen as rotations by a multiple of 0.5π (see Hodel and Fieberg 2021). It is therefore common (see e.g.…”

Section: Linear Combinations Of Reflected Copulaementioning

confidence: 99%

“…The CDF can be derived easily by integrating the PDF and taking care of the boundary conditions of a copula (see Hodel and Fieberg 2021). Taking the arithmetic mean between any linear-linear copula C and C 0.5π produces a circular-linear copula…”

Section: Linear Combinations Of Reflected Copulaementioning

confidence: 99%

“…Specifically, we introduce several new circular-linear copulae with properties appropriate for modeling movement in discrete time together with corresponding methods for estimating parameters and simulating data. Functions for implementing these methods are available in our open-source R-package cylcop (R Core Team, 2019;Hodel and Fieberg, 2021), which can be seen as an extension of the copula package (Hofert et al, 2020;Jun Yan, 2007;Ivan Kojadinovic and Jun Yan, 2010;Marius Hofert and Martin Mächler, 2011;Hofert et al, 2018) and is available on GitHub at floo66/cylcop. Finally, we have also developed an app which allows the reader to interactively visualize all copulae introduced in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Circular-Linear Copulae for Animal Movement Data

Hodel

Fieberg²

2021

Preprint

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1. Animal movement is often modeled in discrete time, formulated in terms of steps taken between successive locations at regular time intervals. Steps are characterized by the distance between successive locations (step-lengths) and changes in direction (turn angles). Animals commonly exhibit a mix of directed movements with large step lengths and turn angles near 0 when traveling between habitat patches and more wandering movements with small step lengths and uniform turn angles when foraging. Thus, step-lengths and turn angles will typically be cross-correlated. 2. Most models of animal movement assume that step-lengths and turn angles are independent, likely due to a lack of available alternatives. Here, we show how the method of copulae can be used to fit multivariate distributions that allow for correlated step lengths and turn angles. 3. We describe several newly developed copulae appropriate for modeling animal movement data and fit these distributions to data collected on fishers (Pekania pennanti). The copulae are able to capture the inherent correlation in the data and provide a better fit than a model that assumes independence. Further, we demonstrate via simulation that this correlation can impact movement patterns (e.g. rates of dispersion overtime). 4. We see many opportunities to extend this framework (e.g. to consider autocorrelation in step attributes) and to integrate it into existing frameworks for modeling animal movement and habitat selection. For example, copula could be used to more accurately sample available locations when conducting habitat-selection analyses.

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Section: Constructing Circular-linear Copulaementioning

confidence: 99%

Section: Circular-linear Copulaementioning

confidence: 99%

“…In some special cases, orthogonal reflections can also be seen as rotations by a multiple of 0.5π (see Hodel and Fieberg 2021). It is therefore common (see e.g.…”

Section: Linear Combinations Of Reflected Copulaementioning

confidence: 99%

Section: Linear Combinations Of Reflected Copulaementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Circular-Linear Copulae for Animal Movement Data

Hodel

Fieberg²

2021

Preprint

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Precipitation extremes projected to increase and to occur in different times of the year

Treppiedi,

Villarini,

Bender

et al. 2024

Environ. Res. Lett.

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There is high confidence that precipitation extremes are projected to become more frequent and severe and, to a lesser extent, that their seasonality may change. However, these precipitation characteristics are dealt with separately, without examining whether magnitude and seasonality are jointly projected to change. Here we assess how the seasonality and magnitude of precipitation extremes are jointly projected to change for different climate scenarios. We perform analyses at the global scale using nine global climate models and four different emission scenarios. We identify large areas of the globe where the magnitude of the extremes is expected to increase as the emissions increase; at the same time, large changes in the seasonality of these extremes are projected to impact regions mainly located in the tropical and sub-tropical areas. These changes could impact our response and mitigation efforts and our resilience against such phenomena in response to climate change.

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Evaluating goodness-of-fit of animal movement models using lineups

Fieberg,

Freeman,

Signer

2023

Preprint

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Models of animal movement are frequently fit to animal location data to understand how animals respond to and interact with local environmental features. Several open-source software packages are available for analyzing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness-of-fit. We describe how a simple graphical technique, thelineup protocol, can be used to evaluate goodness-of-fit of integrated step-selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models, and demonstrate the approach using both methods applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate goodness-of-fit using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g., to visualize variability in model behavior across stochastic simulations or identify areas where the model could be improved). We provide coded examples and two vignettes to demonstrate the flexibility of the approach and encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness-of-fit.

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cylcop: An R Package for Circular-Linear Copulae with Angular Symmetry

Cited by 13 publications

References 56 publications

Circular-Linear Copulae for Animal Movement Data

Circular-Linear Copulae for Animal Movement Data

Precipitation extremes projected to increase and to occur in different times of the year

Evaluating goodness-of-fit of animal movement models using lineups

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