Description Usage Arguments Value Author(s) See Also Examples

Calculates estimated relationships between activity probability density (APD) of the focal and contingent(s) using Bayesian GLMMs with 'STAN'
using `brm`

, with the option to automatically select the statistical distribution that best fits the dataset (weibull,
frechet, gamma, lognormal, inverse gaussian) by `loo`

. The function automatically ensures that MCMC chains reach
convergence and that the specified minimum effective sample size from the posterior distribution is achieved.

Package: AnimalAPD Version: 1.0.0 Date: 2020-11-10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
APDREcorr(
focal,
cont1,
cont2 = NULL,
cont3 = NULL,
cont4 = NULL,
RE1,
RE2 = NULL,
weibullGLMM = TRUE,
frechetGLMM = TRUE,
gammaGLMM = TRUE,
lognormalGLMM = FALSE,
invgaussianGLMM = TRUE,
cores = 1,
iter = 5000,
minESS = 1000,
burnin = iter/2,
thin = 1,
adapt_delta = 0.95,
center = "pi",
adjust = 1,
Reloo = TRUE,
plothist = TRUE,
ploteffects = TRUE,
histcol = "cyan4",
effectcol = "cyan4",
linecol = "red"
)
``` |

`focal` |
Vector of observations in radians of one species/group/individual/etc. for which predictions on another will be made. |

`cont1` |
Vector of observations in radians, or output from generalized circular mixture model of activity curves from |

`cont2` |
Optional vector of observations in radians, or output from generalized circular mixture model of activity curves from |

`cont3` |
Optional vector of observations in radians, or output from generalized circular mixture model of activity curves from |

`cont4` |
Optional vector of observations in radians, or output from generalized circular mixture model of activity curves from |

`RE1` |
Vector identifying a random intercept for observations of the focal to control for hierarchical data (e.g. camera trap IDs) |

`RE2` |
Optional vector identifying levels of a second random effect, for data with additional hierarchical levels (e.g. study sites, sampling periods, data collection seasons); default is NULL |

`weibullGLMM` |
Specifies whether to run a weibull GLMM, using the brms package; default is TRUE for all and results from the best-fitting model are returned |

`frechetGLMM` |
Specifies whether to run a frechet GLMM, using the brms package; default is TRUE for all and results from the best-fitting model are returned |

`gammaGLMM` |
Specifies whether to run a Gamma GLMM, using the brms package; default is TRUE for all and results from the best-fitting model are returned |

`lognormalGLMM` |
Specifies whether to run a lognormal GLMM, using the brms package; default is TRUE for all and results from the best-fitting model are returned |

`invgaussianGLMM` |
Specifies whether to run a inverse.gaussian GLMM, using the brms package; default is TRUE for all and results from the best-fitting model are returned |

`cores` |
Number of cores to use when running MCMC chains in parallel; default=1 |

`iter` |
Number of MCMC iteractions per chain; burnin is iter/2; default=5000 |

`minESS` |
Desired minimum effective sample size; default=1000 |

`burnin` |
Number of MCMC iterations to be discarded as the burn-in; default=iter/2 |

`thin` |
Thinning rate for saving MCMC draws; default=1 |

`adapt_delta` |
Value to use for adapt_delta with brms; default=0.95; see also |

`center` |
Value to use as center of graph; default=pi |

`adjust` |
Smoothing of predicted line; recommended to use default value for observed values and higher value for estimations from circular models |

`Reloo` |
Whether to use reloo when running leave-one-out cross-validation of models (loo); see also |

`plothist` |
Whether to plot histograms of samples from the posterior distribution for the correlation parameters; default=TRUE |

`ploteffects` |
Whether to plot predicted effects; default=TRUE |

`histcol` |
Colour for histogram bars |

`effectcol` |
Colour for predicted effect plot 95% HDI |

`linecol` |
Colour for histogram lines for the 95% HDI and 0 |

Prints results of best-fitting model and posterior samples and/or predicted effects of parameter estimates if `plothist=TRUE`

and `ploteffects=TRUE`

, and returns object of class `APD`

with list of analysis results and information.

`data`

List of data used in analysis

`model`

Object of class `brmsfit`

containing results and information for best-fitting model.

`distribution`

Character vector of statistical distribution of best-fitting model

`allmodels`

List of output for all tested models; object of class `brmsfit`

Liz AD Campbell

1 2 3 4 | ```
data(wolfexample)
data(boarexample)
APDREcorr(focal=wolfexample$Radians,cont1=boarexample$Radians,
RE1=wolfexample$SamplingPeriod)
``` |

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