GCMMpdens

Object of class <code>GCMM</code> with output from <code><a rd-options="" href="/link/GCMM?package=activityGCMM&version=1.1.1" data-mini-rdoc="activityGCMM::GCMM">GCMM</a></code> function

model

Time point for which to predict activity probability density

timepoint

Logical argument for whether to calculate 95% HDI; default=TRUE

Number of posterior samples from which to build HDI; default=1000

sample

Scale of the data, either "2pi" for 0,2pi or "pi" for -pi,pi; default="2pi"

scale

Calculates predicted activity probability density from GCMM model at a specific time point

Bayesian parametric generalized circular mixed effect mixture models (GCMMs) for estimating animal activity patterns from camera trap data and other nested data structures using 'JAGS', including automatic Bayesian k-cluster selection and random circular intercepts for nested data. The GCMM function automatically selects the number of components for the mixture model (supporting up to 4 mixture components) based on a Bayesian linear finite normal mixture model and fits a Bayesian parametric circular mixed effect  mixture model with one or two random effects as random circular intercepts with a a von Mises or wrapped Cauchy distribution. Provides graphs of the combined mixture model or separate mixture components. Functionality is provided to allow quantitative  comparisons between model parameters. See Campbell et al. (in press) It's time to expand our analyses of animal activity;  Campbell et al. (in press) Temporal and microspatial niche partitioning; Campbell et al. (in press) A novel approach to comparing animal activity patterns. News, updates, and tutorials will be available on www.atlasgoldenwolf.org/stats and www.github.com/LizADCampbell .

Liz AD Campbell

activityGCMM

Circular Mixed Effect Mixture Models of Animal Activity Patterns

GCMMpdens function

Object of class <code>GCMM</code> with output from <code><a rd-options='' href='GCMM'>GCMM</a></code> function

GCMMpdens: Predict activity probability density at a time point

Description

Usage

Arguments

Value