Description Usage Arguments Value Author(s) See Also Examples
Calculation of animal activity probability density controlling for nested data with random intercepts using Bayesian GLMMs with 'STAN' and brm
.
The function can automatically select the statistical distribution that is most appropriate for the dataset (weibull, frechet, gamma, lognormal, inverse gaussian)
using loo
and automatically ensures that MCMC chains converge and that a specified minimum effective sample size from the posterior distribution
is achieved. An APD activity curve plot is provided.
Package: AnimalAPD Version: 1.0.0 Date: 2020-11-08
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 29 30 | APDRE(
focal,
contingent,
RE1,
RE2 = NULL,
weibullGLMM = TRUE,
frechetGLMM = TRUE,
gammaGLMM = TRUE,
lognormalGLMM = FALSE,
invgaussianGLMM = TRUE,
cores = 1,
iter = 5000,
burnin = iter/2,
center = "pi",
Reloo = TRUE,
adapt_delta = 0.95,
adjust = 1,
minESS = 1000,
col = "deeppink3",
histcol = "deeppink",
linecol = "black",
xlimCV = NULL,
min = TRUE,
max = TRUE,
points = TRUE,
mean = TRUE,
HDI = TRUE,
rawmean = FALSE,
...
)
|
focal |
Vector of observations in radians of one species/group/individual/etc. for which predictions on another will be made |
contingent |
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; default=5000 |
burnin |
Number of MCMC iteractions discarded as burnin; default=iter/2 |
center |
Value to use as center of graph; default=pi |
Reloo |
Whether to use reloo when running leave-one-out cross-validation of models (loo) |
adapt_delta |
Value to use for adapt_delta with brms; default=0.95 |
adjust |
Smoothing of predicted line; recommended to use default value for observed values and higher value for estimations from circular models |
minESS |
Desired minimum effective sample size; default=1000 |
col |
Specifies colour of points for the focal in the graph |
histcol |
Specifies colour of the histogram plot of the posterior distribution |
linecol |
Specifies colour of HDI line in histogram plot of the posterior distribution |
xlimCV |
A vector of two values indicating the x axis limits for the histCV graph |
min |
Whether to include minimum APD on the graph; default=TRUE; default=TRUE |
max |
Whether to include maximum APD on the graph; default=TRUE; default=TRUE |
points |
Whether to include datapoints for observations of the focal on the graph; default=TRUE |
mean |
Whether to include the estimated mean APD from the GLMM on the graph; default=TRUE |
HDI |
Whether to include the estimated 95% highest density interval of mean APD from the GLMM on the graph; default=TRUE |
rawmean |
Whether to include the raw mean, not correcting for random effects, on the graph; default=FALSE |
... |
Additional parameters |
Prints graph of activity curve and APD estimates from best-fitting GLMM and prints summary of analysis. Returns object of class APD
is returned, containing a list of analysis results and details:
data
List of data used in analysis
output
Matrix with summary output from selected model
distribution
Name of distribution of selected model
model
An object of class brmsfit
containing output from the selected model, including the posterior samples and other information. See brm
CVposterior
Numeric vector of posterior samples for the calculated family-specific population coefficient of variation (CV)
allmodels
List of objects of class brmsfit
containing output from all models from the analysis.
rawvalues
Numeric vector of the raw, uncorrected APD values
rawsummary
List of summary stats of raw APD values
Liz AD Campbell
1 2 3 4 5 6 | data(wolfexample)
data(boarexample)
APDRE(focal=wolfexample$Radians, contingent=boarexample$Radians,
RE1=wolfexample$SamplingPeriod, weibullGLMM=TRUE, frechetGLMM=FALSE,
gammaGLMM=FALSE, lognormalGLMM=FALSE, invgaussianGLMM=FALSE,
min=TRUE, max=TRUE, points=TRUE, mean=TRUE, HDI=TRUE, rawmean=FALSE)
|
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