modelLopod: Run a Bayes inference of Landscape Occupancy from...

In bayesLopod: Bayesian Inference of Landscape Occupancy from Presence-Only Data

Description

Run a Bayes inference of Landscape Occupancy from Presence-Only Data on Stan

Usage

modelLopod(LopodData, varP = F, q = NULL, pmin = 0, CAR = F,
  nChains = 4, warmup = 2000, sampling = 1000, nCores = 4)

Arguments

LopodData	Object with the data to be used in the Model
varP	Boolean. If TRUE, detectability will vary across cells. If FALSE a global value for detectability will be estimated.
q	Number between 0 and 1 or NULL. Rate of false detections. If NULL the values will be estimated by the model.
pmin	Number between 0 and 1. Minimum value for detectability in a unit in which the species occurs.
CAR	Boolean. If TRUE, (and if a adjacency matrix is included in the LopodData object) a conditional auto-regression analysis will be performed for occupancy across units.
nChains	Number of Markov chains used by the Stan model.
warmup	Number of iterations for each chain to be discarded as warm-up.
sampling	Number of iterations for each chain to be sampled (after warm-up).
nCores	Number of cores to use when executing the chains in parallel.

Value

A LopodModel object.

Examples

data("simSpRecords", package = "bayesLopod")
data("simSpSamplingEffort", package = "bayesLopod")
simSpRasters = xyToRaster(xyRecords = simSpRecords,xySamplingEffort = simSpSamplingEffort,
basemap = NULL, nrows = 10, extentExpansion = 0)
ld_Raster = rasterLopodData(rasterN = simSpRasters[["samplingEffort"]],
rasterY = simSpRasters[["spDetections"]], Adjacency = FALSE )
mLopodRaster = modelLopod(LopodData = ld_Raster, varP = TRUE, q = NULL,
pmin = 0, CAR = FALSE, nChains = 1,warmup = 10,sampling = 10,nCores = 1)

data("Andropogon_shape", package = "bayesLopod")
ld_Shape = shapeLopodData(Shapefile = Andropogon_shape, fieldN = "sampEffort",
fieldY = "detections",  Adjacency = FALSE, keepFields = FALSE)
mLopodShape = modelLopod(LopodData = ld_Shape, varP = FALSE, q = NULL,
pmin = 0, CAR = FALSE, nChains = 1,warmup = 5,sampling = 5,nCores = 1)

## Not run: 
data("simSpRecords", package = "bayesLopod")
data("simSpSamplingEffort", package = "bayesLopod")
simSpRasters = xyToRaster(xyRecords = simSpRecords,xySamplingEffort = simSpSamplingEffort,
basemap = NULL, nrows = 50, extentExpansion = 0)
ld_Raster_adMatrix = rasterLopodData(rasterN = simSpRasters[["samplingEffort"]],
rasterY = simSpRasters[["spDetections"]], Adjacency = TRUE )
mLopodRaster = modelLopod(LopodData = ld_Raster_adMatrix, varP = TRUE, q = NULL,
pmin = 0.1, CAR = FALSE, nChains = 4,warmup = 500,sampling = 100,nCores = 4)

data("Andropogon_shape", package = "bayesLopod")
ld_Shape = shapeLopodData(Shapefile = Andropogon_shape, fieldN = "sampEffort",
fieldY = "detections",  Adjacency = TRUE, keepFields = FALSE)
mLopodShape = modelLopod(LopodData = ld_Shape, varP = TRUE, q = NULL,
pmin = 0, CAR = TRUE, nChains = 4,warmup = 500,sampling = 100,nCores =4)

## End(Not run)

bayesLopod documentation built on Jan. 4, 2018, 5:08 p.m.