bayesLopod (version 1.0.1)

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

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

# NOT RUN {
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)
# }