lopodSummary: Summary statistics for a LopodModel

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

Description

Summary statistics for a LopodModel

Usage

lopodSummary(LopodModel, params = NULL, probs = c(0.05, 0.5, 0.95))

Arguments

LopodModel	A LopodModel object
params	Parameters to be plotted. Default is NULL, which plots all global parameters
probs	Quantiles to be estimated

Value

Returns a dataframe which contain summaries for all chains merged for the Global Parameters of a LopodModel. Included in the summary are quantiles, means, standard deviations (sd), effective sample sizes (n_eff), Monte Carlo standard errors (se_mean) and Rhats.

Examples

 ## 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)
lopodSummary(mLopodRaster, params = c("psi","p","q"))

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)
lopodSummary(mLopodRaster)

## End(Not run)

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