Estimates spatial kernel density model

Usage

estimateMapKernel(
  data,
  Longitude,
  Latitude,
  center = c("Europe", "Pacific"),
  independent = NULL,
  CoordType = "decimal degrees",
  Weighting = NULL,
  clusterMethod = NULL,
  nClust = 5,
  nClustRange = c(2, 10),
  kMeansAlgo = "Hartigan-Wong",
  trimRatio = 0.05,
  restr.fact = 12,
  restriction = c(-90, 90, -180, 180),
  nSim = 10,
  smoothness = 1,
  kdeType = "1"
)

Arguments

data: data.frame: data
Longitude: character: name of longitude variable
Latitude: character: name of latitude variable
center: (character) center to shift data to, either "Europe" or "Pacific"
independent: character: name of presence/absence variable (optional)
CoordType: character: type of longitude/latitude coordinates. One of "decimal degrees", "degrees minutes seconds" and "degrees decimal minutes"
Weighting: character: name of weighting variable
clusterMethod: character: cluster method
nClust: numeric: how many clusters
nClustRange: numeric: range of potential mclust cluster
kMeansAlgo: character: kmeans algorithm as in stats:kmeans
trimRatio: numeric: proportion of observations to be trimmed by tclust
restr.fact: numeric: clustering restriction factor
restriction: numeric vector: spatially restricts model data 4 entries for latitude (min/max) and longitude(min/max)
nSim: numeric: number of bootstrap samples
smoothness: numeric: smoothness adjustment
kdeType: character: "1" for correlated bandwidth, "2" for diagonal bandwidth, "3" for diagonal, equal long/lat bandwidth

Examples

if (FALSE) {
#load data
data <- readRDS(system.file("extData", "exampleData.Rds", package = "DSSM"))
# estimate model-map
map <- estimateMap(data = data, independent = "d13C", Longitude = "longitude",
Latitude = "latitude", Site = "site")
# Plot the map
plotMap(model = map)

# Alternative: use app
shiny::runApp(paste0(system.file(package = "DSSM"),"/app"))

}