estimateMapKernel: Estimates spatial kernel density model

In Pandora-IsoMemo/iso-app: Pandora & IsoMemo spatiotemporal modeling

Estimates spatial kernel density model

Description

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",
  restriction = c(-90, 90, -180, 180),
  nSim = 10,
  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
restriction	numeric vector: spatially restricts model data 4 entries for latitude (min/max) and longitude(min/max)
nSim	numeric: number of bootstrap samples
kdeType	character: "1" for correlated bandwidth, "2" for diagonal bandwidth, "3" for diagonal, equal long/lat bandwidth

Examples

## Not run: 
#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"))


## End(Not run)

Pandora-IsoMemo/iso-app documentation built on July 4, 2024, 7:07 p.m.