View source: R/01-estimateMap.R
estimateMap3DKernel | R Documentation |
Estimates spatio-temporal kernel density model
estimateMap3DKernel(
data,
Longitude,
Latitude,
DateOne,
DateTwo,
center = c("Europe", "Pacific"),
independent = NULL,
DateType = "Interval",
CoordType = "decimal degrees",
Weighting = NULL,
clusterMethod = NULL,
nClust = 5,
nClustRange = c(2, 10),
kMeansAlgo = "Hartigan-Wong",
trimRatio = 0.05,
restr.fact = 12,
clusterTimeRange = c(0, 1000),
modelUnc = FALSE,
dateUnc = "point",
restriction = c(-90, 90, -180, 180),
nSim = 10,
smoothness = 1,
kdeType = "1"
)
data |
data.frame: data |
Longitude |
character: name of longitude variable |
Latitude |
character: name of latitude variable |
DateOne |
character: name of date variable 1 (lower interval point / mean / single point) |
DateTwo |
character: name of date variable 2 (upper interval point / sd / ) |
center |
(character) center to shift data to, either "Europe" or "Pacific" |
independent |
character: name of presence/absence variable (optional) |
DateType |
character: one of "Interval", "Mean + 1 SD uncertainty" and "Single Point" |
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 |
clusterTimeRange |
numeric vector: time range of cluster |
modelUnc |
boolean: Include dating uncertainty |
dateUnc |
character: one of "uniform", "normal", "point" |
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 |
## Not run:
# load data
data <- readRDS(system.file("extData", "exampleData.Rds", package = "DSSM"))
# estimate model-map
map <- estimateMap3D(data = data, independent = "d13C", Longitude = "longitude",
Latitude = "latitude", DateOne = "dateLower", DateTwo = "dateUpper", Site = "site")
# Plot the map
plotMap3D(model = map, time = median(data$dateLower, na.rm = TRUE))
## End(Not run)
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