estimateMapSpread: Estimates spatial spread model (first or latest occurence of...

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

Estimates spatial spread model (first or latest occurence of event)

Description

Estimates spatial spread model (first or latest occurence of event)

Usage

estimateMapSpread(
  data,
  Longitude,
  Latitude,
  DateOne,
  DateTwo,
  center = c("Europe", "Pacific"),
  burnin = 500,
  iter = 2000,
  nChains = 1,
  K = 50,
  MinMax = "Max",
  DateType = "Interval",
  dateUnc = "mid point",
  CoordType = "decimal degrees",
  smoothConst = 1,
  penalty = 1,
  splineType = 2,
  shinyApp = FALSE,
  outlier = FALSE,
  outlierValue = 4,
  outlierD = FALSE,
  outlierValueD = 4,
  restriction = c(-90, 90, -180, 180),
  correctionPac = FALSE,
  thinning = 2,
  spreadQ = 0.01,
  minValue = -Inf
)

Arguments

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"
burnin	integer: number of burn-in iterations for Bayesian model (default = 500)
iter	integer: number of iterations for Bayesian model (default = 2000)
nChains	integer: number of chains for Bayesian model (default = 1)
K	integer: number of basis functions for tprs (thin plate regression spline)
MinMax	character: estimate minimum or maximum of distribution. choices: "Max", "Min"
DateType	character: one of "Interval", "Mean + 1 SD uncertainty" and "Single Point"
dateUnc	character: one of "uniform", "normal", "point"
CoordType	character: type of longitude/latitude coordinates. One of "decimal degrees", "degrees minutes seconds" and "degrees decimal minutes"
smoothConst	numeric: adjust smoothing parameter for Bayesian model (optional)
penalty	numeric: 1 for constant extrapolation, 2 for linear extrapolation
splineType	numeric: 1 for classical tprs, 2 for spherical spline
shinyApp	boolean: If called inside shinyApp: Set to true
outlier	boolean: outlier removal TRUE/FALSE
outlierValue	numeric: if outlier removal is TRUE, threshold for removals in sd
outlierD	boolean: data outlier removal TRUE/FALSE
outlierValueD	numeric: if outlierD removal is TRUE, threshold for removals in sd
restriction	numeric vector: spatially restricts model data 4 entries for latitude (min/max) and longitude(min/max)
correctionPac	boolean: correction (data augmentation) for pacific centering
thinning	numeric: mcmc thinning for bayesian models
spreadQ	numeric: exceedance quantile as buffer
minValue	numeric: minValue restriction

Examples

## Not run: 
# load data
data <- readRDS(system.file("extData", "exampleData.Rds", package = "DSSM"))
# estimate model-map
map <- estimateMapSpread(data = data, Longitude = "longitude",
Latitude = "latitude", DateOne = "dateLower", DateTwo = "dateUpper", iter = 200)
# Plot the map
plotMap(model = map)

## End(Not run)

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