Estimates spatio-temporal average model with (optional) random effects (GAMM /Generalized Additive Mixed Model)

Note regarding IndependentType = "categorical": This follows a one vs. all approach using logistic regression, which in the Bayesian case is performed using a Polya-Gamma latent variable during Gibbs-sampling (https://arxiv.org/abs/1205.0310).

Usage

estimateMap3D(
  data,
  independent,
  Longitude,
  Latitude,
  DateOne,
  DateTwo,
  center = c("Europe", "Pacific"),
  IndependentType = "numeric",
  Site = "",
  DateType = "Interval",
  dateUnc = "uniform",
  independentUncertainty = "",
  CoordType = "decimal degrees",
  burnin = 500,
  iter = 2000,
  nChains = 1,
  splineType = 1,
  K = 25,
  KT = 10,
  Bayes = FALSE,
  penalty = 1,
  smoothConst = 1,
  outlier = FALSE,
  outlierValue = 4,
  outlierD = FALSE,
  outlierValueD = 4,
  restriction = c(-90, 90, -180, 180),
  sdVar = FALSE,
  correctionPac = FALSE,
  thinning = 2
)

Arguments

data: data.frame: data
independent: character: name of independent variable
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"
IndependentType: character: type ("numeric" or "categorical") of independent variable
Site: character: name of site variable (optional)
DateType: character: one of "Interval", "Mean + 1 SD uncertainty" and "Single Point"
dateUnc: character: one of "uniform", "normal", "point"
independentUncertainty: character: uncertainty of independent variable in sd (optional)
CoordType: character: type of longitude/latitude coordinates. One of "decimal degrees", "degrees minutes seconds" and "degrees decimal minutes"
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)
splineType: numeric: 1 for classical tprs, 2 for spherical spline
K: integer: number of basis functions for sos (spline on a sphere)
KT: integer: number of basis functions for tprs (thin plate regression spline)
Bayes: boolean: Bayesian model TRUE/FALSE?
penalty: numeric: 1 for constant extrapolation, 2 for linear extrapolation
smoothConst: numeric: adjust smoothing parameter(>0) for Bayesian model (optional)
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)
sdVar: boolean: variable standard deviation
correctionPac: boolean: correction (data augmentation) for pacific centering
thinning: numeric: mcmc thinning for bayesian models

Examples

if (FALSE) {
# 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))
}