fit_geoadj: Fit a geostatistical model to geomasked data

In claudiofronterre/geomask: Geostatistical modeling of geomasked data

Description

This function fit a geostatistical model using composite likelihood to spatial data that have positional error due to geoamsking.

Usage

fit_geoadj(data, locations, displacement = "gaussian", delta, kappa,
  n_sequence = 10, approx = F, thresh = 5e-06, ini, method = "BFGS")

Arguments

data	A numeric vector of spatial data.
locations	A two column matrix containing the locations (coordinates x and y).
displacement	The type of geomasking to be applied: either "gaussian" or "uniform".
delta	A number that specify the standard deviation of the positional error in the case of Gaussian geomasking or the maximum displacement distance in the case of Uniform geomasking.
kappa	Numerical value for the additional smoothness parameter of the matern correlation function.
n_sequence	A numeric value. It will define the lenght of the halton sequence for the quasi monte carlo integration. A longer sequence requires more computational time but provides more accurate results. Defaul to 10.
approx	If TRUE (defautl is FALSE) it will use an approximation to calculate the composite likelihood. If set to TRUE a threshold value in the argument thresh needs to be provided.
thresh	If approx is TRUE this defines the level of the approximation. By default is 0.000005 and garuantess a good comprise between speed and accuracy. Bigger values will make the computation faster but less accurate.
ini	Initial values for the parameters to be passed to the optimisation algorithm.
method	The optimisation method to be used. Default is "BFGS".

Value

A list containing the set of estimated parameters. The likelihood evaluated at the estimated parameters and a code to asses convergence of the algorithm.

claudiofronterre/geomask documentation built on Sept. 4, 2019, 2:13 p.m.