estcovparm: Estimate Covariance Parameters

In highamm/sptotal: Predicting Totals and Weighted Sums from Spatial Data

Estimate Covariance Parameters

Description

Used to estimate spatial covariance parameters for a few different spatial models. Estimated parameters can then be used in predict.slmfit() to predict values at unobserved locations.

Usage

estcovparm(
  response,
  designmatrix,
  xcoordsvec,
  ycoordsvec,
  CorModel = "Exponential",
  estmethod = "REML",
  covestimates = c(NA, NA, NA)
)

Arguments

response	a vector of a response variable, possibly with missing values.
designmatrix	is the matrix of covariates used to regress the response on.
xcoordsvec	is a vector of x coordinates
ycoordsvec	is a vector of y coordinates
CorModel	is the covariance structure. By default, CorModel is "Exponential" but other options are "Spherical" and "Gaussian".
estmethod	is either the default "REML" for restricted maximum likelihood to estimate the covariance parameters and regression coefficients or "ML" to estimate the covariance parameters and regression coefficients.
covestimates	is an optional vector of covariance parameter estimates (nugget, partial sill, range). If these are given and estmethod = "None", the the provided vector are treated as the estimators to create the covariance structure.

Details

The function is a helper function used internally in predict.slmfit().

Value

a list with

• parms.est, a vector of estimated covariance parameters

• Sigma, the fitted covariance matrix for all of the sites

• qrV, the qr decomposition

• b.hat, the vector of estimated fixed effect coefficients

• covbi, the inverse of the covariance matrix for the fixed effects

• covb, the covariance matrix for the fixed effects

• min2loglik, minus two times the log-likelihood

highamm/sptotal documentation built on Aug. 12, 2024, 2:04 a.m.