estcovparm: Estimate Covariance Parameters
In highamm/FPBK-with-Detection: Finite Population Block Kriging with Detection

Description Usage Arguments Details Value

Used to estimate spatial covariance parameters for a few different spatial models, including the Exponential, Gaussian, and Spherical. Estimated parameters can then be used in predict to predict unobserved values.

1
2
3

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

`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, `covstruct` is
`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. Exponential but other options include the Spherical and the Gaussian.
`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.
`pivec`	is a vector of estimated detection probabilities on each of the sites
`Vnn`	is the covariance matrix for the estimated detection probabilities