covparms: Get Covariance Parameters

In SSN: Spatial Modeling on Stream Networks

Get Covariance Parameters

Description

Displays the covariance parameter estimates for the autocovariance function(s) in the glmssn object.

Usage

covparms(object)

Arguments

object

An object of class glmssn-class created using the glmssn function.

Details

A glmssn-classobject allows up to three autocovariance functions, as well as random effects. The covparms function displays the sill and range for each model, as well as the nugget.

Value

Returns a data.frame containing the names of the autocovariance functions and random effects, the parameter names, and their corresponding estimates.

Author(s)

Jay Ver Hoef [e-mail]

See Also

glmssn

Examples

library(SSN)
#for examples, copy MiddleFork04.ssn directory to R's temporary directory
copyLSN2temp()
# NOT RUN
# Create a SpatialStreamNetork object that also contains prediction sites
#mf04p <- importSSN(paste0(tempdir(),'/MiddleFork04.ssn'), 
#  predpts = "pred1km", o.write = TRUE)
#use mf04p SpatialStreamNetwork object, already created
data(mf04p)
#for examples only, make sure mf04p has the correct path
#if you use importSSN(), path will be correct
mf04p <- updatePath(mf04p, paste0(tempdir(),'/MiddleFork04.ssn'))

## NOT RUN Distance Matrix has already been created
## createDistMat(mf04)

# The models take a little time to fit, so they are NOT RUN 
# Uncomment the code to run them
# Alternatively, you can load the fitted models first to look at results
data(modelFits)

## 3 component spatial model
#fitSp <- glmssn(Summer_mn ~ ELEV_DEM + netID,
#    ssn.object = mf04, EstMeth = "REML", family = "Gaussian",
#    CorModels = c("Exponential.tailup","Exponential.taildown",
#    "Exponential.Euclid"), addfunccol = "afvArea")
#for examples only, make sure fitSp has the correct path
#if you use importSSN(), path will be correct
fitSp$ssn.object <- updatePath(fitSp$ssn.object, 
	paste0(tempdir(),'/MiddleFork04.ssn'))

## Look at variance components in more detail
covparms(fitSp)

SSN documentation built on March 7, 2023, 5:30 p.m.