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)
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