SSN (version 1.1.8)

BLUP: Compute the joint mean, variance and covariance of any random effects in a glmssn model conditional on the data

Description

Compute the joint mean, variance and covariance of any random effects in a glmssn model conditional on the data. This assumes each random effect has a Gaussian distribution with mean zero and covariance matrix sigma^2 * Identity. We just plug in the REML estimate of sigma^2 from the fitted glmssn model object.

Usage

BLUP(model, RE = NULL)

Arguments

model

An object of class glmssn-class

RE

Names of random effects (RE), defaults to all REs in the glmssn object, if any

Value

Details

Similar to BLUP in the regress package.

Examples

library(SSN)
# NOT RUN 
# mf04 <- importSSN(system.file("lsndata/MiddleFork04.ssn", 
#	        package = "SSN"), o.write = TRUE)
# use SpatialStreamNetwork object mf04 that was already created
data(mf04)
# for examples, copy MiddleFork04.ssn directory to R's temporary directory
copyLSN2temp()
#make sure mf04p has the correct path, will vary for each users installation
mf04 <- updatePath(mf04, 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)

## Random effect model using STREAMNAME as our random effect
#fitRE <- glmssn(Summer_mn ~ ELEV_DEM + netID,
#    ssn.object = mf04, EstMeth = "REML", family = "Gaussian",
#    CorModels = c("STREAMNAME"))
summary(fitRE)
## random effects details
fitREBLUP <- BLUP(fitRE)
str(fitREBLUP)
fitREBLUP$Mean

## spatial stream model with a random effect
#fitSpRE1 <- glmssn(Summer_mn ~ ELEV_DEM + netID,
#    ssn.object = mf04, EstMeth = "REML", family = "Gaussian",
#    CorModels = c("STREAMNAME","Exponential.tailup"),
#    addfunccol = "afvArea")
fitRE1BLUP <- BLUP(fitSpRE1)
str(fitRE1BLUP)
fitRE1BLUP$Mean