getPreds: Extract Predictions with associated standard errors.

In SSN: Spatial Modeling on Stream Networks

Extract Predictions with associated standard errors.

Description

This function extracts predictions and standard errors from objects of class 'glmssn.predict' or 'influenceSSN'. Predictions are extracted for unobserved locations, while cross-validation predictions are extracted for observed locations.

Usage

getPreds(x, pred.type = "cv")

Arguments

x	an object of class predict.glmssn or influenceSSN-class
pred.type	prediction type, either "pred" or "cv". The "pred" option indicates that a 'glmssn.predict' object is being accessed and a text file containing predictions and standard errors for the predictions is exported. When the "cv" option is used, objects of class influenceSSN are accessed and cross-validation predictions and standard errors are exported.

Value

getPreds returns a matrix containing the point identifier (pid), the predictions, and the standard errors for the predictions.

Author(s)

Erin E. Peterson [e-mail]

See Also

predict, influenceSSN-class

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'))

# get some model fits stored as data objects
data(modelFits)
#NOT RUN use this one
#fitSp <- glmssn(Summer_mn ~ ELEV_DEM + netID,
#    ssn.object = mf04p, EstMeth = "REML", family = "Gaussian",
#    CorModels = c("Exponential.tailup","Exponential.taildown",
#    "Exponential.Euclid"), addfunccol = "afvArea")
#Update the path in fitSp, will vary for each users installation
fitSp$ssn.object <- updatePath(fitSp$ssn.object, 
  paste0(tempdir(),'/MiddleFork04.ssn'))

# Extract predictions and standard errors for the prediction sites
# make sure the distance matrix is there
createDistMat(mf04p, predpts = "pred1km", o.write = TRUE)
#create predictions
fitSpPred <- predict(fitSp, predpointsID = "pred1km")
class(fitSpPred)
fitSpgetPreds <- getPreds(fitSpPred, pred.type = "pred")
head(fitSpgetPreds)

# Extract cross-validation predictions for the observed sites in two ways:
fitSpRes <- residuals(fitSp)
class(fitSpRes)

# Extract from the influenceSSN class object
fitSpResGetCV <- getPreds(fitSpRes, pred.type = "cv")
head(fitSpResGetCV)

# Extract from the glmssn.predict class object
fitSpResGetCV2 <- getPreds(fitSpPred, pred.type = "cv")
# These values are identical
identical(fitSpResGetCV,fitSpResGetCV) ## TRUE

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