This function extracts the binary confusion matrix from 'influenceSSN' objects. The matrix values are based on a leave-one-out cross-validation of the observed dataset.
getConfusionMat(x, threshold = 0.5)
an object of class influenceSSN-class
a numeric value used to classify binary spatial model predictions into 1's and 0's. The default is 0.5.
getConfusionMat returns a 2x2 matrix containing
information about the classification accuracy of the binary spatial
model, based on the observations and the leave-one-out
Erin E. Peterson support@SpatialStreamNetworks.com
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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 #mf04 <- importSSN(paste0(tempdir(),'/MiddleFork04.ssn', o.write = TRUE)) #use mf04 SpatialStreamNetwork object, already created data(mf04) #for examples only, make sure mf04p has the correct path #if you use importSSN(), path will be correct mf04 <- updatePath(mf04, paste0(tempdir(),'/MiddleFork04.ssn')) # get some model fits stored as data objects data(modelFits) ## NOT RUN ## Fit a model to binary data ## binSp <- glmssn(MaxOver20 ~ ELEV_DEM + SLOPE, mf04p, ## CorModels = c("Mariah.tailup", "Spherical.taildown"), ## family = "binomial", addfunccol = "afvArea") #for examples only, make sure binSp has the correct path #if you use importSSN(), path will be correct binSp$ssn.object <- updatePath(binSp$ssn.object, paste0(tempdir(),'/MiddleFork04.ssn')) summary(binSp) ## Generate the leave-one-out cross-validation prediction residuals ## for the observed sites. binResids <- residuals(binSp, cross.validation = TRUE) ## Generate the confusion matrix for the binary spatial ## model, based on the observations and leave-one-out ## cross-validation predictions getConfusionMat(binResids, threshold = 0.5)
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