Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/CrossValidationStatsSSN.r
CrossValidationStatsSSN
operates on glmssn objects and uses the
CrossValidationSSN
function to create a data.frame of crossvalidation
predictions and standard errors. Then it computes summary statistics such
as bias and confidence interval coverage based on crossvalidation.
1  CrossValidationStatsSSN(object)

object 
an object of class 'glmssn' 
This function uses the
CrossValidationSSN
function to create a data.frame of crossvalidation
predictions and standard errors. Then it computes summary statistics on bias,
root meansquared prediction errors (RMSPE), and confidence interval coverage
based on crossvalidation. Output is a data.frame with with a single entry
for the columns as describe below. In the descriptions, obs is an observed
data value, pred is its prediction using crossvalidation, and predSE is the
prediction standard error using crossvalidation.
Bias, computed as mean(obs  pred).
Standardized bias, computed as mean((obs  pred)/predSE).
Root meansquared prediction error, computed as sqrt(mean((obs  pred)^2))
Root average variance, computed as sqrt(mean(predSE^2)). If the prediction standard errors are being estimated well, this should be close to RMSPE.
standardized meansquared prediction error, computed as mean(((obs  pred)/predSE)^2). If the prediction standard errors are being estimated well, this should be close to 1.
The proportion of times that obs was within the prediction interval formed from pred + qt(.9, df)*predSE, where qt is the quantile t function, and df is the number of degrees of freedom. If there is little bias and the prediction standard errors are being estimated well, this should be close to 0.8 for large sample sizes.
The proportion of times that obs was within the prediction interval formed from pred + qt(.95, df)*predSE, where qt is the quantile t function, and df is the number of degrees of freedom. If there is little bias and the prediction standard errors are being estimated well, this should be close to 0.9 for large sample sizes.
The proportion of times that obs was within the prediction interval formed from pred + qt(.975, df)*predSE, where qt is the quantile t function, and df is the number of degrees of freedom. If there is little bias and the prediction standard errors are being estimated well, this should be close to 0.95 for large sample sizes.
Output is a data.frame with with a single entry for the columns as listed above.
Jay Ver Hoef support@SpatialStreamNetworks.com
InfoCritCompare
, glmssn
, CrossValidationSSN
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  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'))
## 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'))
CrossValidationStatsSSN(fitSp)

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