Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/InfoCritCompare.R
InfoCritCompare
displays important model criteria for each object
of class glmssn object in the model list.
1  InfoCritCompare(model.list)

model.list 
a list of fitted glmssnclass model objects in the form

InfoCritCompare
displays important model criteria that can be
used to compare and select spatial statistical models. For instance, spatial
models can be compared with nonspatial models, other spatial models, or both.
InfoCritCompare
returns a data.frame of the model criteria for
each specified glmssnclass object. These are useful for comparing and
selecting models. The columns in the data.frame are described below. In the
description below 'obs' is an observed data value, 'pred' is its prediction
using crossvalidation, and 'predSE' is the prediction standard error using
crossvalidation.
model formula
estimation method, either maximum likelihood (ML) or restricted maximum likelihood (REML)
names of the variance components, including the autocovariance model names, the nugget effect, and the random effects.
2 loglikelihood. Note that the neg2LogL is only returned if the
Gaussian distribution (default) was specified when creating the glmssn
object.
Akaike Information Criteria (AIC). Note that AIC is only returned if the
Gaussian distribution (default) was specified when creating the glmssn
object.
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 the observed value 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 observed value 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 the observed value 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.
Jay Ver Hoef support@SpatialStreamNetworks.com
glmssn
, summary.glmssn
, AIC
,
CrossValidationStatsSSN
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  library(SSN)
data(modelFits)
#for examples only, make sure all models have the correct path
#if you use importSSN(), path will be correct
fitNS$ssn.object < updatePath(fitNS$ssn.object,
paste0(tempdir(),'/MiddleFork04.ssn'))
fitRE$ssn.object < updatePath(fitRE$ssn.object,
paste0(tempdir(),'/MiddleFork04.ssn'))
fitSp$ssn.object < updatePath(fitSp$ssn.object,
paste0(tempdir(),'/MiddleFork04.ssn'))
fitSpRE1$ssn.object < updatePath(fitSpRE1$ssn.object,
paste0(tempdir(),'/MiddleFork04.ssn'))
fitSpRE2$ssn.object < updatePath(fitSpRE2$ssn.object,
paste0(tempdir(),'/MiddleFork04.ssn'))
compare.models < InfoCritCompare(list(fitNS, fitRE, fitSp, fitSpRE1, fitSpRE2))
# Examine the model criteria
compare.models
# Compare the AIC values for all models with random effects
compare.models[c(2,4,5),c("Variance_Components","AIC")]
# Compare the RMSPE for the spatial models
compare.models[c(3,4,5),c("Variance_Components","RMSPE")]
# Compare the RMSPE between spatial and nonspatial models
compare.models[c(1,3),c("formula","Variance_Components", "RMSPE")]

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