question about fitted values form geoR - results 'too good'
Kriging is an exact interpolator. It will always reproduce the values that you use to predict if they are located on the same locations. You should use an independant data set (or some sort of cross-validation) to check how good your prediction is.
On Nov 8, 2007 10:47 PM, Ken Nussear <knussear at usgs.gov> wrote:
Hi I'm using geoR for some spatial linear models and I'm getting surprisingly optimistic values from the spatial models relative to the non-spatial, even when the models appear to be performing about equally (by AIC comparison) For example This model relating encounter rates of lizards to a soil substrate parameter gives
summary(m2)
Summary of the parameter estimation
-----------------------------------
Estimation method: maximum likelihood
Parameters of the mean component (trend):
beta0 beta1
0.0312 0.0024
Parameters of the spatial component:
correlation function: exponential
(estimated) variance parameter sigmasq (partial sill) = 0.0082
(estimated) cor. fct. parameter phi (range parameter) = 797.1
anisotropy parameters:
(fixed) anisotropy angle = 0 ( 0 degrees )
(fixed) anisotropy ratio = 1
Parameter of the error component:
(estimated) nugget = 0.002
Transformation parameter:
(fixed) Box-Cox parameter = 1 (no transformation)
Maximised Likelihood:
log.L n.params AIC BIC
"53.44" "5" "-96.87" "-86.57"
non spatial model:
log.L n.params AIC BIC
"51.99" "3" "-97.98" "-91.8"
With a difference in AIC of only about 1. However looking at the predicted values versus the fits for the model The spatial model fitted values appear to be some how too good.
> cor(fitted.likGRF(m2, spatial=TRUE), td$Crotaphytus)
[1] 0.9934701
> cor(fitted.likGRF(m2, spatial=FALSE), td$Crotaphytus)
[1] 0.2522837 So I don't get how the spatial model with only a delta AIC of 1 can have a correlation with the dependent variable that is this high. Am I mis-interpreting the values I'm getting from the fitted call, or is something amis. I've tried this with different data sets and I'm getting the same result. Thanks Ken
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