Thus, del(AIC) = 2*(log(likelihood ratio)-del(k)). If the trend is strictly linear, then it involves only 1 parameter, so del(k) = 1. Then log(likelihood ratio) = 1+0.5*(156.7-148.6) = 1+0.5*8.1 = 5.05. From this, a significance probability (p value) can be obtained as follows:
> pchisq(5.05, 1, lower.tail=FALSE) [1] 0.02462594
For more information, see, e.g., Burnham and Anderson (2002) Model Selection and Multi-Model Inference, 2nd ed. (Springer) or Ripley (1996) Pattern Recognition and Neural Networks (Cambridge U. Pr.). Also, www.r-project.org -> search -> "R site search" for "Burnham and Anderson".
hope this helps. spencer graves
David Richard John Pleydell wrote:
Hi
I have two geostatistical models from geoR. An ordinary kriging model with AIC=-148.6 and a universal kriging model with AIC=-156.7, there are 345 data points. The improvement shown by the AIC by adding a trend component to the model seems quite small given the number of data points, is there a test to see if the improvement to the model fit is significant?
Thanks David
************************************************ David Pleydell D 31 Peel Building Telford Institute of Environmental Systems School of Environment and Life Sciences University of Salford M5 4WT phone: +44 161 2952094 fax: +44 161 295 5015
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