We want to compute a pseudo R-squared for a model whose parameter
estimation was based on maximum likelihood (function likfit, package geoR).
I tried to compute the R2 proposed by Maddala (1983) which compare the
maximized likelihood for the model without any predictor and the
maximized likelihood for the model with all predictors.
I got a really low value (0,01%). Did I miss something? Are there other
R-squared which are more appropriate than the R2 of Maddala? :
>sp05_l01 <- likfit(cnm05g, *ini.cov.pars=vm05*, lik.method = "REML",
trend = trend.spatial(~ logIKA04 + nbLitre0 +
nbLitre1, cnm05g))#complete model
>sp05_nt <- update(sp05_01, trend = trend.spatial("cte", cnm05g))
#null model
>1-(sp07_nt$loglik/sp07_l0$loglik)^(2/590)#Maddala (1983)
[1] 0.0001510074
Thanks,
Regards,
Marion
--
Marion Jacquot
Laboratoire de Chrono-environnement
UMR UFC/CNRS 6249 USC INRA
Université de Franche-Comté
Place Leclerc
F-25030 Besançon cedex
FRANCE
Tel. : +33 (0)381 665 829
Fax : +33 (0)381 665 797
http://chrono-environnement.univ-fcomte.fr/
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