Thanks a lot for the input! I forgot to add family=binomial, for a binomial glm. Now the AIC's are positive!
I was planning to use AIC (from the binomial glm) and c-index (lrm) to compare and rank different uni-variate (one continue explanatory variable) logistic models to evaluate the 'performance' of the different explanatory variables in the different models. What is the best technique to compare these lrm.models, which are not nested? I found in literature that ranking based on different parameters (goodness of fit and predictability) that these can be used to compare uni-variate models. Thanks in advance, Regards, Jan- _______________________________________________________________________ ir. Jan Verbesselt Research Associate Lab of Geomatics Engineering K.U. Leuven Vital Decosterstraat 102. B-3000 Leuven Belgium Tel: +32-16-329750 Fax: +32-16-329760 http://gloveg.kuleuven.ac.be/ _______________________________________________________________________ -----Original Message----- From: Prof Brian Ripley [mailto:[EMAIL PROTECTED] Sent: Friday, April 15, 2005 5:06 PM To: Jan Verbesselt Cc: [email protected] Subject: Re: [R] negetative AIC values: How to compare models with negative AIC's AICs (like log-likelihoods) can be positive or negative. However, you fitted a Gaussian and not a binomial glm (as lrm does if m.arson is binary). For a discrete response with the usual dominating measure (counting measure) the log-likelihood is negative and hence the AIC is positive, but not in general (and it is matter of convention even there). In any case, Akaike only suggested comparing AIC for nested models, no one suggests comparing continuous and discrete models. On Fri, 15 Apr 2005, Jan Verbesselt wrote: > > Dear, > > When fitting the following model > knots <- 5 > lrm.NDWI <- lrm(m.arson ~ rcs(NDWI,knots) > > I obtain the following result: > > Logistic Regression Model > > lrm(formula = m.arson ~ rcs(NDWI, knots)) > > > Frequencies of Responses > 0 1 > 666 35 > > Obs Max Deriv Model L.R. d.f. P C Dxy > Gamma Tau-a R2 Brier > 701 5e-07 34.49 4 0 0.777 0.553 > 0.563 0.053 0.147 0.045 > > Coef S.E. Wald Z P > Intercept -4.627 3.188 -1.45 0.1467 > NDWI 5.333 20.724 0.26 0.7969 > NDWI' 6.832 74.201 0.09 0.9266 > NDWI'' 10.469 183.915 0.06 0.9546 > NDWI''' -190.566 254.590 -0.75 0.4541 > > When analysing the glm fit of the same model > > Call: glm(formula = m.arson ~ rcs(NDWI, knots), x = T, y = T) > > Coefficients: > (Intercept) rcs(NDWI, knots)NDWI rcs(NDWI, knots)NDWI' > rcs(NDWI, knots)NDWI'' rcs(NDWI, knots)NDWI''' > 0.02067 0.08441 -0.54307 > 3.99550 -17.38573 > > Degrees of Freedom: 700 Total (i.e. Null); 696 Residual > Null Deviance: 33.25 > Residual Deviance: 31.76 AIC: -167.7 > > A negative AIC occurs! > > How can the negative AIC from different models be compared with each other? > Is this result logical? Is the lowest AIC still correct? -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
