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
