[R] Penalized likelihood-ratio chi-squared statistic: L.R. model for Goodness of fit?

From: Jan Verbesselt <Jan.Verbesselt_at_biw.kuleuven.be>
Date: Sat 13 Aug 2005 - 19:09:38 EST


Dear R list,  

From the lrm() binary logistic model we derived the G2 value or the likelihood-ratio chi-squared statistic given as L.R. model, in the output of the lrm().    

How can this value be penalized for non-linearity (we used splines in the lrm function)?  

lrm.iRVI <- lrm(arson ~ rcs(iRVI,5),
penalty=list(simple=10,nonlinear=100,nonlinear.interaction=4))  

This didn’t work properly.    

The aim is to obtain a value that can be used to compare the goodness of fit of the different univariate binary logistic models.  

(The lower the value, the better the fit)    

Kind regards,

Jan



Ir. Jan Verbesselt
Research Associate
Group of Geomatics Engineering
Department Biosystems ~ M³-BIORES
Vital Decosterstraat 102, 3000 Leuven, Belgium Tel: +32-16-329750 Fax: +32-16-329760
http://gloveg.kuleuven.ac.be/
 

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