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2005 Aug 12
0
HowTo derive a correct likelihood-ratio chi-squared statistic from lrm() with a rsc() ?
...model value we obtain from different models are just the inverse of the derived c-index and AIC (from the lrm) (?!). So the L.R. Model is higher for the best model and not lower as explained above… The models: These are the models knots <- 5 lrm.iRVI <- lrm(arson ~ rcs(iRVI,knots)) lrm.ARND <- lrm(arson ~ rcs(ARND,knots)) lrm.ARNDiRVI <- lrm(arson ~ rcs(ARND,knots)+rcs(iRVI,knots)) and the L.R. model values are bigger, the better the model becomes (it is the inverse of...
2005 Aug 13
1
Penalized likelihood-ratio chi-squared statistic: L.R. model for Goodness of fit?
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...