Trevor Wiens wrote:
I was unsure of what suitable goodness-of-fit tests existed in R for logistic 
regression. After searching the R-help archive I found that using the Design 
models and resid, could be used to calculate this as follows:

d <- datadist(mydataframe)
options(datadist = 'd')
fit <- lrm(response ~ predictor1 + predictor2..., data=mydataframe, x =T, y=T)
resid(fit, 'gof').

I set up a script to first use glm to create models use stepAIC to determine 
the optimal model. I used this instead of fastbw because I found the AIC values 
to be completely different and the final models didn't always match. Then my 
script takes the reduced model formula and recreates it using lrm as above. Now 
the problem is that for some models I run into an error to which I can find no 
reference whatsoever on the mailing list or on the web. It is as follows:

test.lrm <- lrm(cclo ~ elev + aspect + cti_var + planar + feat_div + loamy + sands + sandy + wet + slr_mean, data=datamatrix, x = T, y = T)
singular information matrix in lrm.fit (rank= 10 ). Offending variable(s):
slr_mean Error in j:(j + params[i] - 1) : NA/NaN argument



Now if I add the singularity criterion and make the value smaller than the default of 1E-7 to 1E-9 or 1E-12 which is the default in calibrate, it works. Why is that?

Not being a statistician but a biogeographer using regression as a tool, I don't really understand what is happening here.

Does changing the tol variable, change how I should interpret goodness-of-fit 
results or other evaluations of the models created?

I've included a summary of the data below (in case it might be helpful) with 
all variables in the data frame as it was easier than selecting out the ones 
used in the model.

Thanks in advance.

T

The goodness of fit test only works on prespecified models. It is not valid when stepwise variable selection is used (unless perhaps you use alpha=0.5).


--
Frank E Harrell Jr   Professor and Chair           School of Medicine
                     Department of Biostatistics   Vanderbilt University

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