Utkarsh Singhal wrote:> Hi R,
>
>
>
> I am getting this error while trying to use 'lrm' function with
nine
> independent variables:
>
>
>
>> res >
lrm(y1994~WC08301+WC08376+WC08316+WC08311+WC01001+WC08221+WC08106+WC0810
> 1+WC08231,data=y)
>
>
>
> singular information matrix in lrm.fit (rank= 8 ). Offending
> variable(s):
>
> WC08101 WC08221
>
> Error in j:(j + params[i] - 1) : NA/NaN argument
>
>
>
> Now, if I take choose only four independent variables then there is no
> error.
>
>
>
>> res = lrm(y1994~ WC08221+WC08106+WC08101+WC08231,data=y)
>
>
>
>
>
> But strangely, if I use 'glm', with the family as binomial(logit)
and
> with the same dataset, it is working perfectly fine.
>
>
>
>> res >
glm(y1994~WC08301+WC08376+WC08316+WC08311+WC01001+WC08221+WC08106+WC0810
> 1+WC08231,data=y,family=binomial(logit))
>
>
>
> Any ideas..?
>
>
>
> Regards
>
> Utkarsh
Design's fitting functions are not kind about ignoring parameters
associated with singular covariance matrices. In glm you should see a
zero for such coefficients. In design you have to delete the singular
variables manually. Occasionally you have to tweak the tol argument to lrm.
A new function in Hmisc called redun will run a redundancy analysis to
help understand the predictor collinearities.
Frank
>
>
>
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--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University