[R] What does this warning message (from optim function) mean?

[Prof Brian Ripley]

You mean 'TRUE': 'T' is a variable in R, with initial value TRUE.

On Wed, 25 Aug 2010, Sally Luo wrote:

> Hi R users,
> I am trying to use the optim function to maximize a likelihood funciton, and
> I got the following warning messages.
> Could anyone explain to me what messege 31 means exactly?  Is it a cause for
> concern?
> Since the value of convergence turns out to be zero, it means that the
> converging is successful, right?
> So can I assume that the parameter estimates generated thereafter are
> reliable MLE estimates?
> Thanks a lot for your help.
>
> Maomao
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>> p<-optim(c(0,0,0), f, method ="BFGS", hessian =T, y=y,X=X,W=W)
>
> There were 31 warnings (use warnings() to see them)
>
>> warnings()
>
> Warning messages:
>
> 1: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 2: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 3: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 4: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 5: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 6: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 7: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 8: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 9: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 10: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 11: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 12: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 13: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 14: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 15: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 16: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 17: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 18: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 19: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 20: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 21: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 22: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 23: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 24: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 25: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 26: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 27: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 28: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 29: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 30: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced
>
> 31: In if (hessian) { ... :
>
>  the condition has length > 1 and only the first element will be used
>
>> p$counts
>
> function gradient
>
>     148       17
>
>> p$convergence
>
> [1] 0
>
> 	[[alternative HTML version deleted]]
>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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