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
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