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I do not know if this is the appropriate forum for any discussions about Latent Gold but I figure it is worth a shot given I could not find a forum for Latent Gold and because SPSS now owns it, I think
I am currently working with datasets that behave very poorly. I run LC9 models. But many times the models do not converge properly. I therefore adjust the Tolerance levels in the Technical tab, but I am not entirely sure of what each of the three Tolerance parameters represent during the algorithm.
I also wonder if there are any papers or sites that go into detail about the adjustment of the Tolerance. Note that I very frequently adjust the /RCONVERGE criteria in SPSS during Factor Analyses to something like 0.1 to make convergence happen in fewer iterations. I figure if it is good enough for SPSS Base it must be good enough for Latent Gold.
Any and all replies are highly valued and appreciated.
Zachary
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Exactly what type of model are you trying to fit?
On Mon, May 24, 2010 at 8:28 PM, Zachary Feinstein <[hidden email]> wrote:
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In reply to this post by Zachary Feinstein
Dear Zachary,
Did you consult the technical guide? It says (p. 52-53): 6.5 Convergence
The exact algorithm implemented in Latent GOLD works as follows. The program starts with EM until either the maximum number of EM iterations ( Iteration Limits EM) or the EM convergence criterion (EM Tolerance) isreached. Then, the program switches to NR iterations which stop when the maximum number of NR iterations ( Iteration Limits Newton-Raphson) or theoverall converge criterion ( Tolerance) is reached. The convergence criterionthat is used is
[couldn't paste the formula here, sorry!]
which is the sum of the absolute relative changes in the parameters. The program also stops iterating when the change in the log-posterior is negligible, i.e., smaller than 10EXP −12.The program reports the iteration process in the Iteration Detail output file listing. Thus, it can easily be checked whether the maximum number of iterations is reached without convergence. In addition, a warning is given if one of the elements of the gradient is larger than 10 −3.
It should be noted that sometimes it is more efficient to use only the EM algorithm, which is accomplished by setting Iteration Limits Newton-Raphson = 0 in the Technical Tab. This is, for instance, the case in modelswith many parameters. With very large models, one may also consider suppressing the computation of standard errors and Wald statistics.The third tolerance (start values tolerance) is explained on pp. 53-54. However, I'd like to know why the algorithm fails to converge. Perhaps you could to try and contact Prof. Vermunt directly. I bothered him a couple of times with questions and this didn't (seem to) bother him at all and he was very helpful. HTH! Ruben van den Berg Methodologist TNS NIPO P: +31 20 522 5738 Date: Mon, 24 May 2010 17:28:06 -0700 From: [hidden email] Subject: Latent Gold Tolerance To: [hidden email] I do not know if this is the appropriate forum for any discussions about Latent Gold but I figure it is worth a shot given I could not find a forum for Latent Gold and because SPSS now owns it, I think
I am currently working with datasets that behave very poorly. I run LC9 models. But many times the models do not converge properly. I therefore adjust the Tolerance levels in the Technical tab, but I am not entirely sure of what each of the three Tolerance parameters represent during the algorithm.
I also wonder if there are any papers or sites that go into detail about the adjustment of the Tolerance. Note that I very frequently adjust the /RCONVERGE criteria in SPSS during Factor Analyses to something like 0.1 to make convergence happen in fewer iterations. I figure if it is good enough for SPSS Base it must be good enough for Latent Gold.
Any and all replies are highly valued and appreciated.
Zachary
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