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Re: st: "not concave"
From
Nick Cox <[email protected]>
To
[email protected]
Subject
Re: st: "not concave"
Date
Fri, 1 Mar 2013 13:35:14 +0000
No data, thanks.
As said, I am not an expert here and can't comment authoritatively on
what might be changed, but others may be able to help much more.
Nick
On Fri, Mar 1, 2013 at 1:24 PM, Wahideh Achbari
<[email protected]> wrote:
> Thanks for this Nick. Here is the syntax:
>
> sem (Poleff -> opinion_not_vote) (Poleff -> say_pol) (Poleff ->
> care_pol) (Poleff -> pol_not_complex), method(mlmv) standardized
> latent(Poleff ) nocapslatent
> (47 all-missing observations excluded)
>
> There was no fit at all as the iterations kept running with "not
> concave" in brackets.
>
> If you need the data I can also send that.
>
> Thanks again,
>
> On 1 March 2013 14:18, Nick Cox <[email protected]> wrote:
>> It's not the model; it's your log-likelihood function that is awkward
>> over part of the parameter space.
>>
>> It's important to realise also that maximum likelihood is emphatically
>> not an algorithm. It is an estimation method.
>>
>> This isn't necessarily a big problem. Nor is the absence of warning
>> messages from other software necessarily diagnostic. Perhaps Stata is
>> just more explicit, or they used different algorithms to implement
>> their fits. It's difficult to say.
>>
>> However, it is not clear from your post whether the command you used
>> -- not even spelled out here -- converged to give a fit.
>>
>> To say more experts in this technique (not me) would need to see your
>> command syntax and results from Stata.
>>
>> Your post is strong on "I think I have a problem" but much more
>> context is needed for a fuller answer. See the FAQ on advice on this.
>>
>> Nick
>>
>> On Fri, Mar 1, 2013 at 12:55 PM, Wahideh Achbari
>> <[email protected]> wrote:
>>
>>> Here's a query about a very simple confirmatory factor analysis with 4
>>> factors. I have been trying to estimate this model, but unfortunately
>>> the model is "not concave" running a Maximum Liklihood algorithm. What
>>> surprises me most is that I have been running the same model in AMOS
>>> and MPLUS without any difficulties. I would like to know what can be
>>> done to remedy this. Your help is greatly appreciated.
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