Dear Alain,

You may speed up the analysis by using the sample covariance matrix based on
a listwise deletion:
cov.cfa <- cov(your.raw.data, use="complete.obs")

Since you have 36671 cases, the results should be similar to those based on
the raw data unless you have lots of missing data and/or the data are
missing at random.

By the way, if your questions are directly related to SEM, you may get more
responses from SEMNET (http://alabamamaps.ua.edu/archives/semnet.html).

Hope it helps.

Regards,
Mike
-- 
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 Mike W.L. Cheung               Phone: (65) 6516-3702
 Department of Psychology       Fax:   (65) 6773-1843
 National University of Singapore
 http://courses.nus.edu.sg/course/psycwlm/internet/
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On Thu, Jun 2, 2011 at 1:21 AM, yrosseel <yrosseel@gmail.com> wrote:

> On 06/01/2011 05:39 PM, D. Alain wrote:
>
>> Thank you Yves and Mike,
>>
>> your comments make sence, however they do not resolved my problem: The p
>> < N is the result of my poor attempt to give a reproducible example. My
>> "real" dataframe has a dim of 36671 cases an 41 variables.
>> Following your advice, Yves, I passed my model without lavaanification
>> (just "cfa(cfa.model, data=df.cfa,missing="ml")"), but now R is working
>> for hours without printing any results...
>>
>
> To monitor progress, you can use the verbose=TRUE argument. If you are not
> fitting your final model, you may want to use se="none" (no standard errors)
> and test="none" (no test statistic) to speed things up.
>
> I've ran analyses that took multiple days, using both lavaan and commercial
> software. It is annoying (the wait), but not unusual. The time to fit is a
> function of the number of missing patterns you have in your data, and the
> number of variables.
>
> Yves.
>

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