Message-ID: <CANz9Z_JCOuu3cAFZZtpBdzrYCHU1b5OK=Od=rBYp2UDxOb-yKw@mail.gmail.com>
Date: 2012-05-10T17:57:46Z
From: Joshua Wiley
Subject: Help! What are the typical diagnosis that one can do when facing "fail to converge"?
In-Reply-To: <loom.20120510T180005-770@post.gmane.org>
On Thu, May 10, 2012 at 9:10 AM, Ben Bolker <bbolker at gmail.com> wrote:
> arun <smartpink111 at ...> writes:
>
>> I also had a similar warning message but with lmer ( (Warning
>> message:In mer_finalize(ans) : false convergence (8)).? I used
>> verbose=TRUE in the model statement.? It will print each iteration
>> estimates.? I also tried to increase the iterations, but it didn't
>> work.? Then, I found this blog
>> (http://davidhughjones.blogspot.com/2009/11/lme-false-convergence.html).
>> It says to look for betas with estimates very low and divide that
>> variable by 10 or 100.? This was the only solution that worked for
>> me.? But, the estimates of beta for the variable and its
>> interactions will be 10 fold higher than expected.
>
> ?A more generic piece of advice would be to scale and center
> all continuous predictor variables ... it won't always
> help, but it's easy to try.
>
> orig_data <- data.frame(V1=factor(1:5),V2=1:5,V3=(1:5)*0.001,V4=LETTERS[1:5])
> scaled_data <- as.data.frame(lapply(orig_data,
> ? ? ? ? ? ? ? ? ? function(x) {
> ? ? ? ? ? ? ? ? ? ? ? ?if (class(x) %in% c("integer","numeric")) {
> ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?scale(x) } else x
> ? ? ? ? ? ? ? ? ? ?}))
>
> In doing this, the scaling and centering factors seem to get lost,
> so it's not a perfect solution.
> A full-fledged auto-scaling solution *might* be built into some
> future version of lme4 ...
if so FYI is.numeric tests the mode and the mode of both integer and
numeric classes is numeric so you can simplify slightly to:
if (is.numeric(x)) {
scale(x)
} else x
>
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--
Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group
University of California, Los Angeles
https://joshuawiley.com/