Message-ID: <57169E57.4030308@psychologie.uzh.ch>
Date: 2016-04-19T21:08:39Z
From: Henrik Singmann
Subject: Why does the log-likelihood ratio test need a larger maxfun?
In-Reply-To: <CAHpascdPKy=FNWw42JHKu4GT5Oe+T=9qPUwEdtCUnwHiXJQ85Q@mail.gmail.com>
Hi Zhaohong,
The following warning indicates that your results cannot be trusted:
> convergence code 1 from bobyqa: bobyqa -- maximum number of function
> evaluations exceeded
This means that the results are most likely not the MLEs. You should
rerun the model with higher values of maxfun. For example:
control = g/lmerControl(optCtrl = list(maxfun = 1e6))
Why you do not see the warning when fitting seems weird.
Hope that helps,
Henrik
Am 19.04.2016 um 17:48 schrieb Zhaohong:
> Dear All,
>
> I am running a log-likelihood ratio test on two mixed models (differing
> only in one variable) to test the significance of that variable. The two
> mixed models (I set the maxfun=500000 for both) are able to converge with
> no warnings, but the anova(model1, model2) gives warnings as follows:
> Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
> ..1 92 1450.7 2029.6 -633.33 1266.7
> object 93 1452.5 2037.7 -633.22 1266.5 0.2098 1 0.6469
> Warning messages:
> 1: In commonArgs(par, fn, control, environment()) :
> maxfun < 10 * length(par)^2 is not recommended.
> 2: In optwrap(optimizer, devfun, x at theta, lower = x at lower, calc.derivs =
> TRUE) :
> convergence code 1 from bobyqa: bobyqa -- maximum number of function
> evaluations exceeded
> 3: In commonArgs(par, fn, control, environment()) :
> maxfun < 10 * length(par)^2 is not recommended.
> 4: In optwrap(optimizer, devfun, x at theta, lower = x at lower, calc.derivs =
> TRUE) :
> convergence code 1 from bobyqa: bobyqa -- maximum number of function
> evaluations exceeded
>
> The Pr(>Chisq) given from this test is 0.6469, but the Pr(>|t|) from the
> lmerTest is 0.00160 ** , which seems more likely the case, because the
> t-value from the lmer model summary is 3.445, with a total Number of obs:
> 3996.
>
> So I thought maybe I need to increase the maxfun for the anova() test. I
> rerun the test with the command anova(model1,
> model2, ,control=lmerControl(optCtrl=list(maxfun=5000000))), and got the
> same results:
> Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
> ..1 92 1450.7 2029.6 -633.33 1266.7
> object 93 1452.5 2037.7 -633.22 1266.5 0.2098 1 0.6469
> Warning messages:
> 1: In commonArgs(par, fn, control, environment()) :
> maxfun < 10 * length(par)^2 is not recommended.
> 2: In optwrap(optimizer, devfun, x at theta, lower = x at lower, calc.derivs =
> TRUE) :
> convergence code 1 from bobyqa: bobyqa -- maximum number of function
> evaluations exceeded
> 3: In commonArgs(par, fn, control, environment()) :
> maxfun < 10 * length(par)^2 is not recommended.
> 4: In optwrap(optimizer, devfun, x at theta, lower = x at lower, calc.derivs =
> TRUE) :
> convergence code 1 from bobyqa: bobyqa -- maximum number of function
> evaluations exceeded
>
> I am wondering what I should do in this situation then?
>
> Thanks a lot!
>
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