singular convergence with lmer()

Dear all,

Using the latest CRAN version of lme4 
 I get the following warning from lmer() :

Warning message: 
In mer_finalize(ans) : singular convergence (7)

My model is not complicated and it works fine with SAS (if you are
interested in the details of my model see
forums.cirad.fr/logiciel-R/viewtopic.php?t=5071 )

What argument could I change in lmer() to overcome this warning ?

dat$Part <- factor(dat$Part)
( fit <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat) )

laurent stephane <laurent_step at ...> writes:

Dear all,

Using the latest CRAN version of lme4
 I get the following warning from lmer() :

Warning message:
In mer_finalize(ans) : singular convergence (7)

My model is not complicated and it works fine with SAS (if you are
interested in the details of my model see
forums.cirad.fr/logiciel-R/viewtopic.php?t=5071 )

What argument could I change in lmer() to overcome this warning ?

  This warning emerges from the nlminb optimizer used in the guts
of lme4, and I don't think there's much you can do to suppress it
or change the behavior of nlminb to avoid it.  The best you could
do would be to use other packages (SAS, other versions of lme4 or
nlme, etc.) to see if the correct answer was achieved despite the
warning.

  Ben Bolker

Dear all,

Using the latest CRAN version of lme4 I get the following warning from lmer() :

Warning message:
In mer_finalize(ans) : singular convergence (7)

My model is not complicated and it works fine with SAS (if you are interested in the details of my model see forums.cirad.fr/logiciel-R/viewtopic.php?t=5071 )

What argument could I change in lmer() to overcome this warning ?

Kind regards,
SL

        [[alternative HTML version deleted]]

str(dat)

fit

Dear all,

Using the latest CRAN version of lme4 I get the following warning from lmer() :

Warning message:
In mer_finalize(ans) : singular convergence (7)

My model is not complicated and it works fine with SAS (if you are
interested in the details of my model see
forums.cirad.fr/logiciel-R/viewtopic.php?t=5071 )

What argument could I change in lmer() to overcome this warning ?

Kind regards,
SL

        [[alternative HTML version deleted]]

It converged for me for lme4_0.999999-0.
Estimates look different from what you posted at the site.
Reinhold Kliegl

dat$Part <- factor(dat$Part)
( fit <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat) )

Linear mixed model fit by REML
Formula: y ~ (1 | Operator) + (1 | Part) + (1 | Part:Operator)
  Data: dat
   AIC    BIC logLik deviance REMLdev
-619.7 -603.4  314.9   -630.3  -629.7
Random effects:
Groups        Name        Variance   Std.Dev.
Part:Operator (Intercept) 0.00081854 0.028610
Part          (Intercept) 1.06721729 1.033062
Operator      (Intercept) 0.00031226 0.017671
Residual                  0.00063295 0.025159
Number of obs: 192, groups: Part:Operator, 96; Part 12; Operator, 8

Fixed effects:
           Estimate Std. Error t value
(Intercept)   2.7171     0.2983   9.109

I wonder if it is a version issue.  Using the data at forums.cirad.fr/logiciel-R/viewtopic.php?t=5071 I get the following (which matches what SAS produces):

str(dat)

'data.frame':   192 obs. of  3 variables:
 $ Operator: Factor w/ 8 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Part    : Factor w/ 12 levels "1","2","3","4",..: 1 1 2 2 3 3 4 4 5 5 ...
 $ y       : num  0.724 0.699 1.554 1.535 1.786 ...

fit

Linear mixed model fit by REML
Formula: y ~ (1 | Operator) + (1 | Part) + (1 | Part:Operator)
   Data: dat
    AIC    BIC logLik deviance REMLdev
 -619.7 -603.4  314.9   -630.3  -629.7
Random effects:
 Groups        Name        Variance   Std.Dev.
 Part:Operator (Intercept) 0.00081854 0.028610
 Part          (Intercept) 1.06721993 1.033063
 Operator      (Intercept) 0.00031226 0.017671
 Residual                  0.00063295 0.025159
Number of obs: 192, groups: Part:Operator, 96; Part, 12; Operator, 8

Fixed effects:
            Estimate Std. Error t value
(Intercept)   2.7171     0.2983   9.109


I'm using R 1.15.0 32-bit on Windows XP and Package lme4 version 0.999375-42.

Jim Baldwin
Pacific Southwest Research Station
USDA Forest Service
Albany, California

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Joshua Wiley
Sent: Sunday, July 08, 2012 1:52 PM
To: laurent stephane
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] singular convergence with lmer()

Notice that the variance of one of your random effects is estimated at 0.  I suspect that this is the source of the singular convergence.
IIRC proc mixed (which is what I assume you are using in SAS) uses a somewhat different approach to to estimate the random effects than does lme4.

Although it seems to work for Reinhold, again some of the variances are vanishingly small, which seems to me like it may suggest some of the effects are borderline on 0 and perhaps slightly different estimation methods either get "really small" or simply "0" and if 0, you get a warning.  I would also consider simplifying your model (although likelihood ratio tests seem to suggest a significant decrement in the likelihood fixing the variance at 0).

Cheers,

Josh

On Thu, Jul 5, 2012 at 1:01 AM, laurent stephane <laurent_step at yahoo.fr> wrote:

Dear all,

Using the latest CRAN version of lme4 I get the following warning from lmer() :

Warning message:
In mer_finalize(ans) : singular convergence (7)

My model is not complicated and it works fine with SAS (if you are
interested in the details of my model see
forums.cirad.fr/logiciel-R/viewtopic.php?t=5071 )

What argument could I change in lmer() to overcome this warning ?

Kind regards,
SL

        [[alternative HTML version deleted]]

--
Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group University of California, Los Angeles https://joshuawiley.com/