# [R-sig-ME] lme4 and false convergence

Goedhart, Paul paul.goedhart at wur.nl
Thu Nov 27 09:07:40 CET 2008

```I had a similar problem which disappeared after standardizing the
predictor variables. Apparently there is some numerical instability
within LME4. Below you will find a simulated example for a loglinear
model.

Paul W. Goedhart
Biometris,  Wageningen UR,  Postbus 100,  6700 AC Wageningen, The
Netherlands

> require(lme4)

Attaching package: 'Matrix'

The following object(s) are masked from package:stats :

xtabs

Warning messages:
1: package 'lme4' was built under R version 2.7.2
2: package 'Matrix' was built under R version 2.7.2
> # Simulate counts for a block design with a random blockeffect
> set.seed(93923142)
> nblock <- 10
> nplot  <- 4
> nunit  <- nblock * nplot
> block  <- rep(1:nblock, each=nplot)
> plot   <- rep(1:nplot, nblock)
> rmean  <- 25000
> beta   <- 10/rmean
> xx     <- round(rnorm(nunit, mean=rmean, sd=1000))
> rblock <- rep(rnorm(nblock, mean=0, sd=0.5), each=nplot)
> lp     <-  -9 + beta * xx
> mu     <- exp(lp + rblock)
> response <- mu
> for (ii in 1:nunit)
+   {
+   response[ii] <- rpois(1, mu[ii])
+   }
> # GLMM with UN-STANDARDISED explanatory
> mean(xx)
 24897.15
> glmer  <- glmer(response ~ xx + (1 | block), family=poisson)
Warning message:
In mer_finalize(ans) : false convergence (8)
> glmer
Generalized linear mixed model fit by the Laplace approximation
Formula: response ~ xx + (1 | block)
AIC   BIC logLik deviance
59.16 64.23 -26.58    53.16
Random effects:
Groups Name        Variance Std.Dev.
block  (Intercept) 0.66667  0.8165
Number of obs: 40, groups: block, 10

Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.025e+01  2.915e+00  -3.517 0.000437 ***
xx           4.454e-04  1.151e-04   3.872 0.000108 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
(Intr)
xx -0.995
> # GLMM with STANDARDISED explanatory
> xxstan <- (xx-mean(xx))/sd(xx)
> glmer  <- glmer(response ~ xxstan + (1 | block), family=poisson)
> glmer
Generalized linear mixed model fit by the Laplace approximation
Formula: response ~ xxstan + (1 | block)
AIC   BIC logLik deviance
57.12 62.18 -25.56    51.12
Random effects:
Groups Name        Variance Std.Dev.
block  (Intercept) 0.28949  0.53804
Number of obs: 40, groups: block, 10

Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept)   0.9260     0.1995   4.640 3.48e-06 ***
xxstan        0.3597     0.1042   3.451  0.00056 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
(Intr)
xxstan -0.166
> q()

-----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 Ben
Zuckerberg
Sent: dinsdag 25 november 2008 14:46
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] lme4 and false convergence

Dear R-users,

I am using the most updated package of lme4 (lme4_0.999375-2).  I have a
data set consisting of ~900 observations at ~440 independent survey
sites.  There are an unbalanced number of surveys at each site.  I am
attempting to develop several models evaluating the presence/absence of
a species (PRES) at these random sites (SITE) using a number of
predictor variables.  The response variable (PRES) is binomial and the
included predictor variables are either proportions (e.g., AG, FOR,
OPEN, LDEV,or DEV) or numerical (COUNT[min. 1 to max. 37,mean = 10]).
---------------------------------------------------------------------
PROBLEM 1:

I have developed the following GLMM:

mix.1<-glmer(PRES~AG+FOR+OPEN+LDEV+DEV+COUNT+(1|SITE),family=binomial,da
ta=merge1)

Warning message:
In mer_finalize(ans) : false convergence (8)

There is model output, but I am worried it might be biased.
I have tried:
-Data transformation of the predictor variables (e.g., log(COUNT)),
which does seem successful in some simpler models, but does not work
consistently.
-The most updated version of lme4

---------------------------------------------------------------------
PROBLEM #2:
In addition, I am interested in visualizing the predicted probabilities
from this output using the fixed effect function, but receive the
following message:

fixef(mix.1)
Error in UseMethod("fixef") : no applicable method for "fixef"

Any suggestions would be greatly appreciated!

--
Benjamin Zuckerberg, Ph.D.
Post-doctoral Associate
Spatial Ecologist, Citizen Science
Cornell Laboratory of Ornithology