glmer mixed models inconsistent between lme4 updates - r

Question

This is a long post. I’m having trouble attaining consistent model results between lme4 versions. My old computer was running R 3.0.0 “Masked Marvel”, with the 0.999999-2 version of lme4, and my new work computer is running R 3.0.2 “Frisbee Sailing” with lme4 version 1.1-2. They are not giving me the same model outputs. Omitting the new lme4, I installed every package update on my old laptop one-by-one and ran the models. Then, updated R itself and ran the models. Finally, I made further updates to packages for R 3.0.2, and ran the models once more. They remained consistent with the original analysis. I wasn’t able to run the models with the most recent MASS package with R 3.0.0, and with the most recent nlme package in R 3.0.2. The error message I received in those cases was the same, and is below:
“Error in validObject(.Object) : invalid class “mer” object: Slot L must be a monotonic LL' factorization of size dims['q']”
I’m wondering if someone can help shed light on this before I update to lme4 1.1-2 to complete my analyses. There doesn’t appear to be a changelog at the address linked in R Studio…http://cran.rstudio.com/web/packages/lme4/NEWS
Maybe something else has changed on my end? I’ve been using the same datafile, and setting it up the same within R.
I am not sure how to attach data, or how to make this easiest for you all to run through yourselves. I can do that with some instruction. Below are the outputs for 2 models before the updates, and the same models after. The variation occurs in models that I haven't included here as well, some worse than others. It definitely affects an AIC ranking.
lme4 version 0.999999-2
R version 3.0.0 (2013-04-03) -- "Masked Marvel"
at1500o <- glmer(apred ~
(1 | fFarm) + (1 | Eps) + (1 | fExp),
family = binomial,
data = PU3.atristis)
summary(at1500o)
Number of levels of a grouping factor for the random effects
is *equal* to n, the number of observations
Warning message: In mer_finalize(ans) : false convergence (8)
Generalized linear mixed model fit by the Laplace approximation
Formula: apred ~ (1 | fFarm) + (1 | Eps) + (1 | fExp)
Data: PU3.atristis
AIC BIC logLik deviance
182.2 193 -87.11 174.2
Random effects:
Groups Name Variance Std.Dev.
Eps (Intercept) 1.6630e+01 4.0780e+00
fFarm (Intercept) 2.4393e+00 1.5618e+00
fExp (Intercept) 5.8587e-13 7.6542e-07
Number of obs: 110, groups: Eps, 110; fFarm, 17; fExp, 2
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.1406 0.7126 -8.618 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
False convergence didn’t seem to be a problem
at1500a <- glmer(apred ~
pctfield1500 * factor(SiteTrt) +
(1 | fFarm) + (1 | Eps) + (1 | fExp),
family = binomial,
data = PU3.atristis)
summary(at1500a)
Number of levels of a grouping factor for the random effects
is *equal* to n, the number of observations
Warning message: In mer_finalize(ans) : false convergence (8)
Generalized linear mixed model fit by the Laplace approximation
Formula: apred ~ pctfield1500 + factor(SiteTrt) + pctfield1500:factor(SiteTrt) + (1 | fFarm) + (1 | Eps) + (1 | fExp)
Data: PU3.atristis
AIC BIC logLik deviance
185.2 209.5 -83.59 167.2
Random effects:
Groups Name Variance Std.Dev.
Eps (Intercept) 2.4539e+01 4.95370375
fFarm (Intercept) 8.1397e-01 0.90220336
fExp (Intercept) 1.3452e-08 0.00011598
Number of obs: 110, groups: Eps, 110; fFarm, 17; fExp, 2
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.82245 2.64044 -2.584 0.00977 **
pctfield1500 -0.01041 0.10849 -0.096 0.92353
factor(SiteTrt)2 2.47654 3.97069 0.624 0.53282
factor(SiteTrt)3 4.33391 4.65551 0.931 0.35190
pctfield1500:factor(SiteTrt)2 -0.05073 0.13117 -0.387 0.69895
pctfield1500:factor(SiteTrt)3 -0.05729 0.14524 -0.394 0.69323
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) pc1500 f(ST)2 f(ST)3 p1500:(ST)2
pctfild1500 -0.876
fctr(StTr)2 -0.665 0.582
fctr(StTr)3 -0.571 0.504 0.380
p1500:(ST)2 0.724 -0.827 -0.850 -0.417
p1500:(ST)3 0.654 -0.747 -0.435 -0.887 0.618
Lme4 version 1.1-2
R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
at1500o <- glmer(apred ~
(1 | fFarm) + (1 | Eps) + (1 | fExp),
family = binomial,
data = PU3.atristis)
summary(at1500o)
Generalized linear mixed model fit by maximum likelihood ['glmerMod']
Family: binomial ( logit )
Formula: apred ~ (1 | fFarm) + (1 | Eps) + (1 | fExp)
Data: PU3.atristis
AIC BIC logLik deviance
236.8296 247.6316 -114.4148 228.8296
Random effects:
Groups Name Variance Std.Dev.
Eps (Intercept) 4.799e+01 6.927e+00
fFarm (Intercept) 6.542e-01 8.088e-01
fExp (Intercept) 6.056e-10 2.461e-05
Number of obs: 110, groups: Eps, 110; fFarm, 17; fExp, 2
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.975 1.115 -7.154 8.43e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
No warnings anymore, but everything is pretty different. The model fit statement has changed from the Laplace approximation, to maximum likelihood [‘glmerMod’].
at1500a <- glmer(apred ~
pctfield1500 * factor(SiteTrt) +
(1 | fFarm) + (1 | Eps) + (1 | fExp),
family = binomial,
data = PU3.atristis)
summary(at1500a)
Generalized linear mixed model fit by maximum likelihood ['glmerMod']
Family: binomial ( logit )
Formula: apred ~ pctfield1500 + factor(SiteTrt) + pctfield1500:factor(SiteTrt) + (1 | fFarm) + (1 | Eps) + (1 | fExp)
Data: PU3.atristis
AIC BIC logLik deviance
242.7874 267.0917 -112.3937 224.7874
Random effects:
Groups Name Variance Std.Dev.
Eps (Intercept) 3.346e+01 5.784e+00
fFarm (Intercept) 1.809e-07 4.253e-04
fExp (Intercept) 4.380e-11 6.618e-06
Number of obs: 110, groups: Eps, 110; fFarm, 17; fExp, 2
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.46800 3.04197 -2.455 0.0141 *
pctfield1500 0.00032 0.12301 0.003 0.9979
factor(SiteTrt)2 2.05618 4.57444 0.450 0.6531
factor(SiteTrt)3 6.72139 5.20895 1.290 0.1969
pctfield1500:factor(SiteTrt)2 -0.04882 0.14911 -0.327 0.7434
pctfield1500:factor(SiteTrt)3 -0.11324 0.16495 -0.686 0.4924
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) pc1500 f(ST)2 f(ST)3 p1500:(ST)2
pctfild1500 -0.875
fctr(StTr)2 -0.665 0.582
fctr(StTr)3 -0.584 0.511 0.388
p1500:(ST)2 0.722 -0.825 -0.850 -0.422
p1500:(ST)3 0.653 -0.746 -0.434 -0.886 0.615
Cheers,
Nava

Answer 1

Version 1.0 of the lme4 package marked a major change, with a pretty substantial rework of the underlying fitting code. From the changelog:
Because the internal computational machinery has changed, results from
the newest version of lme4 will not be numerically identical to those
from previous versions. For reasonably well- defined fits, they will
be extremely close (within numerical tolerances of 1e-4 or so), but
for unstable or poorly-defined fits the results may change, and very
unstable fits may fail when they (apparently) succeeded with previous
versions. Similarly, some fits may be slower with the new version,
although on average the new version should be faster and more stable.
More numerical tuning options are now available (see below);
non-default settings may restore the speed and/or ability to fit a
particular model without an error. If you notice significant or
disturbing changes when fitting a model with the new version of lme4,
please notify the maintainers.
Considering your model fits using the 0.9999 version of lme4 produced false convergence warnings, your model might be in the "unstable fits" category this is talking about.