Try using bobyqa method and see what happens. This was used by old lme4.
New version uses nelder_mead.
Kevin
On Thu, Sep 19, 2013 at 1:25 PM, Marko Bachl <marko.bachl at uni-hohenheim.de>
wrote:
Dear list, dear lme4-Developers,
first of all, thanks a lot for the terrific work developing lme4 and
explaining it here on this list.
Recently, I installed the most recent version of lme4 from github. I
re-ran an older model that worked well with lme4_0.999999-0 from CRAN.
With the development version lme4_1.1-0 I get an implausibly large
estimate for one of the random effects variances.
Short version:
My model is: m0 = lmer(rtr2 ~ turnsec + (turnsec | kombiid) + (turnsec
| turnid) + (turnsec | idnr), verbose = T, d1)
The R data file can be downloaded from
https://dl.dropboxusercontent.com/u/3262123/data.RData (1,3 MB).
The CRAN version gives 2.11 (Intercept) and 0.026 (turnsec) as
estimates for the random effects in "turnid". These estimates are
sensible and in line with the estimates from a model with only random
intercepts. The devel version gives 102.9740 (Intercept) and 81.0018
(turnsec), which is implausibly large. All other estimates are
approximately equal in both versions.
Do you have any suggestions why the one variance estimate of the devel
version differs so drastically from the CRAN version? And can the
sensible result of the CRAN version be trusted?
More in detail: I analyze how respondents continuously rate a
politician during 34 answers of a televised debate using a response
dial on a scale from -50 to 50. The rating is recorded every second
for the approx. 30 seconds of each answer.
My model is: m0 = lmer(rtr2 ~ turnsec + (turnsec | kombiid) + (turnsec
| turnid) + (turnsec | idnr), verbose = T, d1)
rtr2 is the rating, turnsec is the second of the answer, starting with
0. kombiid is an unique identifier for each combination of respondents
and answers (n = 4762). turnid is an unique identifier for each answer
(n = 34). idnr is an unique identifier for each respondent (n = 172).
As every respondent rates every answer, turnid and idnr are crossed
(but not balanced due to missing data for some combinations of answers
and respondents). The R data file can be downloaded from
https://dl.dropboxusercontent.com/u/3262123/data.RData (1,3 MB).
The variance estimates from the lme4_0.999999-0 version are
theoretically sensible and in line with the estimates from a model
with only random intercepts.
Random effects:
Groups Name Variance Std.Dev. Corr
kombiid (Intercept) 45.821653 6.76917
turnsec 0.494588 0.70327 -0.250
idnr (Intercept) 8.307388 2.88225
turnsec 0.138710 0.37244 0.272
turnid (Intercept) 2.110807 1.45286
turnsec 0.026125 0.16163 -0.062
Residual 40.675410 6.37773
The same model using the same data with lme4_1.1-0 gives these estimates:
Random effects:
Groups Name Variance Std.Dev. Corr
kombiid (Intercept) 45.6992 6.7601
turnsec 0.4946 0.7033 -0.25
idnr (Intercept) 8.8504 2.9750
turnsec 0.1386 0.3723 0.27
turnid (Intercept) 102.9740 10.1476
turnsec 81.0018 9.0001 0.18
Residual 40.6540 6.3760
The variance estimates for the groups kombiid and idnr are almost the
same, but the estimate for turnid is implausibly large.
Do you have any suggestions why the one variance estimate of the devel
version differs so drastically from the CRAN version? And can the
sensible result of the CRAN version be trusted?
Thanks a lot for any advice
Marko