

Hello lmer()  users,
A call to the lmer() function causes my installation of R (2.11.1 on
Mac OS X 10.5.8) to crash and I am trying to figure out the problem.
I have a data set with longitudinal data of four subsequent
performance measures of 1133 individuals nested in 88 groups. The data
is in long format. I hypothesize a performance increase for each
individual over time and intend to explain differences in these slopes
with individuallevel and grouplevel characteristics. Thus, I have
hierarchic data with three levels (measurement time, individual, group).
With lme() (from the nlme package), this is my first simple model:
mod1 < lme(fixed = performance ~ time,
random = ~ 1 + time  GroupID/StudentNumber,
data = dataset.long,
na.action = na.omit)
It fits to the data well. I tried to specify the same model in lmer():
detach("package:nlme")
library(lme4)
mod1 < lmer(performance ~ time + (time  GroupID/StudentNumber), data
= dataset.long, na.action = na.omit)
However, this call results in a segfault:
*** caught segfault ***
address 0x154c3000, cause 'memory not mapped'
and a lengthy traceback. I can reproduce this error. It also occurs
when I don't load nlme before lme4. Can someone tell me what I am
doing wrong? Any help is greatly appreciated.
With best regards,
Bertolt

Dr. Bertolt Meyer
Senior research and teaching associate
Social Psychology, Institute of Psychology, University of Zurich
Binzmuehlestrasse 14, Box 15
CH8050 Zurich
Switzerland
[hidden email]
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Bertolt Meyer <bmeyer <at> sozpsy.uzh.ch> writes:
>
> Hello lmer()  users,
>
> A call to the lmer() function causes my installation of R (2.11.1 on
> Mac OS X 10.5.8) to crash and I am trying to figure out the problem.
[snip snip]
> detach("package:nlme")
> library(lme4)
>
> mod1 < lmer(performance ~ time + (time  GroupID/StudentNumber), data
> = dataset.long, na.action = na.omit)
>
> However, this call results in a segfault:
>
> *** caught segfault ***
> address 0x154c3000, cause 'memory not mapped'
>
> and a lengthy traceback. I can reproduce this error. It also occurs
> when I don't load nlme before lme4. Can someone tell me what I am
> doing wrong? Any help is greatly appreciated.
This may well be a bug in lmer. There have been a number of
fussy computational issues with the lme4 package on the Mac platform.
If it is at all possible, please (1) post the results of sessionInfo()
[which will in particular specify which version of lme4 you are using];
(2) possibly try this with the latest development version of lme4, from
Rforge, if that's feasible (it might be necessary to build the package
from source), and most importantly:
(3) create a reproducible (for others) example  most easily by
posting your data on the web somewhere, but if that isn't possible
by simulating data similar to yours (if it doesn't happen with another
data set of similar structure, that's a clue  it says it's some more
particular characteristic of your data that triggers the problem) and
(4) post to to *either* the Rsigmac or the Rsigmixedmodels list,
where the post is more likely to come to the attention of those who
can help diagnose/fix ...
good luck
Ben Bolker
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


Ben Bolker <bbolker <at> gmail.com> writes:
> Bertolt Meyer <bmeyer <at> sozpsy.uzh.ch> writes:
>
>>
>> Hello lmer()  users,
>>
>> A call to the lmer() function causes my installation of R (2.11.1 on
>> Mac OS X 10.5.8) to crash and I am trying to figure out the problem.
>
> [snip snip]
>
>> detach("package:nlme")
>> library(lme4)
>>
>> mod1 < lmer(performance ~ time + (time  GroupID/StudentNumber),
>> data
>> = dataset.long, na.action = na.omit)
>>
>> However, this call results in a segfault:
>>
>> *** caught segfault ***
>> address 0x154c3000, cause 'memory not mapped'
>>
>> and a lengthy traceback. I can reproduce this error. It also occurs
>> when I don't load nlme before lme4. Can someone tell me what I am
>> doing wrong? Any help is greatly appreciated.
>
> This may well be a bug in lmer. There have been a number of
> fussy computational issues with the lme4 package on the Mac platform.
Ben, thanks for your reply. I tried to replicate this issue with a
small clean data set on a windows machine. You can find the code for
the data frame (100 observations from my data) at the end of this
mail. Very simple: four test scores per student over time, and
students are nested in groups. On my Windows installation, lmer()
throws an error that does not seem to get caught on the Mac, resulting
in the segfault:
library(lme4)
mlmoded1.lmer < lmer(Score ~ Time + (Time  GroupID/StudentID), data
= test.data)
Error: length(f1) == length(f2) is not TRUE
Addditional Warnings:
1: In StudentID:GroupID :
numeric expression has 100 elements: only first one is used
2: In StudentID:GroupID :
numeric expression has 100 elements: only first one is used
It seems to me that I am committing a trivial error here and that I am
too blind to see it. Any ideas?
Regards,
Bertolt
> If it is at all possible, please (1) post the results of sessionInfo()
> [which will in particular specify which version of lme4 you are
> using];
> (2) possibly try this with the latest development version of lme4,
> from
> Rforge, if that's feasible (it might be necessary to build the
> package
> from source), and most importantly:
>
> (3) create a reproducible (for others) example  most easily by
> posting your data on the web somewhere, but if that isn't possible
> by simulating data similar to yours (if it doesn't happen with another
> data set of similar structure, that's a clue  it says it's some more
> particular characteristic of your data that triggers the problem) and
>
> (4) post to to *either* the Rsigmac or the Rsigmixedmodels list,
> where the post is more likely to come to the attention of those who
> can help diagnose/fix ...
>
> good luck
> Ben Bolker
>
test.data < data.frame(c(17370, 17370, 17370, 17370, 17379, 17379,
17379, 17379, 17387, 17387, 17387, 17387, 17391, 17391, 17391, 17391,
17392, 17392, 17392, 17392, 17394, 17394, 17394, 17394, 17408, 17408,
17408, 17408, 17419, 17419, 17419, 17419, 17429, 17429, 17429, 17429,
17432, 17432, 17432, 17432, 17436, 17436, 17436, 17436, 17439, 17439,
17439, 17439, 17470, 17470, 17470, 17470, 17220, 17220, 17220, 17220,
17348, 17348, 17348, 17348, 17349, 17349, 17349, 17349, 17380, 17380,
17380, 17380, 17398, 17398, 17398, 17398, 17400, 17400, 17400, 17400,
17402, 17402, 17402, 17402, 17403, 17403, 17403, 17403, 17413, 17413,
17413, 17413, 17416, 17416, 17416, 17416, 17420, 17420, 17420, 17420,
17421, 17421, 17421, 17421), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), c(1, 2, 3, 4,
1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,
3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1,
2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
1, 2, 3, 4), c(76.76, 81.83, 89.78, 92.82, 75.86, 81.84, 88.96, 92.28,
75.28, 80.68, 88.62, 92.29, 76.60, 84.59, 92.03, 94.05, 75.57, 79.94,
86.11, 90.25, 74.54, 81.42, 87.50, 90.71, 76.02, 83.68, 91.11, 94.14,
76.31, 83.76, 90.44, 94.58, 72.29, 80.51, 86.09, 90.41, 74.99, 82.28,
88.77, 92.26, 75.28, 81.92, 89.25, 92.64, 76.31, 83.93, 91.00, 94.60,
76.31, 82.44, 90.57, 95.17, 76.94, 82.21, 83.81, 85.00, 79.96, 81.92,
86.32, 90.05, 82.01, 84.81, 88.79, 93.10, 77.87, 82.94, 86.86, 90.31,
77.87, 79.64, 85.66, 86.97, 79.35, 80.44, 84.26, 83.62, 79.06, 81.56,
85.00, 87.43, 79.34, 81.47, 83.23, 86.86, 79.44, 80.37, 84.36, 89.11,
78.77, 81.02, 81.60, 87.21, 75.75, 79.35, 80.38, 86.87, 76.04, 80.57,
83.36, 86.31))
names(test.data) < c("StudentID", "GroupID", "Time", "Score")
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https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


Hi:
Let's start with the data:
> str(test.data)
'data.frame': 100 obs. of 4 variables:
$ StudentID: num 17370 17370 17370 17370 17379 ...
$ GroupID : num 1 1 1 1 1 1 1 1 1 1 ...
$ Time : num 1 2 3 4 1 2 3 4 1 2 ...
$ Score : num 76.8 81.8 89.8 92.8 75.9 ...
Both StudentID and GroupID are numeric; in the model, they would be treated
as continuous covariates rather than factors, so we need to convert:
test.data$StudentID < factor(test.data$StudentID)
test.data$GroupID < factor(test.data$GroupID)
Secondly, I believe there are some flaws in your model. After converting
your variables to factors, I ran
library(lme4)
mlmoded1.lmer < lmer(Score ~ Time + (Time  GroupID/StudentID), data =
test.data)
You have two groups, so they should be treated as a fixed effect  more
specifically, as a fixed blocking factor. The StudentIDs are certainly
nested within GroupID, and Time is measured on each StudentID, so it is a
repeated measures factor. The output of this model is
> mlmoded1.lmer
Linear mixed model fit by REML
Formula: Score ~ Time + (Time  GroupID/StudentID)
Data: test.data
AIC BIC logLik deviance REMLdev
393.1 416.5 187.5 376.9 375.1
Random effects:
Groups Name Variance Std.Dev. Corr
StudentID:GroupID (Intercept) 0.504131 0.71002
Time 0.083406 0.28880 1.000
GroupID (Intercept) 12.809567 3.57905
Time 3.897041 1.97409 1.000
Residual 1.444532 1.20189
Number of obs: 100, groups: StudentID:GroupID, 25; GroupID, 2
Fixed effects:
Estimate Std. Error t value
(Intercept) 72.803 2.552 28.530
Time 4.474 1.401 3.193
Correlation of Fixed Effects:
(Intr)
Time 0.994
The high correlations among the random effects and then among the fixed
effects suggests that the model specification may be a bit off.
The above model fits random slopes to GroupIDs and StudentIDs, along with
random intercepts, but GroupID is a betweensubject effect and should be at
the top level. Time is a withinsubject effect and StudentIDs are the
observational units. I modified the model to provide fixed effects for
GroupIDs, scalar random effects for StudentIDs and random slopes for
StudentIDs.
> mod3 < lmer(Score ~ 1 + GroupID + Time + (1  StudentID) +
+ (0 + Time  StudentID), data = test.data)
> mod3
Linear mixed model fit by REML
Formula: Score ~ 1 + GroupID + Time + (1  StudentID) + (0 + Time 
StudentID)
Data: test.data
AIC BIC logLik deviance REMLdev
430.9 446.5 209.4 418.4 418.9
Random effects:
Groups Name Variance Std.Dev.
StudentID (Intercept) 4.2186e13 6.4951e07
StudentID Time 1.8380e+00 1.3557e+00
Residual 1.6301e+00 1.2768e+00
Number of obs: 100, groups: StudentID, 25
Fixed effects:
Estimate Std. Error t value
(Intercept) 70.7705 0.4204 168.33
GroupID2 4.0248 0.5854 6.88
Time 4.5292 0.2942 15.39
Correlation of Fixed Effects:
(Intr) GrpID2
GroupID2 0.668
Time 0.264 0.000
I didn't check the quality of the fit, but on the surface it seems to be
more stable, FWIW. Perhaps one could also add a term (GroupID  StudentID),
but I don't know offhand if that would make any sense. Another issue to
consider is whether to fit by REML or ML, but that is secondary to getting
the form of the model equation right. I don't claim this as a final model,
but rather a 'restarting point'. It may well be in need of improvement, so
comments are welcome.
The confusion between subjects nested in time or vice versa has occurred
several times this week with respect to repeated measures/longitudinal
models using lmer(), so perhaps it merits a comment: subjects/experimental
units are NOT nested in time. Measurements taken on an individual at several
time points *entails* that time be nested within subject. Just saying...
This discussion may be better continued on the Rsigmixed list, so I've
cced to that group as well.
HTH,
Dennis
On Wed, Aug 25, 2010 at 1:27 AM, Bertolt Meyer < [hidden email]> wrote:
> Ben Bolker <bbolker <at> gmail.com> writes:
>
> Bertolt Meyer <bmeyer <at> sozpsy.uzh.ch> writes:
>>
>>
>>> Hello lmer()  users,
>>>
>>> A call to the lmer() function causes my installation of R (2.11.1 on
>>> Mac OS X 10.5.8) to crash and I am trying to figure out the problem.
>>>
>>
>> [snip snip]
>>
>> detach("package:nlme")
>>> library(lme4)
>>>
>>> mod1 < lmer(performance ~ time + (time  GroupID/StudentNumber), data
>>> = dataset.long, na.action = na.omit)
>>>
>>> However, this call results in a segfault:
>>>
>>> *** caught segfault ***
>>> address 0x154c3000, cause 'memory not mapped'
>>>
>>> and a lengthy traceback. I can reproduce this error. It also occurs
>>> when I don't load nlme before lme4. Can someone tell me what I am
>>> doing wrong? Any help is greatly appreciated.
>>>
>>
>> This may well be a bug in lmer. There have been a number of
>> fussy computational issues with the lme4 package on the Mac platform.
>>
>
> Ben, thanks for your reply. I tried to replicate this issue with a small
> clean data set on a windows machine. You can find the code for the data
> frame (100 observations from my data) at the end of this mail. Very simple:
> four test scores per student over time, and students are nested in groups.
> On my Windows installation, lmer() throws an error that does not seem to get
> caught on the Mac, resulting in the segfault:
>
> library(lme4)
> mlmoded1.lmer < lmer(Score ~ Time + (Time  GroupID/StudentID), data =
> test.data)
>
> Error: length(f1) == length(f2) is not TRUE
> Addditional Warnings:
> 1: In StudentID:GroupID :
> numeric expression has 100 elements: only first one is used
> 2: In StudentID:GroupID :
> numeric expression has 100 elements: only first one is used
>
> It seems to me that I am committing a trivial error here and that I am too
> blind to see it. Any ideas?
>
> Regards,
> Bertolt
>
> If it is at all possible, please (1) post the results of sessionInfo()
>> [which will in particular specify which version of lme4 you are using];
>> (2) possibly try this with the latest development version of lme4, from
>> Rforge, if that's feasible (it might be necessary to build the package
>> from source), and most importantly:
>>
>> (3) create a reproducible (for others) example  most easily by
>> posting your data on the web somewhere, but if that isn't possible
>> by simulating data similar to yours (if it doesn't happen with another
>> data set of similar structure, that's a clue  it says it's some more
>> particular characteristic of your data that triggers the problem) and
>>
>> (4) post to to *either* the Rsigmac or the Rsigmixedmodels list,
>> where the post is more likely to come to the attention of those who
>> can help diagnose/fix ...
>>
>> good luck
>> Ben Bolker
>>
>>
>
> test.data < data.frame(c(17370, 17370, 17370, 17370, 17379, 17379, 17379,
> 17379, 17387, 17387, 17387, 17387, 17391, 17391, 17391, 17391, 17392, 17392,
> 17392, 17392, 17394, 17394, 17394, 17394, 17408, 17408, 17408, 17408, 17419,
> 17419, 17419, 17419, 17429, 17429, 17429, 17429, 17432, 17432, 17432, 17432,
> 17436, 17436, 17436, 17436, 17439, 17439, 17439, 17439, 17470, 17470, 17470,
> 17470, 17220, 17220, 17220, 17220, 17348, 17348, 17348, 17348, 17349, 17349,
> 17349, 17349, 17380, 17380, 17380, 17380, 17398, 17398, 17398, 17398, 17400,
> 17400, 17400, 17400, 17402, 17402, 17402, 17402, 17403, 17403, 17403, 17403,
> 17413, 17413, 17413, 17413, 17416, 17416, 17416, 17416, 17420, 17420, 17420,
> 17420, 17421, 17421, 17421, 17421), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), c(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1,
> 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,
> 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
> 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4), c(76.76, 81.83, 89.78, 92.82, 75.86,
> 81.84, 88.96, 92.28, 75.28, 80.68, 88.62, 92.29, 76.60, 84.59, 92.03, 94.05,
> 75.57, 79.94, 86.11, 90.25, 74.54, 81.42, 87.50, 90.71, 76.02, 83.68, 91.11,
> 94.14, 76.31, 83.76, 90.44, 94.58, 72.29, 80.51, 86.09, 90.41, 74.99, 82.28,
> 88.77, 92.26, 75.28, 81.92, 89.25, 92.64, 76.31, 83.93, 91.00, 94.60, 76.31,
> 82.44, 90.57, 95.17, 76.94, 82.21, 83.81, 85.00, 79.96, 81.92, 86.32, 90.05,
> 82.01, 84.81, 88.79, 93.10, 77.87, 82.94, 86.86, 90.31, 77.87, 79.64, 85.66,
> 86.97, 79.35, 80.44, 84.26, 83.62, 79.06, 81.56, 85.00, 87.43, 79.34, 81.47,
> 83.23, 86.86, 79.44, 80.37, 84.36, 89.11, 78.77, 81.02, 81.60, 87.21, 75.75,
> 79.35, 80.38, 86.87, 76.04, 80.57, 83.36, 86.31))
>
> names(test.data) < c("StudentID", "GroupID", "Time", "Score")
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp> PLEASE do read the posting guide
> http://www.Rproject.org/postingguide.html> and provide commented, minimal, selfcontained, reproducible code.
>
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Dennis,
just wow. Thank you so much. I knew it was something trivial  in this
case the variable type of the of the grouping variables. However,
something as trivial as this should not throw a segfault IMHO. I tried
subscribing to Rsigmixed this morning, but the corresponding mail
server at the ETH's stats department seems to be down. And thank you
so much for changing the model, that is a great new starting point.
Can you recommend a good book that deals with multilevel models in
lmer() that include longitudinal data? I was not aware of the
difference between scalar random effects and random slopes and would
like to read up on that.
Again, thanks a lot.
Regards,
Bertolt
Am 25.08.2010 um 13:47 schrieb Dennis Murphy:
> Hi:
>
> Let's start with the data:
>
> > str(test.data)
> 'data.frame': 100 obs. of 4 variables:
> $ StudentID: num 17370 17370 17370 17370 17379 ...
> $ GroupID : num 1 1 1 1 1 1 1 1 1 1 ...
> $ Time : num 1 2 3 4 1 2 3 4 1 2 ...
> $ Score : num 76.8 81.8 89.8 92.8 75.9 ...
>
> Both StudentID and GroupID are numeric; in the model, they would be
> treated as continuous covariates rather than factors, so we need to
> convert:
>
> test.data$StudentID < factor(test.data$StudentID)
> test.data$GroupID < factor(test.data$GroupID)
>
> Secondly, I believe there are some flaws in your model. After
> converting your variables to factors, I ran
>
> library(lme4)
> mlmoded1.lmer < lmer(Score ~ Time + (Time  GroupID/StudentID),
> data = test.data)
>
> You have two groups, so they should be treated as a fixed effect 
> more specifically, as a fixed blocking factor. The StudentIDs are
> certainly nested within GroupID, and Time is measured on each
> StudentID, so it is a repeated measures factor. The output of this
> model is
> > mlmoded1.lmer
> Linear mixed model fit by REML
> Formula: Score ~ Time + (Time  GroupID/StudentID)
> Data: test.data
> AIC BIC logLik deviance REMLdev
> 393.1 416.5 187.5 376.9 375.1
> Random effects:
> Groups Name Variance Std.Dev. Corr
> StudentID:GroupID (Intercept) 0.504131 0.71002
> Time 0.083406 0.28880 1.000
> GroupID (Intercept) 12.809567 3.57905
> Time 3.897041 1.97409 1.000
> Residual 1.444532 1.20189
> Number of obs: 100, groups: StudentID:GroupID, 25; GroupID, 2
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 72.803 2.552 28.530
> Time 4.474 1.401 3.193
>
> Correlation of Fixed Effects:
> (Intr)
> Time 0.994
>
> The high correlations among the random effects and then among the
> fixed effects suggests that the model specification may be a bit off.
>
> The above model fits random slopes to GroupIDs and StudentIDs, along
> with random intercepts, but GroupID is a betweensubject effect and
> should be at the top level. Time is a withinsubject effect and
> StudentIDs are the observational units. I modified the model to
> provide fixed effects for GroupIDs, scalar random effects for
> StudentIDs and random slopes for StudentIDs.
>
> > mod3 < lmer(Score ~ 1 + GroupID + Time + (1  StudentID) +
> + (0 + Time  StudentID), data = test.data)
> > mod3
> Linear mixed model fit by REML
> Formula: Score ~ 1 + GroupID + Time + (1  StudentID) + (0 + Time 
> StudentID)
> Data: test.data
> AIC BIC logLik deviance REMLdev
> 430.9 446.5 209.4 418.4 418.9
> Random effects:
> Groups Name Variance Std.Dev.
> StudentID (Intercept) 4.2186e13 6.4951e07
> StudentID Time 1.8380e+00 1.3557e+00
> Residual 1.6301e+00 1.2768e+00
> Number of obs: 100, groups: StudentID, 25
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 70.7705 0.4204 168.33
> GroupID2 4.0248 0.5854 6.88
> Time 4.5292 0.2942 15.39
>
> Correlation of Fixed Effects:
> (Intr) GrpID2
> GroupID2 0.668
> Time 0.264 0.000
>
> I didn't check the quality of the fit, but on the surface it seems
> to be more stable, FWIW. Perhaps one could also add a term (GroupID
>  StudentID), but I don't know offhand if that would make any sense.
> Another issue to consider is whether to fit by REML or ML, but that
> is secondary to getting the form of the model equation right. I
> don't claim this as a final model, but rather a 'restarting point'.
> It may well be in need of improvement, so comments are welcome.
>
> The confusion between subjects nested in time or vice versa has
> occurred several times this week with respect to repeated measures/
> longitudinal models using lmer(), so perhaps it merits a comment:
> subjects/experimental units are NOT nested in time. Measurements
> taken on an individual at several time points *entails* that time be
> nested within subject. Just saying...
>
> This discussion may be better continued on the Rsigmixed list, so
> I've cced to that group as well.
>
> HTH,
> Dennis
>
> On Wed, Aug 25, 2010 at 1:27 AM, Bertolt Meyer
> < [hidden email]> wrote:
> Ben Bolker <bbolker <at> gmail.com> writes:
>
> Bertolt Meyer <bmeyer <at> sozpsy.uzh.ch> writes:
>
>
> Hello lmer()  users,
>
> A call to the lmer() function causes my installation of R (2.11.1 on
> Mac OS X 10.5.8) to crash and I am trying to figure out the problem.
>
> [snip snip]
>
> detach("package:nlme")
> library(lme4)
>
> mod1 < lmer(performance ~ time + (time  GroupID/StudentNumber), data
> = dataset.long, na.action = na.omit)
>
> However, this call results in a segfault:
>
> *** caught segfault ***
> address 0x154c3000, cause 'memory not mapped'
>
> and a lengthy traceback. I can reproduce this error. It also occurs
> when I don't load nlme before lme4. Can someone tell me what I am
> doing wrong? Any help is greatly appreciated.
>
> This may well be a bug in lmer. There have been a number of
> fussy computational issues with the lme4 package on the Mac platform.
>
> Ben, thanks for your reply. I tried to replicate this issue with a
> small clean data set on a windows machine. You can find the code for
> the data frame (100 observations from my data) at the end of this
> mail. Very simple: four test scores per student over time, and
> students are nested in groups. On my Windows installation, lmer()
> throws an error that does not seem to get caught on the Mac,
> resulting in the segfault:
>
> library(lme4)
> mlmoded1.lmer < lmer(Score ~ Time + (Time  GroupID/StudentID),
> data = test.data)
>
> Error: length(f1) == length(f2) is not TRUE
> Addditional Warnings:
> 1: In StudentID:GroupID :
> numeric expression has 100 elements: only first one is used
> 2: In StudentID:GroupID :
> numeric expression has 100 elements: only first one is used
>
> It seems to me that I am committing a trivial error here and that I
> am too blind to see it. Any ideas?
>
> Regards,
> Bertolt
>
> If it is at all possible, please (1) post the results of sessionInfo()
> [which will in particular specify which version of lme4 you are
> using];
> (2) possibly try this with the latest development version of lme4,
> from
> Rforge, if that's feasible (it might be necessary to build the
> package
> from source), and most importantly:
>
> (3) create a reproducible (for others) example  most easily by
> posting your data on the web somewhere, but if that isn't possible
> by simulating data similar to yours (if it doesn't happen with another
> data set of similar structure, that's a clue  it says it's some more
> particular characteristic of your data that triggers the problem) and
>
> (4) post to to *either* the Rsigmac or the Rsigmixedmodels list,
> where the post is more likely to come to the attention of those who
> can help diagnose/fix ...
>
> good luck
> Ben Bolker
>
>
>
> test.data < data.frame(c(17370, 17370, 17370, 17370, 17379, 17379,
> 17379, 17379, 17387, 17387, 17387, 17387, 17391, 17391, 17391,
> 17391, 17392, 17392, 17392, 17392, 17394, 17394, 17394, 17394,
> 17408, 17408, 17408, 17408, 17419, 17419, 17419, 17419, 17429,
> 17429, 17429, 17429, 17432, 17432, 17432, 17432, 17436, 17436,
> 17436, 17436, 17439, 17439, 17439, 17439, 17470, 17470, 17470,
> 17470, 17220, 17220, 17220, 17220, 17348, 17348, 17348, 17348,
> 17349, 17349, 17349, 17349, 17380, 17380, 17380, 17380, 17398,
> 17398, 17398, 17398, 17400, 17400, 17400, 17400, 17402, 17402,
> 17402, 17402, 17403, 17403, 17403, 17403, 17413, 17413, 17413,
> 17413, 17416, 17416, 17416, 17416, 17420, 17420, 17420, 17420,
> 17421, 17421, 17421, 17421), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), c(1, 2, 3,
> 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,
> 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1,
> 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
> 4, 1, 2, 3, 4), c(76.76, 81.83, 89.78, 92.82, 75.86, 81.84, 88.96,
> 92.28, 75.28, 80.68, 88.62, 92.29, 76.60, 84.59, 92.03, 94.05,
> 75.57, 79.94, 86.11, 90.25, 74.54, 81.42, 87.50, 90.71, 76.02,
> 83.68, 91.11, 94.14, 76.31, 83.76, 90.44, 94.58, 72.29, 80.51,
> 86.09, 90.41, 74.99, 82.28, 88.77, 92.26, 75.28, 81.92, 89.25,
> 92.64, 76.31, 83.93, 91.00, 94.60, 76.31, 82.44, 90.57, 95.17,
> 76.94, 82.21, 83.81, 85.00, 79.96, 81.92, 86.32, 90.05, 82.01,
> 84.81, 88.79, 93.10, 77.87, 82.94, 86.86, 90.31, 77.87, 79.64,
> 85.66, 86.97, 79.35, 80.44, 84.26, 83.62, 79.06, 81.56, 85.00,
> 87.43, 79.34, 81.47, 83.23, 86.86, 79.44, 80.37, 84.36, 89.11,
> 78.77, 81.02, 81.60, 87.21, 75.75, 79.35, 80.38, 86.87, 76.04,
> 80.57, 83.36, 86.31))
>
> names(test.data) < c("StudentID", "GroupID", "Time", "Score")
>
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