problems with allocate memory

Hi Douglas,
Ok, those are not the expected beautiful news :'(. If I understand it right Andrews implementation needs your check first before it is usably with lme4.

And please one more questions what would be a rough time estimation for a new R-core with 64-bit indices for atomic R vectors?

Thank you very much for your help and suggestions.
All the Best
Paul


-------- Original-Nachricht --------

Datum: Wed, 21 Dec 2011 17:10:49 -0600
Von: Douglas Bates <bates at stat.wisc.edu>
An: cumuluss at web.de
CC: r-sig-mixed-models at r-project.org, a.r.runnalls at kent.ac.uk
Betreff: Re: [R-sig-ME] problems with allocate memory

On Wed, Dec 21, 2011 at 5:03 PM,? <cumuluss at web.de> wrote:

Hi Douglas,

thank you for your reply. But it sounds not that good for me. Could you

please suggest me something what I could do more or maybe different. You
said: There are no simple solutions at present. Is there a complicated
available which I could try?

In the third part of your answer where you mentioned Andrew Runnalls and

the ?reimplementing of R? Could this also be helpful for my non
fitting models problem or is this for opening the model results issue only?

By "no easy solutions" I mean that I can't think of any approach that
doesn't involve reimplementing the code from scratch, which would
definitely take a long time.? Consider how long we have been working
on getting a 1.0 version of lme4 :-)

I am not sure if Andrew's CXXR implementation of R would be more
effective or not.? I think it has a better garbage collection scheme
but I haven't tried it and I don't know if lme4 would build in that
system.? I have an item on the "ToDo" list to try it but I have a lot
of items on the "ToDo" list.

Basically you will need to use a simpler model or fit to a sample of
your data or wait for R-core to determine if it is possible to use
64-bit indices for atomic R vectors.

-------- Original-Nachricht --------

Datum: Wed, 21 Dec 2011 15:47:36 -0600
Von: Douglas Bates <bates at stat.wisc.edu>
An: cumuluss at web.de
CC: r-sig-mixed-models at r-project.org, "a.r.runnalls"

<a.r.runnalls at kent.ac.uk>

Betreff: Re: [R-sig-ME] problems with allocate memory

On Tue, Dec 20, 2011 at 5:25 PM,? <cumuluss at web.de> wrote:

Hi Douglas,

The variable?d? has about 710 levels.

For your other request I tried to fit the suggested model but it was

not

possible. I tried it with different approaches, first without any
interactions and non nonlinear term. It fitted. The object size was

about 731571664

bytes. Then I successive made the model more complex. With one two way
interaction, with one three way interaction or with the nonlinear term

it was

slightly the same as before. With five two way interaction always with

the

nonlinear term the object size went up to 1075643424 bytes. With one
additional two way interaction the model won?t fit anymore with the

known

error.

Which is an indication that the fixed-effects model matrix is getting
to be too large.? There are no simple solutions at present.? You may
find that some packages allow you to fit such large models by working
with horizonal chunks of the data and accumulating the result but
extending those to GLMMs would be decidedly non-trivial.

Perhaps another hint: Yesterday I attempted to fit a much simpler

model

with lmer, just to see if this works. (mfit=lmer(gr.b ~ f.ag + f.se +

o.se

+ diff + exp.r + kl + (1|f)+(1|o), data=c.data, family=binomial)). It
fitted but I could not open mfit. Trying to see only the coefficients

also did

not work. I saved the image and this one is unfamiliar huge about 1.7

GB.

The problem there is that the implicit print(mfit) (which is what I
imagine you mean when you say "could not open") ends up taking copies
of the whole object, which will eat up all your memory.? Development
versions of lme4 may eventually help with that.

By the way: After reloading the image some interesting things

happened.

An error occurred: slot coefs are not an S4 object. It seems to me that

it

is not possible to save the model results in an R image. Is that right?

It should be possible to save and load such an object but there is
always the problem that when you have an object that is a sizable
fraction of the total available memory then you can get bitten if
something behind the scenes happens to take a copy at some point in
the calculation.? The original design in R for keeping track of when a
copy must be made is not the greatest and, as a result, R is somewhat
conservative when deciding whether or not to copy an object.? Getting
around that limitation would mean reimplementing R, more-or-less from
scratch and Andrew Runnalls is the only person I know who is willing
to embark on that.

-------- Original-Nachricht --------

Datum: Tue, 20 Dec 2011 09:23:37 -0600
Von: Douglas Bates <bates at stat.wisc.edu>
An: cumuluss at web.de
CC: r-sig-mixed-models at r-project.org
Betreff: Re: [R-sig-ME] problems with allocate memory

On Mon, Dec 19, 2011 at 5:54 PM,? <cumuluss at web.de> wrote:

Hi Douglas,

thanky you for your reply. This is "mydata"

'data.frame':?? 3909896 obs. of? 19 variables:
? $ gr.b??????????? : int? 0 0 0 0 0 0 0 0 0 0 ...
? $ o.ag????? : num? -0.651 -0.651 -0.651 -0.651 -0.651 ...
? $ o.rar????? : num? -0.935 -0.935 -0.935 -0.935 -0.935 ...
? $ si?????? : num? 0.299 0.299 0.299 0.299 0.299 ...
? $ f.ag?????? : num? -1.25 -1.36 -1.33 -1.26 -1.21 ...
? $ f.se?????? : Factor w/ 2 levels "F","M": 1 2 1 2 2 2 1 1

1

1

...

? $ o.se?????? : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1

1

1

...

? $ diff??????? : num? -0.536 -0.514 -0.521 -0.534 -0.545

...

? $ exp.r?????????? : num? -0.168 -0.168 -0.163 -0.168

-0.168

...

? $ f.rar????? : num? -0.911 0.215 1.224 -1.086 1.107 ...
? $ f.si: num? 1.0008 1.1583 0.0561 -0.4163 0.371 ...
? $ kl????????????? : Factor w/ 3 levels

"mat","nonkin",..:

1 2 2 2 3 2 1 2 2 2 ...

? $ sn????????? : Factor w/ 2 levels "BS","MS": 1 1 1 1 1

1

1 1

1 1 ...

? $ MP_y_n????????? : Factor w/ 2 levels "0","1": 2 2 2 2

2

2 2

2 2 2 ...

? $ ratio?????? : num? -0.0506 -0.0506 -0.0506 -0.0506

-0.0506

...

? $ f?????????? : Factor w/ 55 levels

"0A0","0A1","0A2",..:

1

6 7 8 9 10 11 13 15 16 ...

? $ o?????????? : Factor w/ 552 levels

"","00T","00Z",..: 2

2

2 2 2 2 2 2 2 2 ...

? $ d??????????? : int? 9099 9099 9099 9099 9099 9099

9099

9099 9099 9099 ...

? $ MP????????????? : num? 6 6 6 6 5 4 6 6 6 6 ...


formula for the model:
mfit=lmer(gr.b ~ o.ag + o.rar + si + ((f.ag + I(f.ag^2)) * (f.se *

(o.se

+ diff + exp.r + f.rar + f.si + kl + sn + MP_y_n + ratio))) +
(1|f)+(1|o)+(1|d) + offset(log(MP)), data=c.data, family=binomial)

I hope this is what you want to see. Thank you for your help.

My guess is that the problem is with creating the fixed-effects

model

matrix, of which there could be several copies created during the
evaluation and optimization of the deviance.

Just as a test, could you fit the model for the fixed-effects only
using glm and check on what the size of the model matrix is?
Something like

glm1 <- glm(gr.b ~ o.ag + o.rar + si + ((f.ag + I(f.ag^2)) * (f.se *
(o.se + diff + exp.r + f.rar + f.si + kl + sn + MP_y_n + ratio))) +
offset(log(MP)), data=c.data, family=binomial)
object.size(model.matrix(glm1)

Also, could you convert 'd' to a factor and run str again so we can
learn how many levels there are?? Either that or send the result of

length(unique(mydata$d))

-------- Original-Nachricht --------

Datum: Mon, 19 Dec 2011 14:50:06 -0600
Von: Douglas Bates <bates at stat.wisc.edu>
An: cumuluss at web.de
CC: r-sig-mixed-models at r-project.org
Betreff: Re: [R-sig-ME] problems with allocate memory

On Sun, Dec 18, 2011 at 3:17 PM,? <cumuluss at web.de> wrote:

Hi to everyone,

I have been trying to fit a glmm with a binomial error

structure.

My

model is a little bit complex. I have 8 continuous predictor

variables

one of

them as nonlinear term, 5 categorical predictor variables with

some

three-way interactions between them. Additional I have 3 random

effects

and one

offset variable in the model. Number of obs is greater than

3million.

I?m working with the latest version of R 2.14.0 on a 64 bit

windows

system with 8Gb ram.

Everything I tried (reducing model complexity, different 64bit

PC

with

even more memory) nothing leads to a fitted model, always the

Error

occurs:

cannot allocate vector of size 2GB.

Is there anything I can do? I would be very grateful for any

commentary.

You probably have multiple copies of some large objects hanging
around.? Can you send us the output of

str(myData)

where 'myData' is the name of the model frame containing the data

you

are using and the formula for the model you are trying to fit?