[R-sig-ME] Within group estimate of autocorrelation

Mon Sep 22 20:39:12 CEST 2014

Hi

I have a model which fits an corAR1 autocorrelation structure and the code tells the model that time is nested within individual:

fit0<-lme(Respone~1,
random=~1|indiv,
correlation=corAR1(form=~time|indiv),
data=Data2, method = "REML")

>From this I get a Phi single estimate of the autocorrelation.  However  I want to have an estimate of the autocorrelation for each individual.  I have checked but can not find any code to extract this value.

So my first question is: Is it possible to extract an estimate of autocorrelation per individual or does the model not save/calculate this?
And second, if it isn't possible, I wondered if there is any way to use the weights function to group individuals.

If I add the code:  weights=varIdent(form=~1|group), I can fit multiple residual variance terms so I wondered if this could be used to estimate a Phi value per group, by somehow structuring the model so it fits an autocorrelation for each residual variance group?

The solution does not need to be lme necessarily - I’m open to any suggestions!

Many Thanks

Sam

Dr Samantha Patrick
Research Fellow
Biosciences QU116
Francis Close Hall Campus
University of Gloucestershire
Cheltenham, GL50 4AZ, UK

Research Associate: OxNav, University of Oxford

******From 1st August - 14th November 2014 I will be
based in Montréal, which is 5 hours behind GMT  ******

Tel: 07740 472 719
Skype: sammy_patrick
https://sites.google.com/site/samanthacpatrick/

From: Alex Whitworth<mailto:whitworth.alex at gmail.com>
Sent: ‎Thursday‎, ‎18‎ ‎September‎ ‎2014 ‎11‎:‎21
To: Ben Bolker<mailto:bbolker at gmail.com>
Cc: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>

I've tried installing from github on my personal computer as well (in case
there was something with my work network causing a problem). I ran into the
same error. I am able to install other github packages--for example, my
(admittedly very small) package on nongaussian mixture
modeling: devtools::install_github("emclustr","crossfitAL").

I'll re-attempt the binary today--currently having some problems
downloading it.

Regarding question #2 (my hacked function), I completely agree that a
proliferation of predict.merMod functions is undesirable. the random
component of my model is as follows

lmer(... (0 + <logical> + <factor> |subject/school), data= ...)

The issue is selecting the appropriate columns of the random effects
data-frame { ranef(object)[[1]] } when the intercept term is
suppressed--but allowing flexibility in the function when the intercept is
not suppressed. It's made more difficult by the factor variable having 3-4
levels.

Alex

Alex Whitworth
whitworth.alex at gmail.com
(c) 828.429.7478

On Wed, Sep 17, 2014 at 1:37 PM, Ben Bolker <bbolker at gmail.com> wrote:

>
>
> On Wed, Sep 17, 2014 at 3:22 PM, Alex Whitworth <whitworth.alex at gmail.com>
> wrote:
>
>> Dr Bolker,
>>
>> I tried installing the development / github version of lme4 today and ran
>> into an error. R system messages copied below. Any help would be
>> appreciated.
>>
>
>   I'm cc'ing this to r-sig-mixed-models in case anyone has an idea.
>   In the meantime,
> * I'm also posting a Windows binary of the latest development version
> (today's github version), as built by CRAN's win-builder, to
> http://lme4.r-forge.r-project.org/repos/ ; it will take up to 24 hours to
> show up there
> * I've also put a version at
> http://ms.mcmaster.ca/~bolker/R/bin/windows/contrib/3.1/ , available
> immediately
>
>   You can install this under R 3.1.* with an appropriate
> install.packages(...,repos=...) incantation, or download the .zip file and
> install it locally.
>
>
>>
>> On a second note, I'm still working on my "hacked" version of the predict
>> function predict.merMod2() . I've solved one of the issues I was running
>> into but am still working on an issue related to the random effects for
>> grouping variables that are of type factor.
>>
>
>   (What other kinds of grouping variables are there ...  ?)
>
>
>
>> I hope to be able to solve this / put the solution on stackoverflow in
>> the near-ish future.
>>
>
>   I'm hoping that the predict.merMod in the development version will solve
> all your problems, or that if it doesn't you can create a small
> reproducible example and I can fix it centrally.  Provide that you're not
> trying to do something really weird/baroque, it makes more sense to have
> the package Just Work than to have a proliferation of individual
> fixes/workarounds.
>
>
>>
>>
>>
>> -------------------------------------------------------------------------------------------------------------------
>> Restarting R session...
>>
>> > devtools::install_github("lme4","lme4")
>> Installing github repo lme4/master from lme4
>> Downloading master.zip from
>> https://github.com/lme4/lme4/archive/master.zip
>> Installing package from
>> C:\Users\alewit\AppData\Local\Temp\RtmpYHOfgM/master.zip
>> Installing lme4
>> "C:/PROGRA~1/R/R-31~1.1/bin/x64/R" --vanilla CMD build  \
>>
>> "C:\Users\alewit\AppData\Local\Temp\RtmpYHOfgM\devtools138845a711f6\lme4-master"
>> --no-manual  \
>>   --no-resave-data
>>
>> * checking for file
>> 'C:\Users\alewit\AppData\Local\Temp\RtmpYHOfgM\devtools138845a711f6\lme4-master/DESCRIPTION'
>> ... OK
>> * preparing 'lme4':
>> * checking DESCRIPTION meta-information ... OK
>> * cleaning src
>> Warning in cleanup_pkg(pkgdir, Log) :
>>   unable to run 'make clean' in 'src'
>> * installing the package to build vignettes
>> Warning: running command '"C:/PROGRA~1/R/R-31~1.1/bin/x64/Rcmd.exe"
>> INSTALL -l "C:\Users\alewit\AppData\Local\Temp\Rtmp2ppmfo\Rinst13c46423bb3"
>> --no-multiarch
>> "C:/Users/alewit/AppData/Local/Temp/Rtmp2ppmfo/Rbuild13c45c0e2b4e/lme4"'
>> had status 1
>>       -----------------------------------
>> * installing *source* package 'lme4' ...
>> ** libs
>> Warning: running command 'make -f "Makevars.win" -f
>> "C:/PROGRA~1/R/R-31~1.1/etc/x64/Makeconf" -f
>> "C:/PROGRA~1/R/R-31~1.1/share/make/winshlib.mk"
>> SHLIB_LDFLAGS='$(SHLIB_CXXLDFLAGS)' SHLIB_LD='$(SHLIB_CXXLD)'
>> SHLIB="lme4.dll" WIN=64 TCLBIN=64 OBJECTS="external.o glmFamily.o
>> mcmcsamp.o optimizer.o predModule.o respModule.o"' had status 127
>> ERROR: compilation failed for package 'lme4'
>> * removing
>> 'C:/Users/alewit/AppData/Local/Temp/Rtmp2ppmfo/Rinst13c46423bb3/lme4'
>>       -----------------------------------
>> ERROR: package installation failed
>> Error: Command failed (1)
>>
>
>   I'm not  sure what's going on here.  Googling for "status 127" suggests
> that it's a pretty generic error code, so it doesn't give a lot of clues.
> Is it possible you're having permissions errors (hinted at by "unable to
> run 'make clean' in 'src'"?  What happens if you try disabling vignette
> building using build_vignettes=FALSE in your install_github() ?
>
>   Are you able to install other packages from github?
>
>
>> Alex Whitworth
>> whitworth.alex at gmail.com
>> (c) 828.429.7478
>>
>> On Thu, Aug 28, 2014 at 4:13 PM, Ben Bolker <bbolker at gmail.com> wrote:
>>
>>> On 14-08-28 06:46 PM, Alex Whitworth wrote:
>>> > Dr. Bolker,
>>> >
>>> > Could you please take a look at the function I have posted on
>>> > StackOverflow
>>> > <
>>> http://stackoverflow.com/questions/25538199/design-matrix-for-mlm-from-librarylme4-with-fixed-and-random-effects
>>> >?
>>> > I believe that it is working correctly and would appreciate your
>>> review.
>>> >
>>> > Thanks,
>>> >
>>> > Alex Whitworth
>>> > whitworth.alex at gmail.com <mailto:whitworth.alex at gmail.com>
>>> >
>>>
>>>   I saw that.  I will look at it if I have a chance.  I am
>>> simultaneously impressed by your incentive and ability to solve your own
>>> problems and a little bit frustrated at our inability to communicate
>>> very effectively: I think your problems could probably be fixed by a
>>> fairly minor adjustment to predict.merMod , which is likely better
>>> tested and handles a wider range of cases.  On the other hand, if you
>>> needed the solution right away ...  ( a workaround that occurred to me
>>> would be to make predictions on an augmented set of new data that
>>> included all of the factor levels, then throw away the ones you don't
>>> want).
>>>
>>>   If you want to test your function thoroughly, you can try the tests
>>> outlined in
>>>
>>> https://github.com/lme4/lme4/blob/master/tests/predsim.R
>>> https://github.com/lme4/lme4/blob/master/inst/tests/test-methods.R
>>> (search in this one for context("predict"))
>>>
>>>  By the way, getting standard errors of predictions that account
>>> properly for uncertainty in the random-effects parameters is (alas) a
>>> considerably harder problem ...
>>>
>>>   cheers
>>>     Ben Bolker
>>>
>>>
>>
>

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