[R-sig-ME] Any package for best subset selection for, random effects model
Antoine Tremblay
trea26 at gmail.com
Mon Feb 13 21:45:06 CET 2012
Maybe function ffRanefLMER.fnc from package LMERConvenienceFunctions???
Antoine Tremblay, PhD
NeuroCognitive Imaging Laboratory
Dalhousie University
Halifax, NS B3H 3J5,
Canada
Tel.: (902) 494-1911
eom
On 12-02-13 04:30 PM, r-sig-mixed-models-request at r-project.org wrote:
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> 1. Re: Any package for best subset selection for random effects
> model (Thackeray, Stephen J.)
> 2. Re: R-sig-mixed-models Digest, Vol 62, Issue 29
> (anthony.sealey at utoronto.ca)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 13 Feb 2012 20:18:38 +0000
> From: "Thackeray, Stephen J."<sjtr at ceh.ac.uk>
> To: Tao Zhang<zt020200 at gmail.com>, "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org>
> Subject: Re: [R-sig-ME] Any package for best subset selection for
> random effects model
> Message-ID:
> <42AFDDFA3288A141B63C93EE7F138E97216D3BADF7 at nerckwmb1.ad.nerc.ac.uk>
> Content-Type: text/plain; charset="us-ascii"
>
> Hello Tao,
>
>> From your question, I am unsure of quite what you want. If you are interested in determining from a global model (with all fixed effects included) the model(s) with the most optimal subset of these fixed effects then you could try the dredge function in the MuMIn package. This will accept lme and lmer mixed effects models...
>
> All the best
>
> Steve
>
>
>
> ________________________________________
> From: r-sig-mixed-models-bounces at r-project.org [r-sig-mixed-models-bounces at r-project.org] On Behalf Of Tao Zhang [zt020200 at gmail.com]
> Sent: 13 February 2012 17:22
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] Any package for best subset selection for random effects model
>
> Hi Pros,
> I know leaps() computes the best subset selection for linear model,
> and
> the bestglm() computes the best subset selection for generalized linear
> model. Is there any package for best subset selection on random effects
> model, or mixed effects model?
>
> Thank you!
>
> Tao
>
> [[alternative HTML version deleted]]
>
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> ------------------------------
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> Message: 2
> Date: Mon, 13 Feb 2012 20:40:09 +0000
> From: anthony.sealey at utoronto.ca
> To: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] R-sig-mixed-models Digest, Vol 62, Issue 29
> Message-ID:
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> 9sbnopoi
> -----Original Message-----
> From: r-sig-mixed-models-request at r-project.org
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> Date: Mon, 13 Feb 2012 20:07:11
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> Subject: R-sig-mixed-models Digest, Vol 62, Issue 29
>
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> Today's Topics:
>
> 1. Comparing against a negative control in an LMM (Masca, Nick)
> 2. MCMCglmm with cross-classified random effects (Agostino Moro)
> 3. Re: MCMCglmm with cross-classified random effects
> (Jarrod Hadfield)
> 4. Re: Considerable discrepancies between fixed and random
> effect estimates of lme4 (glmer) and glmmADMB (Ben Bolker)
> 5. Any package for best subset selection for random effects
> model (Tao Zhang)
> 6. Interpretation of nonlinear mixed-effects modeling results
> (Gang Chen)
> 7. Re: Considerable discrepancies between fixed and random
> effect estimates of lme4 (glmer) and glmmADMB (Adam Smith)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 13 Feb 2012 12:33:42 +0000
> From: "Masca, Nick"<Nick.Masca at effem.com>
> To: "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org>
> Subject: [R-sig-ME] Comparing against a negative control in an LMM
> Message-ID:
> <8295A4D50D4C644CAC4323DD070D9597106F2D at 034-CH1MPN1-014.034d.mgd.msft.net>
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> Hi all,
>
> I have a problem based on a colleague's experiment that I've been asked to analyse, which is more of a general mixed modelling issue rather than specifically an R issue, and I would be extremely grateful for any help that any readers of this list can provide.
>
> An experiment was conducted in which the aim was to compare 3 concentrations of 2 active treatments (i.e. 6 active treatments in total) to a negative control. Three batches of each of the actives have been tested, and 3 reps tested for each batch. In contrast, 20 replicates have been taken of the negative control - but, by definition, there is no "batch" for this treatment.
>
> Here is some code to reproduce the experimental design:
>
> Treat<- factor(c(rep("NC", 20), rep("A", 27), rep("B", 27)))
> Conc<-factor(c(rep(1, 20), rep(1:3, each=9), rep(1:3, each=9)))
> Batch<-factor(c(rep(1, 20), rep( rep(1:3, each=3), 6)))
> Treatment<-factor(Treat:Conc) #specify new treatment variable (so don't attempt to estimate Conc. 2&3 for NC)
>
> I originally planned to analyses these data in a LMM, with Treat*Conc as a 7 level fixed effect (i.e. 3*2 actives + control), and with Treat:Conc:Batch as random. The following code simulates my response variable assuming this model:
>
> Resp<- rep(9, 74) + #simulate intercept
> c( rep(rnorm(1, 0, sd=2.5), 20)^2, rep(rnorm(18, 0, sd=2.5), each=3)^2) + #simulate treat.conc.batch variance
> rep(rnorm(74, 0, sd=.2)^2) + #simulate residual variance
> c(rep(0,20), rep(c(-4, 0,0,-4, 0,0), each= 9)) #simulate fixed effects
> Data<-data.frame(Treatment, Conc, Batch, Resp)
>
> While this code models the data using lme4:
> Mod<-lmer(Resp ~ Treatment + (1|Treatment:Batch), data=Data)
>
> I can now obtain and plot treatment means/CIs using glht in the multcomp package:
> library(multcomp)
> Mean.mat<-diag(rep(1,7))
> Mean.mat[,1]<-rep(1,7)
> rownames(Mean.mat)<-levels(Data$Treatment)
> Est.means<-glht(Mod, Mean.mat)
> plot(Est.means)
>
> Hopefully from the above plot you can see what my issue is. The negative control, which I want to compare everything against, has by far the least precision around its estimate, despite the data for the control hardly varying at all. This happens because the greatest source of variability in the model (by far) is the variability between batches, but different batches of the negative control don't exist. As such, I'm not sure that this is a fair way to model the data, because the negative control is unfairly penalised by the variability between the batches of the other treatments.
>
> I imagine that this kind of problem isn't particularly uncommon, but it's the first time I've had to deal with something like this myself. The only potential solution I've come up with so far is to scrap the negative control from the model, and simply subtract the negative control's mean "count" from all other values (either by specifying this mean as an offset or by subtracting it from all data-points). But this will probably give "anti-conservative" results, as it would assume the mean for the negative control doesn't vary.
>
> I would be extremely grateful if anyone would care to share their thoughts on possible solutions to this problem - and whether anyone has dealt with this kind of issue before. I feel that I may well be missing something obvious - but can't see at the moment how else to get around it!
>
> Many thanks for any help you can provide.
>
> Cheers,
>
> Nick
>
>
>
>
>
> [[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> Message: 2
> Date: Mon, 13 Feb 2012 15:02:20 +0000
> From: Agostino Moro<agostino.moro99 at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] MCMCglmm with cross-classified random effects
> Message-ID:
> <CAMS_pxvdSZVhe_qSFgqsnkMofyTGxL6eLAY4g114VzS=k9HFpA at mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Dear R-users,
>
> I would like to fit ?a glmm with cross-classified random effects with
> the function MCMCglmm. Something along the lines:
>
> model1<-MCMCglmm(response~pred1, random=~re1+re2, data=data)
>
> where re1 and re2 should be crossed random effects. I was wondering
> whether you could tell me specifying cross-classified random effects
> in MCMCglmm requires a particular syntax? Are there any examples
> somewhere? I have had a look at the manual and the package vignette,
> but I have not been able to find any examples relevant to what I want
> to do.
>
> Thanks,
>
> Agostino
>
>
>
> ------------------------------
>
> Message: 3
> Date: Mon, 13 Feb 2012 15:19:07 +0000
> From: Jarrod Hadfield<j.hadfield at ed.ac.uk>
> To: Agostino Moro<agostino.moro99 at gmail.com>
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] MCMCglmm with cross-classified random effects
> Message-ID:<20120213151907.57957fqy2tmlxcg8 at www.staffmail.ed.ac.uk>
> Content-Type: text/plain; charset=ISO-8859-1; DelSp="Yes";
> format="flowed"
>
> Hi,
>
> As long as the levels of re1 and re2 are uniquely labelled any cross
> classification will be dealt with appropriately.
>
> Cheers,
>
> Jarrod
>
>
> Quoting Agostino Moro<agostino.moro99 at gmail.com> on Mon, 13 Feb 2012
> 15:02:20 +0000:
>
>> Dear R-users,
>>
>> I would like to fit ?a glmm with cross-classified random effects with
>> the function MCMCglmm. Something along the lines:
>>
>> model1<-MCMCglmm(response~pred1, random=~re1+re2, data=data)
>>
>> where re1 and re2 should be crossed random effects. I was wondering
>> whether you could tell me specifying cross-classified random effects
>> in MCMCglmm requires a particular syntax? Are there any examples
>> somewhere? I have had a look at the manual and the package vignette,
>> but I have not been able to find any examples relevant to what I want
>> to do.
>>
>> Thanks,
>>
>> Agostino
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
>
>
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