[R-sig-ME] R-sig-mixed-models Digest, Vol 70, Issue 19
trea26 at gmail.com
Sun Oct 14 20:57:31 CEST 2012
I don't know how this was sent to the R mailing list???
Antoine Tremblay, PhD
NeuroCognitive Imaging Laboratory
Halifax, NS B3H 4R2,
Tel.: (902) 494-1911
On Fri 12 Oct 2012 07:00:01 AM ADT,
r-sig-mixed-models-request at r-project.org wrote:
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> Today's Topics:
> 1. Re: random as fixed effect (Andrew Robinson)
> Message: 1
> Date: Fri, 12 Oct 2012 10:43:20 +1100
> From: Andrew Robinson <A.Robinson at ms.unimelb.edu.au>
> To: John Maindonald <john.maindonald at anu.edu.au>
> Cc: "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org>, Ben Bolker <bbolker at gmail.com>
> Subject: Re: [R-sig-ME] random as fixed effect
> Message-ID: <20121011234320.GV546 at ms.unimelb.edu.au>
> Content-Type: text/plain; charset=iso-8859-1
> On Fri, Oct 12, 2012 at 09:46:58AM +1100, John Maindonald wrote:
>> 1. "we want to make inferences about the population":
>> Even making year a random effect is not really enough. We are
>> dealing with a time series, and modelling it as a random effect is a
>> weak concession to that issue. If one does nonetheless fit year as
>> a fixed effect, one should at least examine the results for the
>> separate years separately, and check on the extent to which they
>> point in the same direction. Published use of the analysis should
>> acknowledge the consequent uncertainty.
>> Note however that for certain types of balanced models, the
>> estimates of treatment effects will be the same irrespective of
>> whether one fits years as random or fixed. The model is not
>> allowing for a year by treatment interaction, just as the standard
>> form of analysis of block designs does not and cannot allow for a
>> block x treatment interaction.
>> 2. "statistical grounds (the variance is extremely poorly
>> determined)": but of course ignoring this component of variance, if
>> it does affect treatment or other estimates, does not cause it to go
> I echo John's concern. I would argue that this component of variance
> will always affect interval estimates, and it should not be ignored.
> I feel uneasy about converting random effects into fixed effects
> simply because they have few levels; in so doing we risk
> over-confidence in our estimates and tests, because we're assuming
> that the contribution is really 0.
> My opinion is that the structure of the model should honestly reflect
> the structure of the design, at very least. In an ideal world we
> should include the uncertainty around the random effects estimate,
> but I do not see that being done. Maybe two experimental units really
> is too few for inference!
> Best wishes
>> 3. "computational reasons": The algorithms used in lme4 are general
>> to the extent that they are able to handle a huge variety of
>> designs. My experience is using Genstat, which uses quite a
>> different algorithm. was that it rarely failed for the balanced or
>> approximately balanced designs that are usual in field and suchlike
>> experimentation. ASREML would no doubt perform similarly.
>> John Maindonald email: john.maindonald at anu.edu.au
>> phone : +61 2 (6125)3473 fax : +61 2(6125)5549
>> Centre for Mathematics & Its Applications, Room 1194,
>> John Dedman Mathematical Sciences Building (Building 27)
>> Australian National University, Canberra ACT 0200.
>> On 12/10/2012, at 12:20 AM, Ben Bolker <bbolker at gmail.com> wrote:
>>> [cc'ing back to r-sig-mixed]
>>> On 12-10-11 09:08 AM, Andrew Koeser wrote:
>>>> I was going to expand on her question, but you beat me to the punch.
>>>> In agriculture, we typically run the same CRD, RCBD, etc (with all fixed
>>>> effects) for 2 to 3 years. In doing this (given instruction from past
>>>> biometry teachers), I would call year/trial random as I do not really
>>>> care about what year/trial is best and I hope to be able to talk about
>>>> the wider range of conditions seen outside of our time frame. I noticed
>>>> in an archived post that you stated 2-3 years/varieties/etc are not
>>>> enough to base an estimate of the variance of the population of effects.
>>>> Is that ultimately the deciding factor in determining whether or not
>>>> year/trial is fixed or random? In other words, is that sufficient
>>>> justification for calling year/trial fixed?
>>>> This is my one major stumbling block in transitioning from SAS to R. I
>>>> greatly appreciate you comments.
>>> I would argue this is not really a problem in transitioning from SAS
>>> to R, but from classical method-of-moments ANOVA to modern mixed models;
>>> you will have the same kinds of results with SAS PROC MIXED as you will
>>> with nlme/lme4. http://glmm.wikidot.com/faq#fixed_vs_random goes into
>>> more detail. There is a distinction between _conceptual_ or
>>> _philosophical_ random effects (we don't want to make inferences about
>>> specific values, we want to make inferences about the population) and
>>> _computational_ random effects (we want to estimate effects with
>>> shrinkage, we have enough levels to estimate the variance reasonably
>>> well). I would agree that in the best of all possible worlds you would
>>> somehow be able to generalize from an experiment that was run in two
>>> successive years to the performance of a crop variety across all
>>> possible years (and estimate the variance among years accurately), but
>>> that doesn't work particularly well on statistical grounds (the variance
>>> is extremely poorly determined), and in the case of mixed models it
>>> generally fails for computational reasons as well.
>>>> On 10/11/2012 7:07 AM, Ben Bolker wrote:
>>>>> joana martelo <jmmartelo at ...> writes:
>>>>>> I?m modeling fish activity data with a gaussian distribution for scores
>>>>>> obtained from Principal Component Analysis. My explanatory variables are
>>>>>> group size, fish length, temperature and year. Because year has only two
>>>>>> levels I know I can?t use it as a random effect. However, do you
>>>>>> think that
>>>>>> considering year a fixed effect will inflate the effect of the other
>>>>>> explanatory variables?
>>>>> No. On the basis of what you've told us, using year as a fixed
>>>>> effect seems perfectly sensible. You might want to check whether
>>>>> there are important interactions between year and the other explanatory
>>>>> variables ...
>>>>> (Your title seems a bit odd.)
>>>>> Ben Bolker
>>>>> R-sig-mixed-models at r-project.org mailing list
>>> R-sig-mixed-models at r-project.org mailing list
>> R-sig-mixed-models at r-project.org mailing list
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