[R-sig-ME] Is BLUP a good thing?
Jarrod Hadfield
j.hadfield at ed.ac.uk
Mon Mar 28 10:47:28 CEST 2011
Hi,
On 28 Mar 2011, at 05:21, Dominick Samperi wrote:
> After reading the recent publications on the dangers of applying
> BLUP to
> natural populations (Hadfield et al 2010, Morrissey et al 2010) I was
> left wondering why it works at all. The latter paper claims that BLUP
> has a long and successful history when applied to animal breeding,
> but no examples showing its effectiveness were presented.
Look at the photos in Hill & Kirkpatrick (2010) Annu. Rev. Ecol. Evol.
Syst 41:1-19. Those chickens bear testimony to the power of BLUP.
see also http://aipl.arsusda.gov/eval/summary/trend.cfm
>
> The paper Hadfield et al 2010 makes the interesting point that
> BLUP's are often used to estimate effects that are not explicitly
> accounted for in the model. I think this zeros in precisely on the
> problem. If the effect is not accounted for in the model, then
> the model is being used metaphorically, making
> a scientific analysis of cause/effect relationships very difficult
> and open to differing interpretations.
This was not a criticism of BLUP, but a criticism of its misuse. If I
fitted a repeated-measure mixed model, extracted the BLUP for each
individual, and then did a t-test to see if males and females
differed, people would rightly question my approach. Of course, people
weren't doing things as blatantly silly as this, but they were doing
things which were similar and would have the same sorts of consequences.
>
> Some recent books on mixed models do not say a word about
> BLUP, perhaps to avoid any discussion of the difficulties.
>
> So, twenty years after Robinson (1991) has a consensus
> formed on the question of whether or not BLUP is indeed a good thing?
It is a good thing, but it should not be used as a short cut for
formulating the appropriate statistical model.
>
> Doug Bates makes the useful point that it really should
> be called the Bayesian posterior mode, at least in the case of
> a Gaussian prior, but even this insight does not really address
> the question of how BLUP can be used effectively.
See the interesting paper by Blasco (2001) J. of Anim. Sci. 79 8
2023-2046. on the relationship between BLUP and Bayesian posterior
modes amongst other things.
>
>
> It seems to me the "predicting" (or "estimating") a random effect
> that is *assumed* to have a zero mean is a little like
> estimating the intercept in a linear model for
> which the intercept has been excluded. Similarly, how does one
> estimate a random effect when the assumed noise is spherical?
> Furthermore, note that the formula for the random effect BLUP
> predicts exactly zero as the G-matrix goes to zero, something
> that Gianola refers to as "paradoxical".
I can't find the Gianola paper you refer to. You have to be a bit
careful here because animal breeders often use G to mean
kronecker(V_{a},A) where V_{a} is a trait x trait matrix of genetic
(co)variances and A is the additive genetic relationship matrix. In
evolutionary biology G often refers to V_{a}. If V_{a}= 0 then I
think it is a good thing that the BLUPs go to zero. What I think
Gianola meant (I am guessing) is that if A goes to an identity matrix
then the BLUP all come out at zero. This is in some ways paradoxical,
and it is so because BLUP are biased predictions of specific random
effects - again I would read the Blasco paper.
>
> I understand how an analysis of variance can help to determine
> what factors are more important than others, but extracting
> information from "white noise" is bound to leave much room
> for differing interpretations. This is not to say that the conclusions
> are necessarily wrong or ineffective, but they may not be effective
> for the traditional reasons (p-values, etc.), and consensus may
> play as large a role as science/statistics.
Inferences about random effects in a mixed model are usually framed in
terms of estimates of (co)variances rather than BLUPs. Depending on
definition this is generally "effective".
>
> Being a non-expert I hope that these comments are not considered
> to be too basic or off-topic.
>
> Thanks,
> Dominick
Cheers,
Jarrod
>
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