[R-sig-ME] LMM reduction following marginality taking out "item" before "subject:item" grouping factor
Jake Westfall
j@ke@@@we@tf@ll @ending from gm@il@com
Wed Nov 28 22:23:13 CET 2018
Maarten,
So, regarding this issue, there is no difference between taking out
> variance components for main effects before interactions within the same
> grouping factor, e.g. reducing (1 + A*B | subject) to (1 + A:B | subject),
> and taking out the whole grouping factor "item" (i.e. all variance
> components of it) before "subject:item"?
I think that if you have strong evidence that this is the appropriate
random effects structure, then it makes sense to modify your model
accordingly, yes.
Do all variances of the random slopes (for interactions and main effects)
> of a single grouping factor contribute to the standard errors of the fixed
> main effects and interactions in the same way?
No -- in general, with unbalanced datasets and continuous predictors, it's
hard to say much for sure other than "no." But it can be informative to
think of simpler, approximately balanced ANOVA-like designs where it's much
easier to say much more about which variance components enter which
standard errors and how.
I have a Shiny power analysis app, PANGEA (power analysis for general anova
designs) <http://jakewestfall.org/pangea/>, which as a side feature you can
also use to compute the expected mean square equations for arbitrary
balanced designs w/ categorical predictors. Near the bottom of "step 1"
there is a checkbox for "show expected mean square equations." So you can
specify your design, check the box, then hit the "submit design" button to
view a table representing the equations, with rows = mean squares and
columns = variance components. (A little while ago Shiny changed how it
renders tables and now the row labels no longer appear, which is really
annoying, but they are given in the reverse order of the column labels, so
that the diagonal from bottom-left to top-right is where the mean squares
and variance components correspond.) The standard error for a particular
fixed effect is proportional to the (square root of the) corresponding mean
square divided by the total sample size, that is, by the product of all the
factor sample sizes. So examining the mean square for an effect will tell
you which variance components enter its standard error and which sample
sizes they are divided by in the expression. I find this useful for getting
a sense of how the variance components affect the standard errors, even
though the results from this app are only simplified approximations to
those from more realistic and complicated designs.
Jake
On Wed, Nov 28, 2018 at 2:33 PM Maarten Jung <
Maarten.Jung using mailbox.tu-dresden.de> wrote:
> Jake,
>
> thanks for this insight.
> So, regarding this issue, there is no difference between taking out
> variance components for main effects before interactions within the same
> grouping factor, e.g. reducing (1 + A*B | subject) to (1 + A:B | subject),
> and taking out the whole grouping factor "item" (i.e. all variance
> components of it) before "subject:item"?
>
> And, I would be glad if you could answer this related question:
> Do all variances of the random slopes (for interactions and main effects)
> of a single grouping factor contribute to the standard errors of the fixed
> main effects and interactions in the same way?
>
> Regards,
> Maarten
>
> On Wed, Nov 28, 2018, 20:03 Jake Westfall <jake.a.westfall using gmail.com
> wrote:
>
>> Maarten,
>>
>> No, I would not agree that the Bates quote is referring to the principle
>> of marginality in the sense of e.g.:
>> https://en.wikipedia.org/wiki/Principle_of_marginality
>>
>> Bates can chip in if he wants, but as I see it, the quote doesn't hint at
>> anything like this. It simply says that "variance components of
>> higher-order interactions should generally be taken out of the model before
>> lower-order terms nested under them" -- which I agree with. The reason this
>> is _generally_ true is because hierarchical ordering is _generally_ true.
>> But it looks like it's not true in your particular case.
>>
>> can you think of a reason why they suggest to follow this principle other
>>> than "higher-order interactions tend to explain less variance than
>>> lower-order interations"?
>>
>>
>> No.
>>
>> Jake
>>
>> On Wed, Nov 28, 2018 at 12:53 PM Maarten Jung <
>> Maarten.Jung using mailbox.tu-dresden.de> wrote:
>>
>>> Hi Jake,
>>>
>>> Thanks for your thoughts on this.
>>>
>>> I thought that Bates et al. (2015; [1]) were referring to this principle
>>> when they stated:
>>> "[...] we can eliminate variance components from the LMM, following the
>>> standard statistical principle with respect to interactions and main
>>> effects: variance components of higher-order
>>> interactions should generally be taken out of the model before
>>> lower-order terms nested under them. Frequently, in the end, this leads
>>> also to the elimination of variance
>>> components of main effects." (p. 6)
>>>
>>> Would you agree with me that this is referring to the principle of
>>> marginality? And if so, can you think of a reason why they suggest to
>>> follow this principle other than "higher-order interactions tend to explain
>>> less variance than lower-order interations"?
>>>
>>> Best regards,
>>> Maarten
>>>
>>> [1] https://arxiv.org/pdf/1506.04967v1.pdf
>>>
>>> On Wed, Nov 28, 2018 at 7:24 PM Jake Westfall <jake.a.westfall using gmail.com>
>>> wrote:
>>>
>>>> Maarten,
>>>>
>>>> I think it's fine. I can't think of any reason to respect a principle
>>>> of marginality for the random variance components. I agree with the feeling
>>>> that it's better to remove higher-order interactions before lower-order
>>>> interactions and so on, but that's just because of hierarchical ordering
>>>> (higher-order interactions tend to explain less variance than lower-order
>>>> interations), not because of any consideration of marginality. If in your
>>>> data you find that hierarchical ordering is not quite true and instead the
>>>> highest-order interaction is important while a lower-order one is not, then
>>>> it makes sense to me to let your model reflect that finding.
>>>>
>>>> Jake
>>>>
>>>> On Wed, Nov 28, 2018 at 12:18 PM Maarten Jung <
>>>> Maarten.Jung using mailbox.tu-dresden.de> wrote:
>>>>
>>>>> Dear list,
>>>>>
>>>>> In a 2 x 2 fully crossed design in which every participant responds to
>>>>> every stimulus multiple times in each cell of the factorial design the
>>>>> maximal linear mixed model justified by the design (using the lme4
>>>>> syntax)
>>>>> should be:
>>>>> y ~ A * B + (1 + A * B | subject) + (1 + A * B | item) + (1 + A * B |
>>>>> subject:item)
>>>>>
>>>>> Within a model reduction process, be it because the estimation
>>>>> algorithm
>>>>> doesn't converge or the model is overparameterized or one wants to
>>>>> balance
>>>>> Type-1 error rate and power, I follow the principle of marginality
>>>>> taking
>>>>> out higher-order interactions before lower-order terms (i.e.
>>>>> lower-order
>>>>> interactions and main effects) nested under them and random slopes
>>>>> before
>>>>> random intercepts.
>>>>> However, it occurs that the variance components of the grouping factor
>>>>> "item" are not significant while those of the grouping factor
>>>>> "subject:item" are.
>>>>>
>>>>> Does it make sense to remove the whole grouping factor "item" before
>>>>> taking
>>>>> out the variance components of the grouping factor "subejct:item"?
>>>>>
>>>>> A reduced model would f.i. look like this:
>>>>> y ~ A * B + (1 + A | subject) + (1 | subject:item)
>>>>>
>>>>> I'm not sure whether this contradicts the principal of marginality
>>>>> and, in
>>>>> general, whether this is a sound approach.
>>>>>
>>>>> Any help is highly appreciated.
>>>>>
>>>>> Best regards,
>>>>> Maarten
>>>>>
>>>>> [[alternative HTML version deleted]]
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-mixed-models using r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>
>>>>
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