keeping both numerically and factor coded factors

Dear Elisa,

if you just want use the RePsychLing::rePCA() function on objects
return by the afex::mixed() function, you can simply set the *return*
argument of the mixed() function to "merMod". This will work, but -
somewhat counter-intuitively - make the mixed() function return an
object of class lmerModLmerTest as mixed(), by default, uses
lmerTest::lmer() for its calculations.
If you really need a merMod object, then you need to set
afex_options(lmer_function = "lme4") before calling mixed().

library("afex")
library("RePsychLing")

d <- lme4::sleepstudy
d$Days <- cut(d$Days, breaks = 2, labels = c("first_half", "second_half"))

set_sum_contrasts()

# optional: make mixed() return a merMod object
# afex_options(lmer_function = "lme4")

m1 <- mixed(formula = Reaction ~ Days + (Days || Subject),
                     data = d,
                     expand_re = TRUE,
                     return = "merMod")

summary(rePCA(m1))

Best regards,
Maarten

On Wed, Jul 31, 2019 at 10:14 AM MONACO Elisa via R-sig-mixed-models
<r-sig-mixed-models at r-project.org> wrote:

On Wed, Jul 31, 2019 at 10:14 AM MONACO Elisa via R-sig-mixed-models
<r-sig-mixed-models at r-project.org> wrote:

Thank you,

Robert Long, I think we are claiming the same idea: the maximal model is too complex (overparameterized and with a degenerate/singular solution) and I want to reduce the random structure, following the steps suggested by Bates et al.. Am I correct?
However one of these steps it's indeed "forcing to zero the correlation parameters" and check the good fit of the consequent model. Therefore my question on how to arrange my D factor in the random structure.

I still don't know how to handle CS model suggested by Bolker ((1|g/f)) and how to integrate more factors in that structure ((f1*f2|g/f3)?) ... any suggestions would be much appreciated!

Elisa Monaco


-----Message d'origine-----
De : R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> De la part de Robert Long
Envoy? : mercredi, 24 juillet 2019 10:33
? : R-mixed models mailing list <r-sig-mixed-models at r-project.org>
Objet : Re: [R-sig-ME] keeping both numerically and factor coded factors

It is quite possible that such a complex random structure will not be supported by the data.

In your initial email you mentioned correlations between random effects.
However, since the model did not converge, there is no point in intetpreting them. Moreover, to force them to be uncorrelated is possibly making unrealistic constraints on the model.

Why do seek such a complex random structure ? If you are following the advice by Barr et al (2013) to "keep it maximal", this is often very poor advice, as noted by Bates et al (2015), Bates being the primary author of the lme4 package:

https://arxiv.org/pdf/1506.04967








On Wed, 24 Jul 2019, 09:01 MONACO Elisa, <elisa.monaco at unifr.ch> wrote:

Dear all,
many thanks for your answers and sorry for not providing the details.

My experiment is a 2X2X4 within subject design, with all three factors
being categorical: L=Language of the stimuli (2 levels), V= type of
the stimuli (2 levels), D= delay of brain stimulation (4 levels). My
dependent variable is the amplitude of a physiological measure.

I thought to build my maximal mixed model in which all the factors are
crossed within subjects and only D is crossed within items (items are
the same, repeated at different delays of stimulation):

lmer(MEPzed ~ L * V * D + (L*V*D|subjects) + (D|items), data=mydata,
control=lmerControl(optCtrl=list(maxfun=1e6)))

So, to answer @Robert Long: my factor D I was referring to is a random
slope, with4 levels

to answer at Ben Bolker:
indeed I don't think that my factor D falls in the 2 cases you
mentioned,
because:
 a) the differences between each level is not the same for each level
(150ms-75ms-75ms-150ms) and we don't expect en effect ordered in time,
we expect the effect to be present at one or more latencies depending
on L;
b) the factor has more than two levels.

According to all of this, I should go for a CS model, right?
I'm a newbie in this field, so can you please give me some indications
of what can I read about it or some indications to understand how to
handle this (especially if I want to reduce gradually the random
structure of the subjects part, see modelreduced2)/?

modelreduced1: lmer(MEPzed ~ L * V * D + (L*V*D|subjects) +
(1|items/D), data=mydata,
control=lmerControl(optCtrl=list(maxfun=1e6)))

modelreduced2: lmer(MEPzed ~ L * V * D + (L*V|subjects/D) +
(1|items/D), data=mydata,
control=lmerControl(optCtrl=list(maxfun=1e6)))


Another point: is this semplification indipendent of which type of
contrast I set for D (I'll set sum contrast for V and L, but I'm still
reasoning on what is the best for D)?

Thank you in advance for this big help and please tell me if you need
further clarifications or code.

 Elisa Monaco | PhD student

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