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
________________________________________
De : R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> de la
part de Ben Bolker <bbolker at gmail.com>
Envoy? : lundi 22 juillet 2019 17:56
? : r-sig-mixed-models at r-project.org
Objet : Re: [R-sig-ME] keeping both numerically and factor coded factors
Elisa,
Can you say a little more about what your factor represents?
It probably *doesn't* make sense to collapse your factor to an integer
for the purpose of allowing a diagonal covariance matrix, unless:
* it's reasonable to treat the factor levels as sequential values with
equal differences between each successive pair (e.g., time), OR
* the factor only has two levels anyway
Another simplifying strategy is to use a compound-symmetric model
(equal correlations among all pairs of levels): if your original model
is (f|g) (where f is a factor and g is your grouping variable), then
(1|g/f) will generate a CS model.
cheers
Ben Bolker
On 2019-07-22 10:24 a.m., Robert Long wrote:
Dear Elisa
Is this factor a grouping variable (for random intercepts) or a random
slope ? How many levels does it have ? And lease can you give us the full
model formula.
On Mon, 22 Jul 2019, 12:17 MONACO Elisa via R-sig-mixed-models, <
r-sig-mixed-models at r-project.org> wrote:
Dear list,
looking at the correlation values of my random effects, as well as the
fact that my model fails to converge, it makes sense to me to simplify
its
random structure (while keeping maximal and according to our hp the
fixed
structure).
One way is to remove correlations, and I know that the || notation works
only with numerically coded factors.
As far as I understood, I have two options:
1) use the package afex, putting my model as object of mixed and adding
"expand_re=true"
2) use the original factor, by default read as "int"
I want to use the option 2) because with mixed I can't apply the PCA
function for random effects to check if my model is over parameterized.
My questions are:
a) is it true that I can use my factor as it is when read by R, i.e.
"int"?
b) if yes, does it make sense to keep in the model both the factor in
the nominal form as fixed effect and the factor in the numerical form as
random effect?
Many thanks for your help,
Elisa Monaco | PhD student
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