keeping both numerically and factor coded factors

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

        [[alternative HTML version deleted]]

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

        [[alternative HTML version deleted]]

	[[alternative HTML version deleted]]

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

        [[alternative HTML version deleted]]

      [[alternative HTML version deleted]]

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

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