If 'subjIndexF' is a factor for subject, then s(subjIndexF, bs="re")
will produce a random effect for subject. i.e. each subject will be
given its own random intercept term, which is a way that repeated
measures data like this are often handled.
The reason for the s(subjIndexF, bs="re") syntax is that smooths can be
viewed as Gaussian random effects, so simple Gaussian random effects can
also be viewed as (0-dimensional) smooths. In general s(x,z,w,bs="re")
just appends the columns of model.matrix(~x:z:w-1) to the gam model
matrix, and treats the associated coefficients as i.i.d. Gaussian random
effects with a common variance (to be estimated). In principle this
works with any number of arguments to s(...,bs="re").
See ?random.effects (and its linked help files) in mgcv for more.
There are mechanisms for allowing random smooth curves for each subject,
(e.g. ?factor.smooth.interaction), but I would only use these if simpler
approaches really aren't adequate here.
best,
Simon
On 30/03/17 17:06, David Winsemius wrote:
On Mar 30, 2017, at 6:56 AM, Leon Lee <bhamlio...@gmail.com> wrote:
David
Thank you for your reply. I apologize if I posted in the wrong forum, as I
really couldn't decide which forum is the best place for my question and I saw
similar questions asked before in this forum.
I agree that a sample of ~30 subjects (70 scans in total), the model can be too
complicated. Based on that, I did the following:
(1) ignored the gender effect, as we have less females than males.
(2) corrected chronological age based on their gestational age, that is, we
subtracted an infant's chronological age by 2 weeks, if the infant's
gestational age is 38 weeks instead of 40weeks.
When I ran the model with corrected age, gestational age and their interactions
modeled, I found the main effect of gestational age and the interaction between
the two are gone.
So, my final model will look something like this:
gamObj=gam(brainVolume~ s(correctedAge) + s(subjIndexF, bs="re"),
method="REML", data=mydata)
Does this look more reasonable?
I'm still having difficulty understand how a "smoothing" function would be used to handle
repeated measures without some sort of "group-within" indicator.
I would have imagined (and this is because I have no experience with using this
package for repeated measures) something along the lines of:
...+s(correctedAg|subjIndexF)
I see this statement in the docs:
smooth.construct.re.smooth.spec {mgcv}
"gam can deal with simple independent random effects, by exploiting the link between smooths
and random effects to treat random effects as smooths. s(x,bs="re") implements this."
But I don't see that as applying to the dependency between individuals measured
repeatedly. I find no examples of repeated measures problems being solve by
gam(). There is also a note on the same page:
"Note that smooth ids are not supported for random effect terms. Unlike most smooth
terms, side conditions are never applied to random effect terms in the event of nesting
(since they are identifiable without side conditions)."
When I do a search on "using gam mgcv formula mixed effects" I am referred to
packages 'gamm' and 'gamm4' produced by the same author (Simon Wood) as pkg 'mgcv', or to
package `nlme`.
Yes, I am relatively new to the mixed model. We originally applied functional
data analysis (PACE) on the data, but want to see the results using a different
approach. Also, I couldn't find the Mixed Models list, do you mind sending me a
link?
This is a link to the main mailing lists page:
https://www.r-project.org/mail.html
Found with a search on Google with "R mailing lists"
Thank you!
Longchuan
On Tue, Mar 28, 2017 at 4:28 PM, David Winsemius <dwinsem...@comcast.net> wrote:
On Mar 28, 2017, at 9:32 AM, Leon Lee <bhamlio...@gmail.com> wrote:
Hi, R experts
I am new to R & GAM toolbox and would like to get inputs from you all on my
models. The question I have is as follows:
I have 30 subjects with each subject being scanned from one to three times
in the first year of life. The brain volume from each scan was measured.
The scan time was randomly distributed from birth to 1 year.
Each subject has different gestational age ranging from 38 to 41 weeks
Each subject has chronological age from birth to 1 year old
Each subject has gender category.
Now, I want to look at how predictors, such as subject's chronological age,
gestational age and gender will explain the changes in brain volume. I also
want to include interactions between gender and age, gestational and
chronological age. Random effects are also included in the model to account
for subject variability. My model looks like the follows:
gam=gam(brainVolume~ s(age) + ti(age, gestationalAge) + gestationalAge +
sex + s(age, by=sex) + s(subjIndexF, bs="re"), method="REML", data=mydata)
Are there any obvious mistakes in the model? Any suggestions will be
greatly appreciated!
I'm not seeing mistakes in the syntax but I would question whether 30 subjects is
sufficient to adequately support estimates in a a model of this complexity. I would also
think that the 's(age)' and 'sex' terms would get aliased out in a model with "+
s(age, by=sex)". Most R regression functions handle removal of over-parametrization
automatically.
You also have a variable number of measurements per subject. I am unable to comment on the effort
to account for the implicit and variably measured correlation and auto-correlation of values within
subjects using a "smooth" on subjIndexF, since that is not an approach I was familiar
with. But I am getting concerned whether you are also new to statistical modeling in addition to
your use of R and GAM being "new to you"?
(Perhaps Simon or one of the mixed-effects experts can correct the gaps in my
understanding of how to model repeated measures in the context of small numbers
of subjects and irregular emasurements.)
Please read the Posting Guide and the pages of candidate mailing lists. Rhelp
is not really the place to go when you need statistical advice. I'm not sure if
this is really in the center of concerns that get discussed on the Mixed Models
list, but to my eyes it would be a better fit there.
--
David.
L
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David Winsemius
Alameda, CA, USA
David Winsemius
Alameda, CA, USA
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Simon Wood, School of Mathematics, University of Bristol BS8 1TW UK
+44 (0)117 33 18273 http://www.maths.bris.ac.uk/~sw15190
______________________________________________
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