[R-sig-ME] [R] updating arguments of formulae
Moreno Ignazio Coco
M.I.Coco at sms.ed.ac.uk
Thu Dec 10 18:42:46 CET 2009
Michael,
Thanks a lot for your reply, I have now understood how to fiddle
around with the formulae updates...my question (see my previous e-mail
where I was sketching this problem out) about LME models remains open...
whether:
depM ~ (1 |Sb2) + OS + (1 + OS | Sb2) + VR + (1 + VR | Sb2)
is equivalent to:
depM ~ OS + VR + (1 + OS + VR | Sb2)
and if probably not what is the best approach to it and where I can
find a kind of guideline/rule of thumb list to build
"semi-automatically" linear mixed effect models with fixed effects and
random intercepts/slopes on it.
I am putting in copy the group you suggested me...
Thanks again,
Moreno
Quoting "Meyners,Michael,LAUSANNE,AppliedMathematics"
<Michael.Meyners at rdls.nestle.com>:
> Moreno,
>
> I leave the discussion on the mixed models to others (you might consider
> the SIG group on mixed models as well for this), but try a few hints to
> make your code more accessible:
>
> * The "." in updating a formula is substituted with the respective old
> formula (depending on the side), but is not mandatory. You could give
> the new formula explicitly, i.e. consider something like
> model1 = update(model, . ~ (1 |Sb2) + OS)
> if you loose control about your models. See ?update.formula
>
> * I don't see the need for using your construct with
> as.formula(paste()), this makes things unnecessarily complicated. See my
> above example, which should work as well on your data (and see ?update)
>
> * There is also the "-" operator available in update.formula to remove
> terms (because it uses formula, see ?formula). As to your question on
> how to move from
>> depM ~ OS + (1 + OS | Sb2)
>> to
>> depM ~ OS + VR + (1 + OS + VR | Sb2)
> try something like
> update(model1, .~. - (1 + OS|Sb2) + VR + (1 + OS + VR | Sb2))
> while it goes without saying that in this case, it would be easier to
> drop the "." and use something like
> update(model1, .~ OS + VR + (1 + OS + VR | Sb2))
> directly.
>
> * paste accepts more than just two arguments to be pasted: Try somthing
> like
> model2 = update(model1, as.formula(paste(". ~ . + (1 + ", "OS", "|" ,
> "Sb2", ")"))
> instead of your construct with several nested calls to paste, and see
> ?paste. (Note that I added quotes to "OS" and "Sb2", it didn't work for
> me otherwise as I have no object OS, not sure what happens if you have
> such an object on our search path, but I would suspect you encounter
> problems as well.)
>
> If you work yourself through these and thereby simplify your code, you
> are more likely to get responses to your questions on which model to use
> (which is actually independent from the use of update). As far as I see
> it, it doesn't make sense to use a formula like in your model4, but the
> mixed model experts might tell me wrong (and I got a bit lost in your
> code as well). Please also try to provide commented, minimal,
> self-contained, reproducible code for further enquiries (use e.g. one of
> the examples on ?lmer to create appropriate examples for your
> questions).
>
> HTH, Michael
>
>
>> -----Original Message-----
>> From: r-help-bounces at r-project.org
>> [mailto:r-help-bounces at r-project.org] On Behalf Of Moreno Ignazio Coco
>> Sent: Donnerstag, 10. Dezember 2009 13:35
>> To: R-help at r-project.org
>> Subject: [R] updating arguments of formulae
>>
>> Dear R-Community,
>>
>> I am relatively new with R, so sorry for things which for you
>> might be obvious...
>> I am trying to automatically update lmer formulae.
>>
>> the variables of the model are:
>>
>> depM= my dependent measure
>> Sb2= a random factor
>> OS = a predictor
>> VR= another predictor
>>
>> So, I am building the first model with random intercept only:
>>
>> model = lmer(depM ~ (1 |Sb2))
>>
>> then I update the formula adding the first predictor
>>
>> model1 = update(model, as.formula(paste(". ~ . + ", OS)))
>>
>> the resulting formula will be:
>>
>> depM ~ (1 |Sb2) + OS
>>
>> let suppose now I want to update the model to have OS both as
>> a fixed effect and in the random term, something like:
>>
>> depM ~ (1 + OS |Sb2) + OS
>>
>> I can do something very ugly (please tell me if there is a
>> more elegant way to do it) that looks like:
>>
>> model2 = update(model1, as.formula(paste(paste(paste(paste(".
>> ~ . + (1
>> + ", OS), "|" ), Sb2), ")")))
>>
>> the resulting model2 formula will be:
>>
>> depM ~ (1 |Sb2) + OS + (1 + OS | Sb2)
>>
>> one first thing I am wondering at this point is whether having
>> (1 |Sb2) and (1 + OS | Sb2) in the same expression is redundant.
>> in the output it will obviously tell me that group Sb2 is
>> considered twice:
>>
>> number of obs: 6514, groups: Sb2, 23; Sb2, 23
>>
>> and i am not sure if am doing it correctly...any advice?
>>
>> So let suppose now I want to add the new predictor VR again
>> both in the fixed and in the random part of the formula.
>> If i just repeat the two steps above:
>>
>> model3 = update(model2, as.formula(paste(". ~ . + ", VR)))
>>
>> and then:
>>
>> model4 = update(model3, as.formula(paste(paste(paste(paste(".
>> ~ . + (1
>> + ", VR), "|" ), Sb2), ")")))
>>
>> the formula I get is:
>>
>> depM ~ (1 |Sb2) + OS + (1 + OS | Sb2) + VR + (1 + VR | Sb2)
>>
>> so, basically I am adding new stuff on the right side of the
>> formula...
>>
>> My first question at this point is whether the above formula
>> is equivalent to:
>>
>> depM ~ OS + VR + (1 + OS + VR | Sb2)
>>
>> if is not equivalent, which one of the two is correct?
>>
>> obviously in the second case, group Sb2, is considered only once.
>>
>> If the second version of the formula is the correct one, I
>> don't understand how I can update arguments inside the
>> formula rather than adding things on his right side...
>>
>> thus, in the ideal case, how do I go from something like this:
>>
>> depM ~ OS + (1 + OS | Sb2)
>>
>> to something like this:
>>
>> depM ~ OS + VR + (1 + OS + VR | Sb2)
>>
>> Thanks a lot for your help,
>> Best,
>>
>> Moreno
>>
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>> --
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>> in Scotland, with registration number SC005336.
>>
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>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
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