updating arguments of formulae

-----Original Message-----
From: Moreno Ignazio Coco [mailto:M.I.Coco at sms.ed.ac.uk] 
Sent: Donnerstag, 10. Dezember 2009 18:43
To: Meyners,Michael,LAUSANNE,AppliedMathematics
Cc: R-help at r-project.org; r-sig-mixed-models at r-project.org
Subject: RE: [R] updating arguments of formulae

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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