[R] define subset argument for function lm as variable?

[Joshua Wiley]

On Wed, Aug 29, 2012 at 3:56 AM, Milan Bouchet-Valat <nalimilan at club.fr> wrote:
> Le mardi 21 août 2012 à 07:51 -0700, Joshua Wiley a écrit :
>> Hi Rainer,
>>
>> You could try:
>>
>> subs <- expression(dead==FALSE & recTreat==FALSE)
>>
>> lme(formula, subset = eval(subs))
>>
>> Not tested, but something along those lines should work.
> Out of curiosity, why isn't "subset" (and "weights", which is very
> similar in that regard) evaluated in the "data" environment, just like
> the formula? Is this for historical reasons, or are there drawbacks to
> such a feature?

I am not sure about weights offhand, but subset is evaluated in the
data environment----that is why that solution works.  The original
question was how to setup the expression as an object that was passed
to subset.  The trick is to avoid having the logical expression
evaluated when the object is created, which I avoided by using
expression, and then in lme() forcing the evaluation of the object.

>
> It seems very common to pass a data frame via the "data" argument, and
> use variables from it for subsetting and/or weighting.
>
>
> Regards
>
>
>> Josh
>>
>> On Tue, Aug 21, 2012 at 7:44 AM, Rainer M Krug <r.m.krug at gmail.com> wrote:
>> > Hi
>> >
>> > I want to do a series of linear models, and would like to define the input
>> > arguments for lm() as variables. I managed easily to define the formula
>> > arguments in a variable, but I also would like to have the "subset" in a
>> > variable. My reasoning is, that I have the subset in the results object.
>> >
>> > So I wiould like to add a line like:
>> >
>> > subs <- dead==FALSE & recTreat==FALSE
>> >
>> > which obviously does not work as the expression is evaluated immediately. Is
>> > is it possible to do what I want to do here, or do I have to go back to use
>> >
>> > dat <- subset(dat, dead==FALSE & recTreat==FALSE)
>> >
>> > ?
>> >
>> >
>> >
>> > dat <- loadSPECIES(SPECIES)
>> > feff <- height~pHarv*year               # fixed effect in the model
>> > reff <- ~year|plant                     # random effect in the model, where
>> > year is the
>> > dat.lme <- lme(
>> >              fixed = feff,                           # fixed effect in the
>> > model
>> >              data  = dat,
>> >              random = reff,                          # random effect in the
>> > model
>> >              correlation = corAR1(form=~year|plant), #
>> >              subset = dead==FALSE & recTreat==FALSE, #
>> >              na.action = na.omit
>> >              )
>> > dat.lm <- lm(
>> >             formula =  feff,              # fixed effect in the model
>> >             data = dat,
>> >             subset = dead==FALSE & recTreat==FALSE,
>> >             na.action = na.omit
>> >             )
>> >
>> > Thanks,
>> >
>> > Rainer
>> >
>> > --
>> > Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation Biology,
>> > UCT), Dipl. Phys. (Germany)
>> >
>> > Centre of Excellence for Invasion Biology
>> > Stellenbosch University
>> > South Africa
>> >
>> > Tel :       +33 - (0)9 53 10 27 44
>> > Cell:       +33 - (0)6 85 62 59 98
>> > Fax :       +33 - (0)9 58 10 27 44
>> >
>> > Fax (D):    +49 - (0)3 21 21 25 22 44
>> >
>> > email:      Rainer at krugs.de
>> >
>> > Skype:      RMkrug
>> >
>> > ______________________________________________
>> > R-help at r-project.org mailing list
>> > 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.
>>
>>
>>
>



-- 
Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group
University of California, Los Angeles
https://joshuawiley.com/