Yet again my baroque programming style shows itself. The . notation is
great, although solution 2. is perhaps more versatile, allowing you to
pick and choose your predictors more easily.
On Thu, 2008-11-13 at 11:56 +1100, [EMAIL PROTECTED] wrote:
> Two possible ways around this are
>
> 1. If the x's are *all* the other variables in your data frame you can use a
> dot:
>
> fm <- lm(y ~ ., data = myData)
>
> 2. Here is another idea
>
> > as.formula(paste("y~", paste("x",1:10, sep="", collapse="+")))
> y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10
> >
>
> (You bore easily!)
>
>
> Bill Venables
> http://www.cmis.csiro.au/bill.venables/
>
>
> -----Original Message-----
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of June Kim
> Sent: Thursday, 13 November 2008 10:27 AM
> To: r-help@r-project.org
> Subject: [R] metaprogramming with lm
>
> Hello,
>
> Say I want to make a multiple regression model with the following expression:
>
> lm(y~x1 + x2 + x3 + ... + x_n,data=mydata)
>
> It gets boring to type in the whole independent variables, in this
> case x_i. Is there any simple way to do the metaprogramming for this?
> (There are different cases where the names of the independent
> variables might sometimes have apparent patterns or not)
>
> ______________________________________________
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>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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--
Simon Blomberg, BSc (Hons), PhD, MAppStat.
Lecturer and Consultant Statistician
Faculty of Biological and Chemical Sciences
The University of Queensland
St. Lucia Queensland 4072
Australia
Room 320 Goddard Building (8)
T: +61 7 3365 2506
http://www.uq.edu.au/~uqsblomb
email: S.Blomberg1_at_uq.edu.au
Policies:
1. I will NOT analyse your data for you.
2. Your deadline is your problem.
The combination of some data and an aching desire for
an answer does not ensure that a reasonable answer can
be extracted from a given body of data. - John Tukey.
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