[R] metaprogramming with lm

[Simon Blomberg]

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, Bill.Venables at csiro.au 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: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of June Kim
> Sent: Thursday, 13 November 2008 10:27 AM
> To: r-help at 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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> ______________________________________________
> R-help at r-project.org mailing list
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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.
-- 
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

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