Re: [R] metaprogramming with lm

From: Simon Blomberg <s.blomberg1_at_uq.edu.au>
Date: Thu, 13 Nov 2008 11:19:55 +1000

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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-- 
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.

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be extracted from a given body of data. - John Tukey.

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Received on Thu 13 Nov 2008 - 01:23:21 GMT

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