R help - Mar 2005 - creating a formula on-the-fly inside a function

I have a function that, among other things, runs a linear model and
returns r2. But, the number of predictor variables passed to the
function changes from 1 to 3. How can I change the formula inside the
function depending on the number of variables passed in?

An example:

get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL,
pred3.dat = NULL)
{
    res <- lm(response.dat ~ pred1.dat + pred2.dat + pred3.dat)
    summary(res)$r.squared
    # other stuff happens here...
}

y <- rnorm(10)
x1 <- y + runif(10)
x2 <- y + runif(10)
x3 <- y + runif(10)
get.model.fit(y, x1, x2, x3)
get.model.fit(y, x1, x2)
get.model.fit(y, x1)

Many thanks....
DrC



> version
         _              
platform i386-pc-mingw32
arch     i386           
os       mingw32        
system   i386, mingw32  
status                  
major    2              
minor    0.1            
year     2004           
month    11             
day      15             
language R

as.formula(paste(response.dat" ~ ", paste(pred.dat, collapse=
"+")))

pred.dat is a list of predictors

Sandip

-----Original Message-----
From: Dr Carbon [mailto:drcarbon@gmail.com]
Sent: Thursday, March 03, 2005 10:28 AM
To: r-help@r-project.org
Subject: [R] creating a formula on-the-fly inside a function


I have a function that, among other things, runs a linear model and
returns r2. But, the number of predictor variables passed to the
function changes from 1 to 3. How can I change the formula inside the
function depending on the number of variables passed in?

An example:

get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL,
pred3.dat = NULL)
{
    res <- lm(response.dat ~ pred1.dat + pred2.dat + pred3.dat)
    summary(res)$r.squared
    # other stuff happens here...
}

y <- rnorm(10)
x1 <- y + runif(10)
x2 <- y + runif(10)
x3 <- y + runif(10)
get.model.fit(y, x1, x2, x3)
get.model.fit(y, x1, x2)
get.model.fit(y, x1)

Many thanks....
DrC



> version
         _              
platform i386-pc-mingw32
arch     i386           
os       mingw32        
system   i386, mingw32  
status                  
major    2              
minor    0.1            
year     2004           
month    11             
day      15             
language R

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On Thu, 2005-03-03 at 10:28 -0500, Dr Carbon wrote:
> I have a function that, among other things, runs a linear model and
> returns r2. But, the number of predictor variables passed to the
> function changes from 1 to 3. How can I change the formula inside the
> function depending on the number of variables passed in?
> 
> An example:
> 
> get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL,
> pred3.dat = NULL)
> {
>     res <- lm(response.dat ~ pred1.dat + pred2.dat + pred3.dat)
>     summary(res)$r.squared
>     # other stuff happens here...
> }
> 
> y <- rnorm(10)
> x1 <- y + runif(10)
> x2 <- y + runif(10)
> x3 <- y + runif(10)
> get.model.fit(y, x1, x2, x3)
> get.model.fit(y, x1, x2)
> get.model.fit(y, x1)

Consider using as.formula() to take a character vector that you pass as
an argument instead of specifying each IV separately:

get.model.fit <- function(my.form)
{
    res <- lm(as.formula(my.form))
    summary(res)$r.squared
    # other stuff happens here...
}


Then call it with:

get.model.fit("y ~ x1 + x2 + x3")

Internally, the vector will be converted to:
> as.formula("y ~ x1 + x2 + x3")
y ~ x1 + x2 + x3

Doing it this way provides for greater flexibility if you want to use a
more complicated formula construct.

See ?as.formula for more information and further examples, including the
use of paste() if you want to separate the DV from the IVs for an
additional approach for a long set of similarly named IV's (ie x1:x25).

HTH,

Marc Schwartz

Dr Carbon <drcarbon <at> gmail.com> writes:

: 
: I have a function that, among other things, runs a linear model and
: returns r2. But, the number of predictor variables passed to the
: function changes from 1 to 3. How can I change the formula inside the
: function depending on the number of variables passed in?
: 
: An example:
: 
: get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL,
: pred3.dat = NULL)
: {
:     res <- lm(response.dat ~ pred1.dat + pred2.dat + pred3.dat)
:     summary(res)$r.squared
:     # other stuff happens here...
: }


The following allows any number of predictors:

 f <- function(y, ...) summary(lm(y ~., data.frame(y = y, ...)))$r.squared

Another possibility is to just pass the formula itself:

 f <- function(fo) summary(lm(fo))$r.squared