For performance reasons, I advise on using the following function instead of
model.matrix:
factorsToDummyVariables<-function(dfr, betweenColAndLevel="")
{
nc<-dim(dfr)[2]
firstRow<-dfr[1,]
coln<-colnames(dfr)
retval<-do.call(cbind, lapply(seq(nc), function(ci){
if(is.factor(firstRow[,ci]))
{
lvls<-levels(firstRow[,ci])[-1]
stretchedcols<-sapply(lvls, function(lvl){
rv<-dfr[,ci]==lvl
mode(rv)<-"integer"
return(rv)
})
if(!is.matrix(stretchedcols))
stretchedcols<-matrix(stretchedcols, nrow=1)
colnames(stretchedcols)<-paste(coln[ci],
lvls, sep=betweenColAndLevel)
return(stretchedcols)
}
else
{
curcol<-matrix(dfr[,ci], ncol=1)
colnames(curcol)<-coln[ci]
return(curcol)
}
}))
rownames(retval)<-rownames(dfr)
return(retval)
}
Just for comparison: here is my old version of the same function, using
model.matrix:
factorsToDummyVariables.old<-function(dfrPredictors,
form=paste("~",paste(colnames(dfrPredictors), collapse="+"), sep=""))
{
#note: this function seems to operate quite slowly!
#Because it is used often, it may be worth improving its speed
dfrTmp<-model.frame(dfrPredictors, na.action=na.pass)
frm<-as.formula(form)
mm<-model.matrix(frm, data=dfrTmp)
retval<-as.matrix(mm)[,-1]
return(retval)
}
In a testcase with a reasonably big dataset, I compared the speeds:
#system.time(tmp.fd.convds.full.man<-manualFactorsToDummyVariables(ds))
## user system elapsed
## 9.44 0.00 9.48
#system.time(tmp.fd.convds.full<-factorsToDummyVariables.old(ds))
## user system elapsed
## 15.49 0.00 15.64
#system.time(invisible(factorsToDummyVariables (ds[10,])))
## user system elapsed
## 0.36 0.00 0.36
#system.time(invisible(factorsToDummyVariables.old (ds[10,])))
## user system elapsed
## 2.18 0.00 2.20
#system.time(invisible(factorsToDummyVariables (ds[20:30,])))
## user system elapsed
## 0.34 0.00 0.38
#system.time(invisible(factorsToDummyVariables.old (ds[20:30,])))
## user system elapsed
## 2.11 0.00 2.15
If you have to do this quite often, the difference surely adds up...
More improvements may be possible.
This function only works if you don't include interactions, though.
Nick Sabbe
--
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link: http://biomath.ugent.be
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-- Do Not Disapprove
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of David Winsemius
Sent: maandag 2 mei 2011 20:48
To: Steve Lianoglou
Cc: r-help@r-project.org
Subject: Re: [R] Lasso with Categorical Variables
On May 2, 2011, at 10:51 AM, Steve Lianoglou wrote:
> Hi,
>
> On Mon, May 2, 2011 at 12:45 PM, Clemontina Alexander <ckale...@ncsu.edu
> > wrote:
>> Hi! This is my first time posting. I've read the general rules and
>> guidelines, but please bear with me if I make some fatal error in
>> posting. Anyway, I have a continuous response and 29 predictors made
>> up of continuous variables and nominal and ordinal categorical
>> variables. I'd like to do lasso on these, but I get an error. The way
>> I am using "lars" doesn't allow for the factors. Is there a special
>> option or some other method in order to do lasso with cat. variables?
>>
>> Here is and example (considering ordinal variables as just nominal):
>>
>> set.seed(1)
>> Y <- rnorm(10,0,1)
>> X1 <- factor(sample(x=LETTERS[1:4], size=10, replace = TRUE))
>> X2 <- factor(sample(x=LETTERS[5:10], size=10, replace = TRUE))
>> X3 <- sample(x=30:55, size=10, replace=TRUE) # think age
>> X4 <- rchisq(10, df=4, ncp=0)
>> X <- data.frame(X1,X2,X3,X4)
>>
>>> str(X)
>> 'data.frame': 10 obs. of 4 variables:
>> $ X1: Factor w/ 4 levels "A","B","C","D": 4 1 3 1 2 2 1 2 4 2
>> $ X2: Factor w/ 5 levels "E","F","G","H",..: 3 4 3 2 5 5 5 1 5 3
>> $ X3: int 51 46 50 44 43 50 30 42 49 48
>> $ X4: num 2.86 1.55 1.94 2.45 2.75 ...
>>
>>
>> I'd like to do:
>> obj <- lars(x=X, y=Y, type = "lasso")
>>
>> Instead, what I have been doing is converting all data to continuous
>> but I think this is really bad!
>
> Yeah, it is.
>
> Check out the "Categorical Predictor Variables" section here for a way
> to handle such predictor vars:
> http://www.psychstat.missouristate.edu/multibook/mlt08m.html
Steve's citation is somewhat helpful, but not sufficient to take the
next steps. You can find details regarding the mechanics of typical
linear regression in R on the ?lm page where you find that the factor
variables are typically handled by model.matrix. See below:
> model.matrix(~X1 + X2 + X3 + X4, X)
(Intercept) X1B X1C X1D X2F X2G X2H X2I X3 X4
1 1 0 0 1 0 1 0 0 51 2.8640884
2 1 0 0 0 0 0 1 0 46 1.5462243
3 1 0 1 0 0 1 0 0 50 1.9430901
4 1 0 0 0 1 0 0 0 44 2.4504180
5 1 1 0 0 0 0 0 1 43 2.7535052
6 1 1 0 0 0 0 0 1 50 1.6200326
7 1 0 0 0 0 0 0 1 30 0.5750533
8 1 1 0 0 0 0 0 0 42 5.9224777
9 1 0 0 1 0 0 0 1 49 2.0401528
10 1 1 0 0 0 1 0 0 48 6.2995288
attr(,"assign")
[1] 0 1 1 1 2 2 2 2 3 4
attr(,"contrasts")
attr(,"contrasts")$X1
[1] "contr.treatment"
attr(,"contrasts")$X2
[1] "contr.treatment"
The numeric variables are passed through, while the dummy variables
for factor columns are constructed (as treatment contrasts) and the
whole thing it returned in a neat package.
--
David.
>
> HTH,
> -steve
>
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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______________________________________________
R-help@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.
