[R] randomForest

[Weiwei Shi]

thanks. but can you suggest some ways for the classification problems
since for some specific class, there are too few observations.

the following is from adding sample.size :
> najie.rf.2 <- randomForest(Diag~., data=one.df[ind==1,4:ncol(one.df)], importance=T, sampsize=unlist(sample.size))
> najie.pred.2<- predict(najie.rf.2, one.df[ind==2,])

> table(observed=one.df[ind==2,"Diag"], predicted=najie.pred.2)
        predicted
observed  1  2  3  4  5  6
       1  6  0  1  0  0  1
       2  0  4  0  0  0  0
       3  1  0 37  0  0  0
       4  0  0  3  5  0  0
       5  1  0  3  0  8  0
       6  0  0  0  3  0  5

and class number returned from sample.size is like:
28, 8, 82, 28, 18, 22

Should I use gbm to try it since it might "focus" more on misplaced cases?

thanks,

weiwei


On 7/7/05, Liaw, Andy <andy_liaw at merck.com> wrote:
> > From: Weiwei Shi
> >
> > it works.
> > thanks,
> >
> > but: (just curious)
> > why i tried previously and i got
> >
> > > is.vector(sample.size)
> > [1] TRUE
> 
> Because a list is also a vector:
> 
> > a <- c(list(1), list(2))
> > a
> [[1]]
> [1] 1
> 
> [[2]]
> [1] 2
> 
> > is.vector(a)
> [1] TRUE
> > is.numeric(a)
> [1] FALSE
> 
> Actually, the way I initialize a list of known length is by something like:
> 
> myList <- vector(mode="list", length=veryLong)
> 
> Andy
> 
> 
> > i also tried as.vector(sample.size) and assigned it to sampsz,it still
> > does not work.
> >
> > On 7/7/05, Duncan Murdoch <murdoch at stats.uwo.ca> wrote:
> > > On 7/7/2005 3:38 PM, Weiwei Shi wrote:
> > > > Hi there:
> > > > I have a question on random foresst:
> > > >
> > > > recently i helped a friend with her random forest and i
> > came with this problem:
> > > > her dataset has 6 classes and since the sample size is
> > pretty small:
> > > > 264 and the class distr is like this (Diag is the
> > response variable)
> > > > sample.size <- lapply(1:6, function(i) sum(Diag==i))
> > > >> sample.size
> > > > [[1]]
> > > > [1] 36
> > > >
> > > > [[2]]
> > > > [1] 12
> > > >
> > > > [[3]]
> > > > [1] 120
> > > >
> > > > [[4]]
> > > > [1] 36
> > > >
> > > > [[5]]
> > > > [1] 30
> > > >
> > > > [[6]]
> > > > [1] 30
> > > >
> > > > I assigned this sample.size to sampsz for a stratiefied sampling
> > > > purpose and i got the following error:
> > > > Error in sum(..., na.rm = na.rm) : invalid 'mode' of argument
> > > >
> > > > if I use sampsz=c(36, 12, 120, 36, 30, 30), then it is
> > fine. Could you
> > > > tell me why?
> > >
> > > The sum() function knows what to do on a vector, but not on
> > a list.  You
> > > can turn your sample.size variable into a vector using
> > >
> > > unlist(sample.size)
> > >
> > > Duncan Murdoch
> > >
> > > > btw, as to classification problem for this with uneven
> > class number
> > > > situation, do u have some suggestions to improve its accuracy?  I
> > > > tried to use c() way to make the sampsz works but the result is
> > > > similar.
> > > >
> > > > Thanks,
> > > >
> > > > weiwei
> > > >
> > > > On 6/30/05, Liaw, Andy <andy_liaw at merck.com> wrote:
> > > >> The limitation comes from the way categorical splits are
> > represented in the
> > > >> code:  For a categorical variable with k categories, the split is
> > > >> represented by k binary digits: 0=right, 1=left.  So it
> > takes k bits to
> > > >> store each split on k categories.  To save storage, this
> > is `packed' into a
> > > >> 4-byte integer (32-bit), thus the limit of 32 categories.
> > > >>
> > > >> The current Fortran code (version 5.x) by Breiman and
> > Cutler gets around
> > > >> this limitation by storing the split in an integer
> > array.  While this lifts
> > > >> the 32-category limit, it takes much more memory to
> > store the splits.  I'm
> > > >> still trying to figure out a more memory efficient way
> > of storing the splits
> > > >> without imposing the 32-category limit.  If anyone has
> > suggestions, I'm all
> > > >> ears.
> > > >>
> > > >> Best,
> > > >> Andy
> > > >>
> > > >> > From: Arne.Muller at sanofi-aventis.com
> > > >> >
> > > >> > Hello,
> > > >> >
> > > >> > I'm using the random forest package. One of my factors in the
> > > >> > data set contains 41 levels (I can't code this as a numeric
> > > >> > value - in terms of linear models this would be a random
> > > >> > factor). The randomForest call comes back with an error
> > > >> > telling me that the limit is 32 categories.
> > > >> >
> > > >> > Is there any reason for this particular limit? Maybe it's
> > > >> > possible to recompile the module with a different cutoff?
> > > >> >
> > > >> >       thanks a  lot for your help,
> > > >> >       kind regards,
> > > >> >
> > > >> >
> > > >> >       Arne
> > > >> >
> > > >> > ______________________________________________
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> > > >> > PLEASE do read the posting guide!
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> > > >> >
> > > >> >
> > > >> >
> > > >>
> > > >> ______________________________________________
> > > >> R-help at stat.math.ethz.ch mailing list
> > > >> https://stat.ethz.ch/mailman/listinfo/r-help
> > > >> PLEASE do read the posting guide!
> > http://www.R-project.org/posting-guide.html
> > > >>
> > > >
> > > >
> > >
> > >
> >
> >
> >
> > --
> > Weiwei Shi, Ph.D
> >
> > "Did you always know?"
> > "No, I did not. But I believed..."
> > ---Matrix III
> >
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
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> >
> >
> >
> 
> 
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