[BioC] pamr / R graphics recording

[Min-Han Tan]

Hi Katleen, 

I think I know why. You have to specify values for geneids and
genenames in your initial train.data, otherwise you get the error
message you reported for both pamr.plotcen and pamr.listgenes.

See following output as illustration:


> train.data<-list(x=exprData.clean,y=dx)
> pam.trained<-pamr.train(train.data)
123456789101112131415161718192021222324252627282930
> pamr.plotcen(pam.trained,train.data,threshold=5)
Error in text(xy.coords(x, y, recycle = TRUE), labels, adj, pos, offset,  : 
        zero length 'labels'


train.data<-list(x=exprData.clean,y=dx,geneids=rownames(exprData.clean),genenames=rownames(exprData.clean))
> pam.trained<-pamr.train(train.data)
123456789101112131415161718192021222324252627282930
> pamr.plotcen(pam.trained,train.data,threshold=5)
NULL


Regards,
Min-Han Tan
Van Andel Institute


On Sat, 15 Jan 2005 18:33:39 +0100, Katleen De Preter
<katleen.depreter at ugent.be> wrote:
> Hello,
> 
> I'm trying to use the pamr package on Affymetrix RMA normalised data.
> Pamr.train-function works on the data. However, I got following errors
> with pamr.plotcen and pamr.listgenes. Do anyone know what may be the
> problem?
> And another (R-related) question: how can I automatically (before
> starting a script) record all generated graphes???
> 
>  > pamr.plotcen(mData.train, mData.data, threshold=4.0)
> Error in text(xy.coords(x, y, recycle = TRUE), labels, adj, pos, offset,  :
>         zero length 'labels'
> 
>  > pamr.listgenes(mData.train, mData.data, threshold=4.0)
> Error in pamr.listgenes(mData.train, mData.data, threshold = 4) :
>         length of dimnames [2] not equal to array extent
> 
> used R-SCRIPT:
> library(pamr)
> library(affy)
> load("C:/Documents and Settings/Katleen/Mijn documenten/WERK/Analyse
> affymetrix/analyse ALLE CEL FILES/R/nbNBdata.RData")
> NBselect<-nbNBdata[,nbNBdata$classnameb%in%c("NB1","NB2B","NB2A")]
> classname <- pData(phenoData(NBselect))$classnameb
> mData.data <- pamr.knnimpute(list(x = exprs(NBselect), y = classname))
> mData.train <- pamr.train(mData.data)
> mData.results<- pamr.cv(mData.train, mData.data)
> pamr.plotcv(mData.results)
> pamr.confusion(mData.results, threshold=4.0)
> pamr.plotcvprob(mData.results, mData.data, threshold=4.0)
> pamr.plotcen(mData.train, mData.data, threshold=4.0)
> pamr.geneplot(mData.train, mData.data, threshold=5.3)
> pamr.listgenes(mData.train, mData.data, threshold=4.0)
> 
> Best regards,
> 
> Katleen
> 
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