similar to: Concerning the clustering tree

Dear List, I am trying to modify the xlab and ylab for a current figure that was plotted by a package, I searched through the low level plotting command and they do not seem to contain how to do this (the only way is to use xlab, ylab as arguments in "plot" command, which I can not do since the plot is plotted using some other package, not by my own script). Is there any command for

Hi, I must be missing something here...Essentially, a short piece of code works if it's standalone, but doesn't work if it's divided into two functions. The code that works is: ################### WORKS ############### library(pamr) set.seed(120) x <- matrix(rnorm(1000*20),ncol=20) y <- sample(c(1:4),size=20,replace=TRUE) mydata <- list(x=x,y=y)

Dear maintainers and R-devel, Several orphaned CRAN packages are about to be archived due to outstanding QC problems, but have CRAN and BioC packages depending on them which would be broken by the archival (and hence need archiving alongside). Therefore we are looking for new maintainers taking over maintainership for one or more of the following packages: R2HTML SemiPar cghseg hexbin lgtdl

Hello, I am writing an interface to some functions from the CRAN package pamr, which is poorly written. I have a S4 method I declared with setMethod. I'd like to provide a signature of "pamrtrained" which is the class of object that training creates. The last two lines of code of pamr.train are : class(junk) = "pamrtrained" junk How can I dispatch on these kinds

Hi, I am running R 2.2.0 on the Windows XP x64. The mechanism of error hanlder seems different. It will take a very long time to pop up a error message diaglog box, even when some simple errors happen such as "Syntax error" or "object xxxx not found". Does anybody have the similar experience? Thanks a lot. BTW: everything works fine under 32-bit Windows XP, error messages

Angus, I just found your posting to samba@lists.samba.org when I tried to find a solution for exactly the same problem you had. The problem arose when we migrated our M$ servers to w2k, with NT40 it worked fine. Question: As I found no answer to your question in the samba-list, did you find a easy solution (besides possibly upgrading the smb suite) Regards, Walter Fischer IT Manager FPD

Dear all, i searched for some classification methods and I have no glue if i took the right once. My problem: I have a matrix with 17000 rows and 33 colums (genes and patients). The patients are grouped into 3 diseases. No I want to classify the patients and for sure i want to know which rows are more helpful for the classification than others. I tried SVM and random forest. Do you think this

Art (and group), I'm doing this as a form of missing value analysis. Approximately 30% of the cases are missing data for one variable. To impute values for those cases, I'd like to match those cases that are missing the variable to all other cases and then take an average of those to infill. I realize there are many methods for imputing data. I'm not well versed on any in

I've started to implement checks for package versions on dependencies in install.packages(). However, this is revealing a number of problems/misconceptions. (A) We do not check versions when loading namespaces, ahd the namespace registry does not contain version information. So that for example (rtracklayer) Depends: R (>= 2.7.0), Biobase, methods, RCurl Imports: XML (>=

This group impresses me, so far I have been helped with all my questions within 24 hours. Thanks. Therefore another one. I am used to programs (such as STATA) where observations with missing values that are included in a model are simply ignored in the analysis. So far I have not been able to figure out how to deal with missing values in R and have solved the problem by deleting observations

Hi, I was working on a classification problem using the pamr package. I used the pamr.adaptthresh() function to find the optimal accuracy of the classifier. I must not be doing it right, since it doesn't return the threshold values for optimum classification. For example,if I run it on a dataset, I get the following result using pamr.adaptthresh(): predicted true

Hi, I was working on a classification problem using the pamr package. I used the pamr.adaptthresh() function to find the optimal accuracy of the classifier. I must not be doing it right, since it doesn't return the threshold values for optimum classification. For example,if I run it on a dataset, I get the following result using pamr.adaptthresh(): predicted true (1)

Hi, Sorry about the earlier formatting errors... I was working on a classification problem using the pamr package. I used the pamr.adaptthresh() function to find the optimal accuracy of the classifier. I must not be doing it right, since it doesn't return the threshold values for optimum classification. For example,if I run it on a dataset, I get the following result using

Hi, I have tried some time trying to figure out how to use pamr to plot multiclass Estimated probabilities for the training data and test data? Specifically, how to recreate the PAMR publication on PNAS with Tibshrani et al. The publication is as attached. The plot I want to do is Figure 5. I have downloaded the pamr package and the function which gives similar plot is pamr.plotcvprob

1. According to the doc of PAMR, the shrinkage threshold is determined by cross-validation. Does this mean that user need not tune any parameter? 2. I tried two applications using PAMR, the results are very disappointing. The attached are the cross-validation results. You can see that the classification errors are relatively high (0.2 at the best), in the case of two categories classification,

Hello, I have binary labels from a nearest shrunken centroids prediction (package pamr). I need to obtain a ROC curve for this test. What is the easiest way to obtain one? Paolo Sonego

I have recently upgraded to Ubuntu Gutsy and, for the first time, am using a 64-bit installation. After failing miserably to install R from source, not a problem for me in the past with a 32-bit install, I went the route of using the Debian Etch build. This went smoothly, but I am unable to update my numerous R and BioConductor packages, getting non-zero exit status errors on each package. Is

I am working with microarray analysis and was using PAM with excel interface. Is there a way to do random divisions for the training set in excel? I also tried PAM in R with the pamr menu. How can I do the random divisions in R? Then I tried to reproduce "classification with gene expression data", which is chapter 24 from the book "bioinformatics and computational biology

Dear all, i have microarray data of 3 classes of patients. It's not a time course experiment only steady state. I used a rule-based method to classify the groups by the expression of the genes. This works out so far. Nevertheless I want to check my results with an other method. Therefore I look for one and want to ask you, what you suggest. I have 3 different patient groups, only the steady

Hello, I'm currently using MCRestimate package and I have a question regarding the MCRestimate function. Here is my code: NestedCV.rf<-MCRestimate(eset, "Class", classificatin.fun="RF.wrap", variableSel.fun="varSel.highest.var", poss.parameters= list(var.numbers=c(100), mtry=c(10,50), cross.outer=10,cross.inner=10,cross.repeat=3) I'm pretty sure that I