search for: outlyingness

Dear help list, I think I found a bug a the R Random Forest. Hopefully, you are able to reproduce it. I use R version 2.7.2 and RF version 4.5-27. This is a minimal code to describe the problem: library(randomForest) tries <- 20 dimension <- 20 n <- 200 outlyingness <- rep(NaN,tries) for (o_number in 1:tries){ features <- matrix(rnorm(n*dimension,0,1),n,dimension) #Generate features, n uncorrelated normally distributed points outlier.rf <- randomForest(features, ntree=100, proximity=TRUE) #Compute Random Forest including the proximity matrix outlyi...

Hello, Does anybody know if the outscale option of randomForest yields the standarized version of the outlier measure for each case? or the results are only the raw values. Also I have notice that this measure presents very high variability. I mean if I repeat the experiment I am getting very different values for this measure and it is hard to flag the outliers. This does not happen with two other

Hello R-users, Is there any more sophisticated way how to identify the dataset outliers other then seeing them in boxplot? I wanna exclude them from further analysis and I am interested in their position in my vector data. Rado -- Radoslav Bonk M.S. Dept. of Physical Geography and Geoecology Faculty of Sciences, Comenius University Mlynska Dolina 842 15, Bratislava, SLOVAKIA tel: +421 2 602

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my