similar to: FW: multivariate splits

Dear R users! Does someone know about any algorithms / packages in R, that perform classification / regression / decision trees using multivariate splits? I have done some research, but I found nothing. Packages "tree" and "rpart" seem only to be able to do CART with univariate splits. Thank you for your help! B?lint -- Cz?cz B?lint PhD hallgat? BCE KTK Talajtan ?s

The package mvpart is now available. mvpart includes partitioning based on (1) multivariate numeric responses and (2) dissimilarity matrices. The package mvpart is a modification of rpart -- -- authors of original: Terry M Therneau and Beth Atkinson <atkinson at mayo.edu>, and R port of rpart Brian Ripley <ripley at stats.ox.ac.uk>. Includes some modified routines from vegan --

The package mvpart is now available. mvpart includes partitioning based on (1) multivariate numeric responses and (2) dissimilarity matrices. The package mvpart is a modification of rpart -- -- authors of original: Terry M Therneau and Beth Atkinson <atkinson at mayo.edu>, and R port of rpart Brian Ripley <ripley at stats.ox.ac.uk>. Includes some modified routines from vegan --

Dear R community, as stated in Breiman et.al. (1984) and De'Ath & Fabricius (2000) using sums of absolute deviations about the median as an impurity measure gives robust trees. I would like to use this method in rpart. Has somebody already tried this method in rpart? Is there maybe already a script available somewhere? I am aware of the possibility to define usersplits myself with

Hello, As suggested in "De'ath, 2002. Multivariate regression trees: A new technique for modelling species-environment relationships. Ecology, 83 (4):1105-1117" (for those interested), I am trying to compare the performance of a multivariate regression tree to a cluster analysis. A simple partitioning with k clusters (as done by `pam`) seemed straightforward and appropriate

I have a question relating to mvpart, which I hope you can answer. We recently conducted a study using TBR. In our first study, we used "regular" TBR in SPSS to model 1 dependent variable. Note we have a relatively small data-set of 100 cases. In SPSS, we used a minimum change of improvement smaller than 0.000001 as a stopping rule. Also, we chose the 1SE "rule", set the

Dear R community, I am analyzing plant communities with the function mvpart, using a dissimilarit matrix as input. The matrix is calculated with the funtion gdist. fit <- mvpart(gdist (ba12[,18:29], meth="maximum", full=TRUE, sq=F) ~ beers + slope_dem + elev_dem+ plc_dem + pr_curv+ +curv+max_depth+doc_rocks+ abandon+land_use+ca_old, data=ba12, xv="p") This

Is there any R package which implements non-linear dimensionality reduction (LLE, ISOMAP, GTM, and so on) and/or intrinsic dimensionality estimation ? Thank you, Maura tutti i telefonini TIM! [[alternative HTML version deleted]]

Hi, Everyone, I want to calculate the principal curve of a points set. First I read the points'coordinate with function "scan", then converted it to matrix with the function "matrix", and fit the curve with function "principal.curve". Here is my data in the file "bmn007.data": 0.023603 -0.086540 -0.001533 0.024349 -0.083877 -0.001454 .. ..

Hi, I am trying to write my own split function for rpart. The aim is to do, instead of anova, a linear regression to determine the split (minimize some criterion like sum of rss left and right of the split). The regression (lm) should simply use the dependent and independent variables passed to rpart. I am aware of the example provided in the rpart source code, but stumbled on similar problems

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al

I download a csv extract from a database and use read.csv to read it from R and when there are large character fields with embedded blanks, slashes etc. - R often sees one line as two lines (or more). I verfied with readLines that an embedded blank in a character field causes a spurious new line to be seen. I am sure this problem has beem seen before - and would appreciate any help in reading

I am using the package rpart to explore various classification structures. The call looks like: seekhi1<-rpart(pvol~spec+a1+psize+eppres+numpt+icds+bivalcrt+stents+ppshare+ nhosp+nyrs,data=dat,method="class", control=rpart.control(minsplit=30,xval=10)) The output is 1) root 198 87 1 (0.5606061 0.4393939) 2) psize=1,2 122 43 1 (0.6475410 0.3524590)

Hi All,   I am currently trying to familiarize with "ff" package which allows me to store R objects in the hard drive. One of things that I notice when reading in a text file with "read.csv.ffdf" function - is that, in the R temp folder, 1000+ small files get created, each file having a name like "ffdf1bcd8b4aa0.ff". Each file is about 5KB in size.   My understanding

Hi, I have a vector like this: DF <- c("Aetna, Inc.", "Alexander's Inc.", "Allegheny Energy, Inc") For each element in the vector I would like to remove the "incorporated" info, so that my vector looks like this: DF <- c("Aetna", "Alexander's", "Allegheny Energy") That means that I have to strip: strip <-

On Wed 07 Jan 2015, Lenz Weber wrote: > Where the local destination /data/snapshots is an NFS volume mounted with the flags > (rw,noatime,addr=192.168.1.XX) > and the source is a symlink to a zfs snapshot - that looks like this: > /var/backups/mail -> /tank/mail/.zfs/snapshot/zfs-auto-snap_hourly-2015-01-07-1417 Why not skip the NFS part and run rsync to the destination

system(..., intern=TRUE) splits long lines after 118th character and discards the 119th character > a <- paste(rep("a", 124), collapse="") > system(paste("echo", a), intern=TRUE) system(paste("echo", a), intern=TRUE) [1] "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa

R helpers, I am working with the R program performing multivariate regression trees (MRT). I have a matrix with species and environmental variables saved as a CSV file (sprot_matrix.csv), I have 42 species and 8 environmental variables (SECCHI+PH+TA+PTOT+NTOT+CHLA+AREA+ MEANDEP) for 104 samples Title SpA SpB SpC SpD Varible1 Variable2 Variable3 Sample1 Sample 2

Hi everyone, I'm having trouble figuring out how to split a dataframe more than once. Let's say I have a dataframe d with a certain column called splitcol composed of four possible ordinal values. The same dataframe has two other columns, col1 and col2, that have one of two possible values each. I'd like to split d$splitcol based on col1 and col2 so I can report frequencies of the

Hi, I have a vector like: r <- runif(100) Now I would like to split r into 10 pieces (each with 10 elements) ? but the 'pieces' should be roughly similar with regard to mean and sd. what is an efficient way to do this in R? thanks!