similar to: Graph superimposition

I've been using the package rpart with R 1.3.0 for Windows to produce simple classification trees for some measurement data from paleontological specimens. Both the rpart documentation and the output confirm that the program produces splits on continuous data that leave "holes" in the data. It is probably of little practical importance, but is there a reason why the binary

I just downloaded version 1.0.0 and several binary libraries (VR, rpart, norm, stataread) - WinNT version. I then converted a file from Stata 6.0 to R format by using the stataread library. The file converts perfectly and I was able to use the VR function lda on the dataframe without difficulty. I then tried to use the same dataframe with RPART. The model statement:

At the risk of starting a holy war, I'd like opinions on the "best" current Linux distribution to run "out of box" with current versions of R. I note rpms for various versions of RedHat, SUSE, Mandrake, and Debian. I've had experience with RH 7.1 & 7.2 and it hasn't been entirely pretty. I understand that 7.3 is a bit "better", but have also

I'm trying to adapt some S-Plus scripts to run in R (1.2.2, Windows). In one of these scripts, I've bootstrapped the prediction success rate under the discriminant function (lda). The bootstrap() functions are proprietary to S-Plus and there aren't exact equivalents in R. The closest is Canty's library boot based on the Davidson and Hinkley book. Unfortunately, I

David Brahm's initial post seems to me to be filled with a mixture of bugs, wants, and needs coupled with a desire to "make" R become S-Plus. David, if S-Plus works for you, why are you bothering to convert all your code to R? No one has ever asserted that R==S (or S-Plus), and there is no reason to expect this equality to hold, ever. R is open source. If you want to change

Before I reinvent the wheel, I have need for a relatively straightforward crosstabulation (2 x n) function. I know that R has table(), ftable(), xtabs(), and summary(xtabs()), but none of these produce a fully "tricked" out cross-tabulation with marginal totals, expected cell frequencies, and an array of statistics about the contingency table. Is there a more complete (something

I frequently get data sets with cryptically-named variables. The datasets are more useful to me with informative variable names. I know that I can rename variables using the following command: dimname(dataset[[2]][index.of.variable.to.be.renamed]<-new.variable.name If I want to do this inside a function (say something I call RenameCol) what is the best way to communicate the

>selectiris<-sample(1:5, 150, replace=T) > hist(selectiris, plot=TRUE) #freq=TRUE, breaks=5, etc all do the same thing >truehist(selectiris) Is the behavior of "hist" in this sample of code correct. On my system (Windows 2K, SP2) the first and second bars are pushed together, while the 3rd, 4th, and 5th bars are correctly positioned. The function "truehist"

I'm in the final week of teaching a course to beginners using R and S-Plus. Since I forbade the students from using the "point-and-click" interface in S-Plus, they've become reasonably proficient at using the command line in both programs. Students really like the speed and ease-of-use of R for the vast majority of their projects. I think they'd switch to R completely

I'm running a series of simple bivariate linear regressions on grouped data. I want to test the slopes to see if they are parallel. I normally use analysis of covariance to do so, looking at interaction between the covariate and the factor to make this determination. VR3 pp.149 - 154 has a very nice example of an ANOCOVA, ending with a discussion of this very operation. My question has

Sorry for the uninformative subject. The following piece of code gives different output in S-Plus (2K & 6) vs R 1.2.1 (Win) apply(apehum[,6:15], 2, function(x) which(is.na(x))) S-Plus results: > apply(apehum[,6:15], 2, function(x) which(is.na(x))) $latsupri: numeric(0) $medepico: numeric(0) $pdhtcapi: numeric(0) $mlhtcapi: numeric(0) $aphttroc: [1] 151 152 $mlhttroc: numeric(0)

I just downloaded the binaries for the released 1.2.0 for Windows (from Guido Masarotti's site). In using the rwinst.exe installer, I noted two significant problems. First, the installer seems extraordinarily slow compared to the the 1.1.1 installer. It took almost 20 minutes to perform a full base extraction from binaries to installation (all base pages, html help, windows help, pdf

My earlier message was not as clear as it could have been. I already have Emacs for my NT system, but prefer not to use it or vi. It is really a case of not needing a sledgehammer to swat a gnat. The advantage of EditPadPro (for me anyway) is that it has a footprint of 1MB including helpfiles, whereas the versions of Emacs I've seen for NT require about 30MB of disk space to store.

Thanks to those who suggested I use make clean. I *had* done that before asking the question, but it would never run. I kept getting: nothing to make. After getting a couple of replies suggesting "make clean" again, I blew away the entire directory, reinstalled the sources, reset the paths, and first did a normal "make". Then I followed that with "make

I want to produce some boxplots (and qqnorm plots) for each of a series of groups. It is easy to produce the boxplots or qqplots by groups using either by() or tapply(), but the moment that I want unique labels for each boxplot, I seem to be stuck using a for loop to achieve the effect. For example: tapply(fstat$femur, fstat$race, boxplot) by(fstat$femur, fstat$race, boxplot) gives nicely

I've run into another postscript/rpart problem unrelated to the issues I've mentioned in a previous query. I'm using 1.3.1 on a Win2K box. >plotcp(some.rpart.object) draws a very nice plot on the windows graphic device. If I save this as a postscript file, either by opening a postscript device before calling plotcp, or by saving the graphics window as a postscript file, the

I'm trying to figure out whether there is a simple one or two-pass approach to randomly creating missing values for a set of existing (complete) data. For example, I want to randomly make 10% of the entries in the Iris dataset missing (i.e. NA). I don't want any case to have all missing values and I don't want any case to be missing the classification variable. I can do this in

I've tried to download the Windows 1.7.0 binary from three different mirrors today. Each time the binary comes down corrupt - MD 5 sums don't check and the installer reports that its own setup files are corrupt. Since I've tried from different computers and different connections, I have to assume a problem downstream from me. Has anyone else reported this problem? Has the binary

I have a matrix (1000 x 224) that contains the output from the function boot.array. For each row, there are 224 columns that represent the case numbers of the selected individuals from a bootstrap. The cases are listed in selection order, not numerical order. I'd like to rearrange the cells so that every row has the column entries listed ascending numeric order. I've been able to

I'm doing kmeans partitioning on a small (n=26) dataset that has 5 variables. I noticed that if I repeatedly run the same command, the cluster centers change and the cluster membership changes. Using RW1022 under Windows NT & Windows 2000 >kmeans(pottery[,1:5], 4, 20) [...snip] $size [1] 7 3 9 7 [...snip] $size [1] 7 10 4 5 [...snip] $size [1] 6 10 5 5 yields a different