similar to: Normal behavior or bug?

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 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:

I'm running a four-group, 15-variable discriminant analysis using the MASS function lda(). Discriminant functions 1 and 2 explain about 93% of the variance. I would like to plot the group scatters in a single scatterplot, identify each group's centroid, and encircle the group scatter with a 90% bivariate confidence ellipse. I can do this for each group on a separate plot, but I

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'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'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

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 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

I'm trying to develop a systematic way of placing legends on (among other things) scatterplots of grouped data. The journals in my field can be very fussy about the placement of figure legends. Typically they want them to be in either of two places - the upper left corner of the plotting area, not obscuring any data points, or outside the plotting area in the lower right hand corner of

I'm not really sure this is the place to raise this, but here goes anyway. I've downloaded the 1.4.0 recommended package sources R-1.4.0-recommended.tgz multiple times from the main CRAN site. The sources have arrived corrupted each time (from multiple computers BTW). If I download the same file from the UCLA or University of Bristol mirror, I also get corrupt archive files. On the

Forgive this naive question, but I'm not accustomed to using Win ports of Unix tools (actually I'm unfamiliar with Unix tools, period). Anyway, I've successfully compiled the R-1.1.1 sources into the full binary package. Everything seems to work. I'd like to remove all the "made" files and leave only the original source code and the compiled binary code. Is there

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

Thanks to all who offered suggestions for my question of how to "remove" row.names. As Bill Venables patiently pointed out, row.names are part of the object-oriented design of the S language (all dialects). As a result, there is no way to "literally" remove row.names and still have a data.frame. This was a useful thing to have Bill point out. In fact, my problem was