similar to: distributional properties of rnorm

Thanks to all who replied to my query about the "normality" of rnorm. I am using R 1.1.1 (the version released last week) under Windows 98. It has been unusually hot here and I noticed that the CPU fan seemed to be running at high speed for a long time. The CPU probe that I use to monitor the temperature emitted a warning shortly after I sent off the email. After shutting my

I've experienced numerous "freeze ups" with R 1.1.0 in the past few days. I'm running on a Windows 98 (*not SE*) notebook with 128 MB RAM. Typically I have no other applications running when I load "R". The circumstances that frequently leads to the "freeze ups" is when I have done the following: >fix(somefunction) This spawns "Notepad",

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

The question was: > I have this 'odd' problem; I need to let R pause, for a given time, before > starting next iteration in a loop. I'm using the following to do this > task, but feel a little bit guilty because I'm using as much CPU time as I > can get while pausing: > > while(keepGoing) { > t.end <- proc.time()[3] + 5 ## the time this loop should end

Hello! I need to compare 2 datasets whether they come from the same distribution. I use function ks.boot{Matching}. And what is the confidence level of the p-value, returned by ks.boot function? The code is: set=read.table("http://stella.sai.msu.ru:8080/~gala/data/testsets.csv", header=T,sep=',') set1=set[!is.na(set$set1),'set1']

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 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'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'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

>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 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 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'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