similar to: R 2.12.1 is released

Hi, A colleague recently came across an R crash, which I can boil down to the following, running under Rgui on Windows 7: library(ggplot2) ggplot(data.frame(x=1, y=1, z=4.7), aes(x, y, z=z)) + stat_summary2d() This reliably causes a segmentation fault. sessionInfo() below. What's happening is that (for reasons which I'll discuss with the ggplot2 developers) the 'colorbar'

Sorry to bother you kind folks again with my questions. I am trying to learn as much as I can about all this, and I will admit that I don't have the proper background, but I hope that someone can at least point me in the correct direction. I have created a test matrix for what I want to do: s1 s2 s3 s4 s5 s1 10 5 0 8 7 s2 5 10 0 0 5 s3 0 0 10 0 0 s4 8 0 0 10 0 s5 7

Dears, I have a question about copying reference objects using the initialize method. 1) If the latter has no arguments, there is no problem to copy an object. myClass = setRefClass("myClass", fields = list(value = "numeric") ) myClass$methods(initialize = function(...){ ? value <<- 1 ? callSuper(...) }) newObject = myClass$new() newObject$value = 2 copyObject =

Dear all, I'm getting more and more frustrated with the whole S4 thing and I'm looking for a more advanced summary on how to deal with them. Often I get error messages that don't make sense at all, or the code is not doing what I think it would do. Far too often inspecting the code requires me to go to the source, which doesn't really help in easily finding the bit of code

Dear R developers, there appears to be a small problem with function cmdscale: for non-Euclidean distance matrices, using option add=FALSE (the default), cmdscale misses the smallest eigenvalue. This affects GOF statistic g.1 (See Mardia, Kent + Bibby (1979): Multivariate Analysis, eq. (14.4.7). The corresponding formula in Cox + Cox (2001): Multidimensional Scaling, 2nd ed., p 38, would

If do: > library("e1071") > example(svm) I get: svm> data(iris) svm> attach(iris) svm> ## classification mode svm> # default with factor response: svm> model <- svm(Species ~ ., data = iris) svm> # alternatively the traditional interface: svm> x <- subset(iris, select = -Species) svm> y <- Species svm> model <- svm(x, y) svm>

Hello all, I'm using R1.7.1 on Linux, generating sammon-optimized MDS plots from distance matrices. This is a calculation I run routinely, often on sample sets of up to 100 samples. This time, with three samples, the sammon function returned an error (shown below), which I tracked down to the cmdscale function it uses to find a starting configuration. In short, cmdscale is returning NaN

In applying multidimensional scaling, it seems to me that sometimes the underlying dimensionality of the matrix is 1. However I found a case where cmdscale failed when I tried k=1. Here it is: m<-matrix( c(.5,.81,.23,.47,.61, .19,.5,.06,.17,.28, .77,.94,.5,.74,.85, .53,.83,.26,.5,.64, .39,.72,.15,.36,.5), nrow=5) # BTW I think cmdscale uses only the lower triangle--how to enter only # that

The function cmdscale tries to copy names from the source to the result. This only works if the source is a matrix. If m is a matrix with labels (rownames) and d is an object of class "dist" with labels, this works: cmdscale(m) ...but with this, there are no labels in the results: cmdscale(d) However, this works: cmdscale(as.matrix(d)) My suggestion is to change, in

Dear people, I used clusplot to plot a partition result. The partition result was from pamk with a distance object as input. Then I applied cmdscale on the same distance object for coordinates to make another scatterplot. My problem is this: the coordinates from the cmdscale calculation, though with the same shape, were different in scale and rotation from the scatter plot yielded by clusplot.

Hi We have a few questions regarding the use of the "isoMDS" function. When we run "isoMDS" function using 60,000 x 60,000 data matrix, we get the following error message: ------------------------------------ cmdscale(d, k) : invalid value of 'n' Calls: isoMDS -> cmdscale ------------------------------------ We checked the source code of "cmdscale" and

I get an error when using self-defined (not standard) functions with mapply with S4 objects from the raster package that I develop: "Error in as.character(sys.call(sys.parent())[[1]]) : cannot coerce type 'closure' to vector of type 'character'". Does anyone understand why? The problem is illustrated below. Thanks, Robert > # First a general example that works

Hi, everybody! i created a imagem by kriging using geoR package. I imported points from GRASS("zn", after converted to geodata "zn_geo"), the border "zn_border" and a raster mask. Then i interpolated the points by kriging and created a raster image. Now, i need export this image back to GRASS to use it in the module r.mapcalc. I can't do it. I tried use

Full_Name: Cam Webb Version: 1.6.0 (fink X11 compile) OS: Mac OS X (Jaguar) Submission from: (NULL) (64.168.28.87) This is an unpredictable, intermittant hang during cmdscale of the mva library. Some data never cause a problem, other data always do, abut I can't track down the difference in the structure of the data. Sometimes the function will work for `difficult' data after it has

I have 12 binary (raster) files https://echange-fichiers.inra.fr/get?k=k3M2jatJyHy65Cs99G4 . I would like to calculate the moving average for the 12 values for each pixel in the 12 files. For a simple vector we can get a moving average by using this : x <- c(1,2,3,NA,NA,4,6,5,6,4,2,5) movingmean <- rollapply(x, 3, FUN = mean, na.rm = T) now I want

hi, i have just begun using R, so please bear with me. i am trying to use cmdscale and display the result. i read the data using read.table(), calculate the proximity matrix using dist() and the display the result using the cmdscale(). this is very fine. in addition, i want the display to distinguish between two classes of records in my data. i have my data records marked as "1" or

The following problem occurs in R 1.4.0 and 1.3.1 for Windows95, but not in R 1.2.0 for Windows95. The problem does not occur in R 1.4.0 for Linux PC, Linux Alpha and HP-UX. Sometimes, the type of 'Size' of an object of type 'dist' changes from integer into double. Running cmdscale on such a 'dist' object gives invalid results. I don't know what should be considered

I'm looking for someone to explain the difference between these procedures. The function prcomp() does principal components anaylsis, and the function cmdscale() does classical multi-dimensional scaling (also called principal coordinates analysis). My confusion stems from the fact that they give very similar results: my.d <- matrix(rnorm(50), ncol=5) rownames(my.d) <-

Hello, I have a dilemma that I'm hoping the R gurus will be able to help resolve. For background: My data is in the form of a (dis)similarity matrix created from taking the inverse of normalized reaction times. That is, each cell of the matrix represents how long it took to distinguish two stimuli from one another-- a square matrix of 45X45 where the diagonal values are all zero (since this

I've rolled up R-2.12.0.tar.gz a short while ago. This is a development release which contains a number of new features. Also, a number of mostly minor bugs have been fixed. See the full list of changes below. You can get it from http://cran.r-project.org/src/base/R-2/R-2.12.0.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. Binaries for various platforms will appear in