similar to: dist{amap} error??

I discovered recently that the phrase "Oja median" produces no hits in Jonathan Baron's very valuable R search engine. I found this surprising since I've long regarded this idea as one of the more interesting notions in the multivariate robustness literature. To begin to remedy this oversight I wrote a bivariate version and then decided that writing a general p-variate version

Dear R users, Thanks to a new contributor, Luigi Cerulo, amap provides 2 more metrics, and another agglomeration linkage for distance computation, and hierarchical clustering. The new centroid linkage method implemented in the cluster3 software tool (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm). In this method the pseudoitem of a cluster is computed as the average of the items

Dear R users, Version 0.7-1 of the amap package has been uploaded to CRAN. Amap package includes standard hierarchical clustering and k-means. We optimize implementation (with a parallelized hierarchical clustering) and allow the possibility of using different distances like Eulidean or Spearman (rank-based metric). We implement a principal component analysis (with robusts

A new major version of Amap package is available on CRAN. For this major release, all clustering code has been rewritten in C++. Amap implements several tools in the field of clustering and robust statistics. New features are: * clustering possible in float precision (less memory needs) * new rank-based metric: Kendall distance, use for both matrix distance computation, K-means, and

Hi there dear R users, Does anyone have any idea what the following error means and how to sort it out? Runtime Error! Program: C\Program Files\R\R-2.9.0\bin\Rgui.exe This application has requested the Runtime to terminate it in an unusual way. Please contact the application’s support team for more information. Here is the story: Running the same code, sometimes the error happens and other

Hi, I'm trying to understand the complete linkage method in hclust. Can anyone provide a breakdown of the formula (p9 of the pdf documentation) or tell me what the "sup" operator does/means? thanks in advance Tom [[alternative HTML version deleted]]

The compositional data xi=(x_i1,x_i2,...,x_in), for each fixed i , xij>0, and sum(xij)=1; I want to compare the mean( u_i) of several groups i.e. H0: u_1=u_2=...=u_N or H0: u_11=u_21=...=u_N1 Are there any ANOVA tpye tools to do this work in R? Thanks, WEN S Q [[alternative HTML version deleted]]

The compositional data xi=(x_i1, x_i2,..., x_in), for each fixed i , xij>0, and sum(xij)=1; I want to compare the mean( u_i) of several groups i.e. H0: u_1=u_2=...=u_N or Hj0: u_1j=u_2j=...=u_Nj Are there any ANOVA tpye tools to do this work in R? Thanks, WEN S Q [[alternative HTML version deleted]]

On 12/04/2013 12:38 AM, Christian Hesse wrote: > > Sure. Here we go: > > boot: debug -e bios_boot_linux calc_cmdline_offset > boot: ipxe.lkrn > Loading ipxe.lkrn... ok > cmdline_offset at 0x9f7e0 > Initial memory map: > Could not find location for protected-mode code > Booting kernel failed: Invalid argument > OK, that was a useful clue. I think I know what is

Dear R users, When I run the following code, R crashes: require(cclust) x <- matrix(c(0,0,0,1.5,1,-1), ncol=2, byrow=TRUE) cclust(x, centers=x[2:3,], dist="manhattan", method="kmeans") While this works: cclust(x, centers=x[2:3,], dist="euclidean", method="kmeans") I'm posting this here because I am not sure if it is a bug. I've been searching

Some kernel images use the Linux kernel boot protocol and header structure, but do not actually have any protected-mode code. For instance, grub's 1024-byte lnxboot.img consists of 1024 real-mode bytes and 0 protected-mode bytes; you can concatenate it with a full grub core.img to produce a self-contained bootable kernel, but you can also use it standalone as the kernel with the core.img

On Fri, Jun 10, 2016 at 2:06 AM, Adam Goldman via Syslinux <syslinux at zytor.com> wrote: > PXELINUX has stopped recognizing the keeppxe option on the kernel > command line. Here is a patch to make it work again. > > With COM32, it is no longer possible to use preprocessor directives to > determine the SYSLINUX variant. The code inside the #if will never be > compiled. So,

Hi, Seeking suggestions to compute uncentered (pearson correlation) correlation efficiently. corr from stats library works on x and y columns. dist from amap library works on x and y rows. My data layout is slightly different such that row(i) of matrix x is compared to row(i) of matrix y. Thanks [[alternative HTML version deleted]]

Dear r-helpers! I want to classify my vegetation data with hierachical cluster analysis. My Dataset consist of Abundance-Values (Braun-Blanquet ordinal scale; ranked) for each plant species and relev?. I found a lot of r-packages dealing with cluster analysis, but none of them is able to calculate a distance measure for ranked data. Podani recommends the use of Goodman and Kruskals' Gamma for

This patch is just a (shameless) copy from bug #28 [1]. The original patch has been sitting there for a couple of years now, and it was based on Syslinux v 4.05. The patch I am posting here is just a re-base on v.6.03. Whichever inadequate formatting (tabs, trailing spaces...), or any kind of correction that was required for the original patch to be accepted, is probably still required now.

Hi all, once more about the old subj :-) My data has too much various distribution families and for every particular experiment I need just to decide whether the data is "quite homogeneous" or it has two or more clusters. I've revisited the following libraries: amap, clust, cclust, mclust, multiv, normix, survey. And I didn't find any ready-to-use general

We've added a column at http://cran.r-project.org/web/checks/check_summary.html of test results using the Sun Studio compiler: it is intended that these will be updated weekly. The Sun Studio compiler is that used on Solaris: these runs were on the Linux version. All the other platforms are using gcc 4, so this provides an opportunity for checking for use of gcc-specific features and

Hi, I misunderstand the definition of Canberra distance in R. On Internet and in function description pages of dist() from stats and Dist() from amap, Canberra distance between vectors x and y, d(x,y), is : d(x,y) = sum(abs(x-y)/(x+y)) But in use, through simple examples, we find that the formula is : d(x,y) = (NZ + 1)/NZ * sum(abs(x-y)/(x+y)) with NZ = nb of pairs of coordinates that are

I am trying to use Fisher's z' transformation of the Pearson's r but the standard error does not appear to be correct. I have simulated an example using the R code below. The z' data appears to have a reasonably normal distribution but the standard error given by the formula 1/sqrt(N-3) (from http://davidmlane.com/hyperstat/A98696.html) gives a different results than sd(z). Can

I've got 1.9.18p10 up and running happily on a Solaris 2.4 box. I have got the configuration set so that from my own PC (running NT4, sp4) on the same subnet, I can login with no problems. Running smbclient on the Unix box also confirms that I can use other user ID's also. Problem 1: other users do not seem to be able to login. What I think are the relevant sections of the config file