similar to: Amap new release

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

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

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

Dear all, I have come across a very confusing matter regarding dist() supplied by the amap package: --- m is just a test matrix > library(amap) Loading required package: mva Warning message: package 'mva' has been merged into 'stats' > m a b c aa 0.1 0.2 0.3 bb 2.0 3.0 4.0 cc 2.0 4.0 6.0 dd 0.3 0.2 0.1 > ds<-dist(m,method="pearson") >

Hi. I am wondering if anybody has studied and/or written code in R to calculate the distance between 2 "trees". For example, if one does a hierarchical agglomerative clustering and say, a hierachical divisive clustering (represented as trees) and wishes to compute a metric on them. I am thinking of something like the symmetric difference as mentioned in Margush and McMorris (1982).

Colleagues, I ran some nonparametric regressions in R (run in RedHat Linux), then a colleague repeated the analyses in SAS. When we obtained different results, I tested S-Plus (same Linux box). And, got yet different results. I replicated the results with a small dataset: DATA: 37.5 23 37.5 13 25 16 25 12 100 15 12.5 19 50 20 100 13 100 10 100 10 100 16 50 10 87.5

Dear SIr, In case of looking at the codes of the fuction, "cov", we find all the codings below. But, incase of "mean", we don't find the contents. Please show me the way to look at all the codings of any functions. Best regards, Kei ----------------------- > cov function (x, y = NULL, use = "all.obs", method = c("pearson",

Dear R maintainers, R is great. Now that I have that out of the way, I believe I have encountered a bug, or at least an inconsistency, in how Spearman and Kendall rank correlations are handled. Specifically, cor() and cor.test() do not produce the same answer when the data contain NAs. cor() treats the NAs as data, while cor.test() eliminates them. The option use="complete.obs" has

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]]

Full_Name: Dirk Koschuetzki Version: 2.1.1 OS: source code Submission from: (NULL) (194.94.136.34) Hello, >From the source code (R-2.1.1, file: .../R-2.1.1/src/library/stats/R/) ****************************** cor.test.default <- function(x, y, alternative = c("two.sided", "less", "greater"), method = c("pearson", "kendall",

I can't hardly accept the result of cor function with pairwize.colplete.obs or complete.obs insert print statements in cor function, + if (method != "pearson") { + Rank <- function(u) if (is.matrix(u)) + apply(u, 2, rank, na.last = "keep") + else rank(u, na.last = "keep") + x <- Rank(x) +

Dear R developers, Currently many (all?) test functions in R describe the alternative hypothesis, but not the the null hypothesis being tested. For example, cor.test: > require(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="kendall")) Kendall's rank correlation tau data: mpg and wt z = -5.7981, p-value = 0.000000006706 alternative hypothesis: true tau is not

In some contexts, I find the current behavior of rank() very `suboptimal'. We have the argument na.last = {TRUE | FALSE | NA } where the first two cases treating NAs (almost) as if they were == +Inf or == -Inf whereas the 3rd case just drops NAs. For the typical ``Rank Transformation'' that is recommended in EDA in several contexts, I would however want something else, namely keep

I'm sure this is a simple task, but how to do it has escaped me. I have imported data from two separate files (each file contains the results from an information retrieval algorithm) organized into a list. They are organized by File,Query, and Rank (in that order): [[1]] Doc Query Rank 5 1 1 9 1 2 7 1 3 5 2 1 7 2 2 9 2 3 [[2]]

Hello, I wuold like to propose my modifications of the original cor.test to you : I tried to calcolate the correct p-value for Spearman and Kendall's test with ties. Let me know what you think. Thanks you for your time. Antonietta di Salvatore test <- function(x, ...) UseMethod("test") test.default <- function(x, y, alternative = c("two.sided",

Hi, R! Question1: I am trying to correlate two vectors of numbers (two columns of microarray signal values) by using the non-parametric Spearman's rank correlation coefficient rho: > cor.test(V2.Signal,V3.Signal,method="spearman") but I get the error message: Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tail = FALSE) else pspearman(q, : missing value

Full_Name: Marek Ancukiewicz Version: 2.10.1 OS: Linux Submission from: (NULL) (74.0.49.2) Both cor() and cor.test() incorrectly handle ordered variables with method="kendall", cor() incorrectly handles ordered variables for method="spearman" (method="person" always works correctly, while method="spearman" works for cor.test, but not for cor()). In

Dear Rexperts, First of all let me say that R is a wonderful and useful piece of software. The only thing is that sometimes it takes me a long time to find out how something can be done, especially when aiming to write compact (and efficient) code. For instance, I have the following function (very rudimentary) which takes a (very specific) data frame as input and for certain subsets

> From: Siegfried.Macho > > Dear R-helpers, > > 3 questions: > 1. Is there a package that contains a routine for computing > Kendall's W > (coefficient of concordance), with and without ties ? Is that the same as Kendall's tau, as in cor(..., method="kendall")? > 2. Is there a package that contains a routine for computing > Goodman' s