search for: hartigans

Hi all, I am using Hartigan & Hartigan's [1] "dip test" of unimodality via the diptest package in R. The function dip() returns the value of the test statistic but I am having problems calculating the p-value associated with that value. I'm hoping someone here is familiar with this process and can explain it. In the original article there is an example using n=63 and a

Hi, I just got a value for the dip test out of my data of 0.074 for a sample size of 33. I'm trying to work out what this actually means though? Could someone help me relate this to a p-value? Thanks James

>From library(diptest): Shouldn't the following almost always be non-significant for Hartigan's dip test? dip(x = rnorm(1000)) I get dip scores of around 0.0008 which based on p values taken from the table (at N=1000), using the command: qDiptab, are 0.02 < p < 0.05. Anyone familiar with Hartigan's dip test and what I may not be understanding? Thanks, kbrownk

Hi STATISTICA has a function called "Two-way joining" (see http://www.statsoft.com/TEXTBOOK/stcluan.html#twotwo) and the reference material states that this is based on the method as published by Hartigan (found this paper: http://www.jstor.org/pss/2284710 through wikipedia). What is the relationship (if any) between the "heatmap" function in R and this technique? Is there an

...uministrada por los test suponen la existencia o no de unimodalidad/multimodalidad. Una parte de la salidad de diptest es la que pego a continuación (el resto esta en el fichero adjunto con las distribuciones kernels y las soluciones gráficas). *Distribución #1(suponía que hay más de una moda)* Hartigans' dip test for unimodality data: Datos8$V2 D = 0.0432, p-value = 0.3501 alternative hypothesis: non-unimodal, i.e., at least bimodal *Distribución #2* dip.test(Datos9$V2) Hartigans' dip test for unimodality data: Datos9$V2 D = 0.0667, p-value = 1.535e-06 alternative hypo...

Dear R users, I am interested in uni- bi- multimodality tests, for analysing reaction times data. I was lead to Hartigan's dip test (Ann. Statistics, 13, 1985, pp. 70-84, Applied Statistics, 34, 1985, 320-325). Not being a programmer I am unable to translate the Fortran code given in ref. 2 into a R function. I'd be glad to learn that someone already did it, or has devised a better

Hi, I'm wondering if anyone can help me. I'm writing java application that using JRclient and Rserve to communicate with R. I want to get response from R for command: x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)) kmeans(x, 2, 22, algorithm = "Hartigan-Wong") and put result of kmeans() function in my TextArea.I've

Hello. This is not a question about a bug or even best practices; rather I'm trying to understand the philosophy or theory as to why certain portions of the R codebase are written as they are. If this question is better posed elsewhere, please point me in the proper direction. In the thread about the issues with the Tukey line, Martin said [1]: > when this topic came up last (for me) in

hi r-help, i am doing kmeans clustering in stats. i tried for five clusters clustering using: kcl1 <- kmeans(as1[,c("contlife","somlife","agglife","sexlife",                         "rellife","hordlife","doutlife","symtlife","washlife",                       

Dear All,i applied "NbClust", to my data to find optimum number of clusters, and got following resultsNow, i don't know how to read these results. more precisely, i would like to know, how to see which method is more precise for my data considering these index values.your help is needed...thanks in advance Eliza Botto > dput(Eliza)structure(list(All.index = structure(c(2, 3, 4, 5,

Hi. I am trying to modify kmeans function. It seems that is failing something obvious with the workspace. I am a newbie and here is my code: myk = function (x, centers, iter.max = 10, nstart = 1, algorithm = c("Hartigan-Wong", + "Lloyd", "Forgy", "MacQueen")) + { + do_one <- function(nmeth) { + Z <- switch(nmeth, { + Z

Dear experts, I am encountering a version-dependent issue. My laptop runs Ubuntu 12.04 LTS 64-bit, R 2.14.1; the issue explained below never occurred with this version of R My desktop runs Ubuntu 11.10 64-bit, R 2.13.2; what follows applies to this setup. The data I'm clustering is constituted by the rows of a 320 x 6 matrix containing integers ranging from 1 to 7, no missing data. I applied

Hello, here is a working reproducible example which crashes R using kmeans or gives empty clusters using the nstart option with R 15.2. library(cluster) kmeans(ruspini,4) kmeans(ruspini,4,nstart=2) kmeans(ruspini,4,nstart=4) kmeans(ruspini,4,nstart=10) ?kmeans either we got empty always clusters and or, after some further commands an segfault. regards, Detlef Groth ------------ [R] Empty

Hi there, I'm doing some clustering analysis and try to find all the algorithms related to clustering in R. Here is the list of the algorithms I found. But I'm not sure if It's the complete list. Could you please check it and see if there're other ones? Thank you very much! P.S.: List of the algorithms related to clustering: (1) Hierarchical methods hclust

Hi All, A question from a newbie using R 2-5-0 on windows XP. Why is it that kmeans clustering with apparently the exact same parameters behaves so differently between the two following examples : > cl1 <- kmeans(subset(pointsUXO15555, select = c(2:4)), 10) Takes about 2 seconds to deliver a result > cl1 <- clust(subset(pointsUXO15555, select = c(2:4)), k=10,

Hello all, I've been comparing results from kmeans() in R to PROC FASTCLUS in SAS and I'm getting drastically different results with a real life data set. Even with a simulated data set starting with the same seeds with very well seperated clusters the resulting cluster means are still different. I was hoping to look at the source code of kmeans(), but it's in C and FORTRAN and

I'm not sure where to begin with this, but I was wondering if someone could refer me to an R package that would test to see if a distribution fits a bimodal distribution better than a unimodal distribution. Thanks, Andrew [[alternative HTML version deleted]]

Here are three reasons for converting Fortran code, especially older Fortran code, to C: 1. The C-Fortran interface is not standardized. Various Fortran compilers pass logical and character arguments in various ways. Various Fortran compilers mangle function and common block names in variousl ways. You can avoid that problem by restricting R to using a certain Fortran compiler, but that can

Folks: I'm trying to port some code from python over to R, and I'm running into a wall finding R code that can solve a generalized eigenvalue problem following this function model: http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html Any ideas? I don't want to call python from within R for various reasons, I'd prefer a "native" R solution if one

Buenos dias, desde hace algunos dias estoy realizando un trabajo,mi computadora es una DELL, windows 10 64 bits, 8G de RAM y disco de estado solido, estoy procesando 29000 filas y 23 columnas, mi codigo es este: nb <- NbClust(datos.scaled, distance = "euclidean", min.nc = 2, #max.nc = 10, method = "complete", index ="all") . y mi error es este: Error: