search for: hartigan

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 ex...

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 alternative function to use? Thanks for the help! Schalk Heunis

...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 hyp...

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 solution for this kind of p...

...ndering 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 tried something like that: zz <- file("ex.txt", "w") capture.output(kmeans(x, 2, 22, algorithm = "Hartigan-Wong"), file = zz, append = TRUE) and then get "ex.txt" file from R and re...

...e much of the code was written before Fortran 90 compilers were freely available? Does it help with maintenance or make debugging easier? Is it faster or more likely to compile cleanly? Thank you, Avi [1] https://stat.ethz.ch/pipermail/r-devel/2017-May/074363.html [2] Such as kmeans does for the Hartigan-Wong method in the stats package [2] Such as the mvtnorm package does

...        "rellife","hordlife","doutlife","symtlife","washlife",                        "chcklife","rptlife","countlife","coltlife","ordlife")], 5, iter.max = 10, nstart = 1,          algorithm = "Hartigan-Wong")       table(kcl1$cluster) every time i am getting five clusters of different sizes like first time with cluster sizes table(kcl1$cluster)   1   2   3   4   5 140  72 105  98 112 second time with cluster sizes table(kcl1$cluster)   1   2   3   4   5  91 149 106  76 105 and so on. I wis...

..."2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15"), c("nc.Ward", "index.KL", "index.CH", "index.Hartigan", "index.CCC", "index.Scott", "index.Marriot", "index.TrCovW", "index.TraceW", "index.Friedman", "index.Rubin", "index.Cindex", "index.DB", "index.Silhouette", "index.Duda"...

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 <- .Fortran(R_kmns, as.double(x), as.integer(m), + as.integer(ncol(x)), centers = as.double(centers), +...

...as then suggested that R's behaviour was correct. I'm not familiar with such an early R version, but the up-to-date documentation of kmeans clearly states that "Except for the Lloyd-Forgy method, k clusters will always be returned if a number is specified.". I am using the default Hartigan-Wong, and I specify an exact number k: thus, k clusters should be returned. They aren't, and the empty cluster is then more likely the symptom of a bug rather than the outcome of a 'true' local minimum. Using synaptic, I managed to downgrade R to version 2.13.2. The problem disappeard,...

...at R's behaviour was correct. I'm not familiar with such > an early R version, but the up-to-date documentation of kmeans clearly > states that "Except for the Lloyd-Forgy method, k clusters will always be > returned if a number is specified.". > I am using the default Hartigan-Wong, and I specify an exact number k: > thus, k clusters should be returned. They aren't, and the empty cluster is > then more likely the symptom of a bug rather than the outcome of a 'true' > local minimum. > > Using synaptic, I managed to downgrade R to version 2.13.2....

...ms related to clustering: (1) Hierarchical methods hclust agnes diana mona mclust hopach (2) Partitioning methods pam: clustering around medoids clara: clustering large datasets fanny: fuzzy cluster analysis (3) k-means: bdCluster: big data k-means clustering kmeans: Hartigan's K-Means Clustering [[alternative HTML version deleted]]

...ith 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, method="kmeansHartigan") Dies after about 10 minutes and fills up RAM : *** running kmeansHartigan cluster algorithm... *** calculating validity measure... Erreur : impossible d'allouer un vecteur de taille 922.9 Mo De plus : Warning messages: 1: Reached total allocation of 1023Mb: see help(memory.size)...

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]]

...ran 90 compilers were freely available? Does it > help with maintenance or make debugging easier? Is it faster or more > likely to compile cleanly? > > Thank you, > > Avi > > [1] https://stat.ethz.ch/pipermail/r-devel/2017-May/074363.html > [2] Such as kmeans does for the Hartigan-Wong method in the stats package > [2] Such as the mvtnorm package does > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel [[alternative HTML version deleted]]

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: