similar to: fitting some form of linear model with bimodal distribution of dependent variable

Hi, I was looking for a package that would help with outlier detection for bimodal distributions. I have tried 'outliers' and 'extremevalues' packages, but am not sure if they are ok for bimodal distribution. Any help would be highly appreciated! thanks, [[alternative HTML version deleted]]

Hello everyone, I'm trying to use mle from package stats4 to fit a bi/multi-modal distribution to some data, but I have some problems with it. Here's what I'm doing (for a bimodal distribution): # Build some fake binormally distributed data, the procedure fails also with real data, so the problem isn't here data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3)) # Just to check

We currently ship a PAE 32-bit domU that we can trivially make bimodal, except that if we set it to "bimodal", then older Xens will default to thinking the domU is not PAE: 353 dsi->pae_kernel = PAEKERN_no; 354 if ( dsi->__elfnote_section ) 355 { 356 p = xen_elfnote_string(dsi, XEN_ELFNOTE_PAE_MODE); 357 if ( p != NULL && strncmp(p,

Hi, is there any package/function that can tell if a numeric vector (continuous data) has a bimodal or trimodal distribution and caluclate the location of the corresponding modes? Thanks

Hi Everyone- After searching through posts and my favorite R-help websites I'm still confused about a problem. I have data which is bimodal in nature, but there is no clearly obvious separation between the two peaks. In programs such as Origin, I can deconvolute the two distributions and have it generate a "best guess" as to what the two subpopulations are which make up my

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]]

Hi, Suppose I have the following vector (data points): > x [1] 36.0 57.3 73.3 92.0 300.4 80.9 19.8 31.4 85.8 44.9 24.6 48.0 [13] 28.0 38.3 85.2 103.6 154.4 128.5 38.3 72.4 122.7 123.1 41.8 21.7 [25] 143.6 120.2 46.6 29.2 44.8 25.0 57.3 96.4 29.4 62.9 66.4 30.0 [37] 24.1 14.8 56.6 102.4 117.5 90.4 37.2 79.6 27.8 17.1 26.6 16.3 [49] 41.4 48.9 24.1

Hi, Does anybody have a good tip of how to treat bimodal data to perform statistical analyses? My data set ranges from -1 to 1 (any values are posssible in between) and most data are either close to -1 or close to 1. They are the results of a two choice experiment where individuals could choose more than once in either direction and scores were calculated. Simone Simone Immler

For several years, I have been using Splus to analyze an ongoing series of datasets that have a bimodal distribution. I have used the following functions, in particular the ms() function, to estimate the parameters: two means, two standard deviations, and one proportion. Here is the code I've been using in S: btmp.bi <- function(vec, p, m1, m2, sd1, sd2) {

Hello, Is there any test  for bimodality in R that x <- c(rnorm(1000,0,1),rnorm(1000,3,1)) hist(x,nclass=100) Thank you in advance for any help. Regards, Samor [[alternative HTML version deleted]]

Hello R Users, I am doing a Latin Hypercube type simulation. I have found the improvedLHS function and have used it to generate a bunch of properly distributed uniform probabilities. Now I am using functions like qlnorm to transform that into the appropriately lognormal or triangularly distributed parameters for my modes. However I have a parameter which I believe is bimodally distributed,

Dear R users, I'm looking for a test of bimodality in order to make some decisions about how to procede with an analysis algorithm. I have not come across any such tests in my readings and discussions apart from the Rao which appears to be applicable to cyclic data. The data I'm interested in characterizing as uni- or bimodal are frequency x amplitude spectra of consonant speech sounds,

Dear All, just a quick example: > x = 1:25 > x[12] = NA > x [1] 1 2 3 4 5 6 7 8 9 10 11 NA 13 14 15 16 17 18 19 20 21 22 23 24 25 > y = x[x<10] > y [1] 1 2 3 4 5 6 7 8 9 NA Is there any way of NOT getting NA for y = x[x<10]? Similarly > y = x[x<15] > y [1] 1 2 3 4 5 6 7 8 9 10 11 NA 13 14 How do I get rid of the NA (not post

Hi All, I'm having a problem with barplot: mydata [1,] 2 108 0 0 0 1 3 0 0 0 0 0 7 18 3 4 8 20 26 20 19 7 1 1 mycol = c(rep('yellow', 2), rep('white', 3), rep('orange',2), rep('white', 5), rep('orange',3), rep('red',9)) barplot(mydata, col = mycol) gives me an uniformly yellow barplot. How do I solve this? bw Federico

Hi, according to the help file rtags does not support VI(M) yet. Is there any known hack to ctags to get tags for R in VI(M)? BW F -- Federico C. F. Calboli Neuroepidemiology and Ageing Research Imperial College, St. Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 75941602 Fax +44 (0)20 75943193 f.calboli [.a.t] imperial.ac.uk f.calboli [.a.t] gmail.com

Hi All, is there a reasonably simple way of using a black and white chequer/checker board pattern as a colour: barplot(mydata, col = c('red', 'blue' 'checkerboard')) ? BW F -- Federico C. F. Calboli Neuroepidemiology and Ageing Research Imperial College, St. Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 75941602 Fax +44 (0)20 75943193 f.calboli

Hi, I am subsetting a matrix thus: test [,1] [,2] [,3] [1,] 1 7 13 [2,] 2 8 14 [3,] 3 9 15 [4,] 4 10 16 [5,] 5 11 17 [6,] 6 12 18 test[cbind(c(1,3,5), c(2,1,3))] [1] 7 3 17 This works fine, and is the equivalent of c(test[1,2], test[3,1], test[5,3]). cbind(c(1,3,5), c(2,1,3)) would obviously look like: [,1] [,2] [1,] 1 2 [2,]

Hello, What I am trying to do is: Generate a density plot of a population of data. This data has a bimodal distribution so I've isolated a couple of possible sub-populations and I want to overlay these two density plots over the first to see whether they are contributing to the bimodal population. I can do this fine with plot(density(...)) and lines(density(...)) . But the resulting plots

Hi All, I am writing a function that reads a file in myfile = file('myfile.raw', 'rb') . . . . . close(myfile) No matter what, I get the warning Warning message: closing unused connection 3 (myfile.raw) Since the whole thing is in a function, I'd like to avoid unecessary noise for the user, and I'd like to kill the warning -- without having to mess with global options.

Hola a tod en s, estoy intentando averiguar el número de modas en una distribución. Para ello utilizo diptest. Mi duda es que no acabo de entender cuando la información suministrada 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