similar to: Outlier detection in bimodal distribution

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,

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

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

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, 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

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) {

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]]

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,

Hi All, I have a parameter that is bimodal, and I want to get some sort of linear model done with it results = some.linear.function(bimodal.param ~ factor1 + some other stuff, mydata) I want to see if factor 1 matters (it has 3 levels, of of which can be taken as baseline), i.e: summary(results) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.108522

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 sir/madam ? My name is Quan, I am a PhD student in Korea. my major is Hydrological in Water Resources Engineering. I am interested in Extremes Toolkit model and I known you from information in internet.? I?installed successfully this model but I didn't know how to make?the type of ?files input for this model such as: Flood.dat; Ozone4H.dat; Flood.R; HEAT.R.?(free download these files of

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

I am trying to make horizontal violin plots. I have tried both vioplot and simple.violinplot, but both of them seem to not be willing to take the horizontal option. Is this correct, or am I just bungling it somehow? For instance, for vioplot (from the example shown, with the horizontal modification): > vioplot(bimodal,uniform,normal, horizontal=TRUE) Error in median(data) : need numeric data

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

Hello R users, I am trying to estimate the parameters of a bimodal normal distribution using moments matching, so I have to solve a non-linear system of equations. How can I solve the following simple example? x^2 - y^2 = 6 x ? y = 3 I heard about nlsystemfit, but I don?t know how to run it exactly. I have tried the following code, but it doesn?t really work: f1 <-y~ x[1]^2-x[2]^2-6 f2

Dear R helpers, I have a bimodal dataset dealing with loss amounts. I have divided this dataset into two with the bounds for the first dataset i.e. dataset-A being 5,000$ to 100,000$ and the dataset-B deals with the losses exceeding 100,000$ i.e. dataset-B is left truncated. I need to fit truncated normal disribution to dataset - I having lower bound of 5000 and upper bound of 100,000. While I

Hello all I'm trying to do a simulation that involves identifying the minimum point between two peaks of a (usually) bimodal distribution. I can do this easily if there are only two peaks: CnBdens<-density(Ys/Xs) #probability density function for ratio of Ys to Xs for(p in 1:512) ifelse(CnBdens$y[p]>CnBdens$y[p-1],peak1<-p,break) #identifies first peak in probability

Hi, I'm dealing with wind data and I'd like to model their distribution in order to simulate data to fill-in missing values. Wind direction are typically following a vonmises distribution and wind speeds follow a weibull distribution. I'd like to build a joint distribution of directions and speeds as a VonMises-Weibull bivariate distribution. First is this a stupid question? I'm