similar to: Bimodal Distribution

Hi I want to du a sensitivity analysis using Latin Hypercubes. But my parameters have to fulfill two conditions: 1) ranging from 0 to 1 2) have to sum up to 1 So far I am using the lhs package and am doing the following: library(lhs) ws <- improvedLHS(1000, 7) wsSums <- rowSums(ws) wss <- ws / wsSums but I think I can't do that, as after the normalization > min(wss) [1]

Hi All, Did anyone else have a problem like this? I am sorry if its a small issue, I seem to not understand what to do to get rid of this error. > Sigma [1] 0.1939025 > MuRest [1] 8.512772 > TauZero [1] 0.1 > curve(qlnorm(x,-TauZero+MuRest, Sigma,lower.tail=F), xlim=c(4000,9000), ylim=c(0,.99),xlab="", ylab="") Warning message: In qlnorm(p, meanlog, sdlog,

Hello everyone I am trying to get one of my packages through R's QC. The package is clean for me under R-2.4.1, R-2.5.0, and R-devel, but Kurt gets > > * checking R code for possible problems ... WARNING > hypercube: no visible binding for global variable ?f? Function hypercube() [cut-&-pasted below] is intended to return an adjacency matrix for an n-dimensional

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, 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]]

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

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 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, 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

Hello, I am performing a sensitivity analysis using a Latin Hypercube sampling. However, I have difficulty to draw a Hypercube sample for one variable. I?ve generated this variable from a Poisson distribution as follows: set.seed(5) mortality_probability <- round(ppois(seq(0, 7, by = 1), lambda = 0.9), 2) barplot(mortality_probability, names.arg = seq(0, 7, by = 1), xlab = "Age

Hi all, I am a begginer in R and I have been trying to use the Conditioned Latin Hypercube to choose sample points only in areas close to roads due to the difficult thorough access in the study area. I could use a code to create the points throughout the area, but I need to create a code to make the R only comes up with points close to this road. I've been using the packages

>May 26, 2017; 11:41am Nelly Reduan Latin Hypercube Sampling when parameters are >defined according to specific probability distributions >Hello, > I would like to perform a sensitivity analysis using a Latin Hypercube Sampling (LHS). >Among the input parameters in the model, I have a parameter dispersal distance which is defined according to an exponential probability

(1) ...need to speed up a monte-carlo sampling...any suggestions about how I can get R to use all 8 cores of a mac pro would be most useful and very appreciated... (2) spent the last few hours trying to get pnmath to compile under os- x 10.5.4... using gcc version 4.2.1 (Apple Inc. build 5553) as downloaded from CRAN, xcode 3.0... ...xcode 3.1 installed over top of above after

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) {

Thank you very much Rob for your answer. I have some difficulties to understand how to apply my agent-based model to each parameter combination generated by the LHS, in particular when parameters are defined by probability distributions. Indeed, I have multiple parameters in my model: parameters which are defined by a single value (like ?temperature", "pressure?) and parameters which are

Hello all, I am trying to do a Latin Hypercube Sampling (LHS) to a 5-parameter design matrix. I start as follows: library(lhs) p1<-randomLHS(1000, 5) If I check the distribution of each parameter (column), they are perfectly uniformly distributed (as expected).For example, hist(p1[,1]) Now the hard (maybe strange) question. I want the combination of the first three parameters to sum up to

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, I got one question on the Latin hypercube sampling. suppose there are three variables a, b, c, all of them follow the normal distribution. the mean value and standard deviation for each are  a(32, 2), b(35,5), c(37,3). I would like to use Latin hypercube sampling to random generate 1000 samples. but it needs to satisfy the condition that a<b<c. How can I implement this