similar to: mvrnorm limits

Dear All,   I often have to work with certain models in which I try to "reproduce" a distribution the best I can with very little known information avaible. Is there a package or function in R that could best reproduce a probability distribution using only the mean, median and SD values availble without knowing the actual distribution type to begin with and/or the covariance matrix (for

Dear all, I have some trouble with special characters while building my R package. I tried to follow the usual LATEX format, but could not fix the problem: For instance, for "greater than or equal", I tried "\geq", but R says that this is an unknown macro. Could anyone direct me how to solve this issue? Best Ozgur ----- ************************************ Ozgur ASAR

Hello R-Experts, I am facing the problem that I have to estimate several parameters for a lot of different dependent variables. One single regression looks something like this: y = beta0 + beta1 * x1 + beta2 * x2 + beta3 * x1 * x2 + beta4 * x4 + beta5 * lag(x4,-1) where y is the dependent variable and xi are the independent ones. Important to me are the different estimates of betai and their

Hi All This might be something very trivial but I seem to miss something in the syntax or logic which makes me keep wandering around the problem without arriving at a solution. What I want to do is to simulate a sample data for performing cluster analysis. I tried to use x1= mvrnorm(10,rep(0.8,3),diag(3)) x2= mvrnorm(10,rep(0,3),diag(3)) x3= mvrnorm(10,rep(-0.5,3),diag(3)) x=rbind(x1,x2,x3)

Hello, does anybody know another faster function for random multivariate normal variable simulation? I'm using mvrnorm, but as profiling shows, my algorithm spends approximately 50 % in executing mvrnorm function. Maybe some of you knows much faster function for multivariate normal simulation? I would be very gratefull for advices. -- View this message in context:

Hello, I have a dataframe (Lx) with 5 Lb, and 5 Lw variables. I want to create several variables according to the loop presented below (data attached). Lx <- read.csv("Lx.csv", header=T, sep=",") for (i in 1:20) { Lx$sb1[i] <- Lx$Lb1[i+1]/Lx$Lb1[i] Lx$sb2[i] <- Lx$Lb2[i+1]/Lx$Lb2[i] Lx$sb3[i] <- Lx$Lb3[i+1]/Lx$Lb3[i] Lx$sb4[i] <-

Dear?All, ? a few weeks ago I have posted a question on the R help listserv that?some of you have responded to with a great solution, would like to thank you for that? again.?I thought I would reach out to you with the issue I am trying to solve now. I have posted the question a few days ago, but probably it was not?clear enough, so I thought i try it again.?At times I have a multivariate example

Dear R community! I would like to simulate distributions based on a polynomial model. It consists of 96 values and I want to randomly select new normaly distributed values around the modeled values. Furthermore, each value should be correlated with the previous one. Is it correct to therefore use mvrnorm? How do I define sigma? Thank you very much for your help. All the best, Marianne -- View

I want to generate multiple multivariate normal samples with different mean vectors and common covariance matrix. I can do this with a loop, but can't quite figure out how to do it with apply and friends. In the example below, I want values to have 3 columns: group, x, y # number of groups, and group means x <- jitter(seq(2,10,by=2)) y <- x + rnorm(length(x), 0, .5) means <-

Hi, Your mistake seems to be in sum(v[1:x]) You create "x" as a vector but your treat it as a single number. v[1:x] expects "x" to be a single number and only considers its first element which is 1. If I understand your query correctly, the following might handle your problem: sum.vec <-NULL for (x in 1:1000){ t <- rbinom(1000, 1, 0.5) v <- replace(t,t==0,-1)

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

I am trying to simulate draws from a multivariate normal using mvrnorm, and am getting the following error message: Error in mu + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X) : non-conformable arrays I do not understand why I am getting this message, since the vector of means I am giving to the function is 13 by 1 and the variance matrix I am giving to the function is 13

Dear all, Actually I could not decide who to contact, then decided to post here. If not appropriate sorry for that. I have written some functions in R which might be supposed to available in the base package but not available. I am wondering that how can I submit those functions to that package? Best Ozgur ----- ************************************ Ozgur ASAR Research Assistant Middle East

Dear List: I am generating N datasets using the following Sigma<-matrix(c(400,80,80,80,80,400,80,80,80,80,400,80,80,80,80,400),4,4 ) mu<-c(100,150,200,250) N=100 for(i in 1:N) { assign(paste("Data.", i, sep=''), as.data.frame(cbind(seq(1:1000),(mvrnorm(n=1000, mu, Sigma))))) } With these datasets, I need to work on some of the variables and then run each dataset

Hi group, I've been thinking of calculating euclidean distance between each column of a data frames that each consists of standardized numerical columns. However, I don't know if there's a way of summarizing the overall distance by some kind of metrics. If anyone know a proper way of doing so and/or a package I would greatly appreciate your suggestions. Thanks very much! Kel --

Dear all, I want to concatenate the elements of two vectors such as a<-c("a1","a2") b<-c("b1","b2") and obtain "a1b1", "a1b2","a2b1","a2b2" I tried the paste and paste0 functions, but they yielded elementwise concatenation such as "a1b1","a2b2" I am wondering that is there an efficient

Dear All, would you please provide your thoughts on the following: let us say I have: a <-c(1,5,8,15,32,69) b <-c(8.5,33) and I would like to extract from "a" the two values that are closest to the values in "b", where the length of this vectors may change but b will allways be shorter than "a". So at the end based on this example I should have the result

Dear all, I want to manipulate a character string such as ex<-"cbind(data$response1,data$response2)" in R in two ways: 1) extracting the "response1" portion of ex 2) replacing "$" with "." I am wondering that is it possible efficiently doing these in R? Best Ozgur -- View this message in context:

hello, I try to run simulation of levene's test to find the p-value but the error of replacement has length zero occur, could anyone help me to fix this problem? asim <- 1000 pv<-rep(NA,asim) for(i in 1:asim) {print(i) set.seed(i) g1 <- rnorm(20,0,2) g2 <- rnorm(20,0,2) g3 <- rnorm(20,0,2) x <- c(g1,g2,g3) group<-as.factor(c(rep(1,20),rep(2,20),rep(3,20))) library(Rcmdr)

https://bugzilla.mindrot.org/show_bug.cgi?id=1285 Vladimir Rutsky <rutsky.vladimir at gmail.com> changed: What |Removed |Added ---------------------------------------------------------------------------- CC| |rutsky.vladimir at gmail.com -- You are receiving this mail because: You are watching the assignee of