similar to: generate Binomial (not Binary) data

Hi all, I was wondering how to generate samples for two RVs X1 and X2. X1 ~ Bernoulli (p1) X2 ~ Bernoulli (p2) Also, X1 and X2 are correlated with correlation \rho. Regards, Vineet [[alternative HTML version deleted]]

Hi. I am trying to generate a correlated binary data set. I've tried to use mvtBinaryEP, binarySimCLF, and bindata packages but none of them works in R version 2.15.1. Do you know any package to generate correlated binary covariates and work in R version 2.15.1, or how to generate it? Thanks, [[alternative HTML version deleted]]

Dear R-users, I'm running a lmer model using the lme4 package. My dependent variable is dichotomous and I'm using the "binomial" family. The results are slightly different from the HLM results based on a Bernoulli distribution. I read that a Bernoulli distribution is an extension of a binomial distribution. Is that right? If so, how can I adapt my R model to a Bernoulli

Dear All, I try to simulate correlated binary data for a clinical research project. Unfortunately, I do not come to grips with bindata(). Consider corr<-data.frame(ID=as.factor(rep(c(1:10), each=5)), task=as.factor(rep(c(1:5),10))) [this format might be more appropriate:] corr2<-data.frame(ID=as.factor(rep(c(1:10),5)), tablet=as.factor(rep(c(1:5),each=10))) Now, I want to

Dear all, I've got a quite large dataframe (stor) with rows subject and rt (reaction time). I would like to split the reaction times per subject into 6 bins of equal size. Right now, I'm using the following code: bindata <- function(rt) { bindata <- rep(-1,length(rt)) binwidth <- length(rt)/6 bindata[order(rt)[(0*binwidth)+1:(1*binwidth)]] <- 1

Dear subscribers, I am looking for a function which would allow me to model the dependent variable as the number of successes in a series of Bernoulli trials. My data looks like this ID TRIALS SUCCESSESS INDEP1 INDEP2 INDEP3 1 4444 0 0.273 0.055 0.156 2 98170 74 0.123 0.456 0.789 3 145486 30 0.124

I need to construct confidence intervals for the binomial variance. This is the usual estimate v = x*(n-x)/n or its unbiased counterpart v' = x*(n-x)/(n-1) where x = binomial number of successes observed in n Bernoulli trials from proportion p. The usual X^2 method for variance confidence intervals will not work, because of the strong non-normal character of the sampling

Dear All, I would be grateful if you can help me. My problem is the following: I have a data set like: ID time X1 X2 1 1 x111 x211 1 2 x112 x212 2 1 x121 x221 2 2 x122 x222 2 3 x123 x223 where X1 and X2 are 2 covariates and "time" is the time of observation and ID indicates the

Dear list I am having a couple of problems with the package 'bindata'. Having installed it using install.packages("bindata"), I am finding that when I load it up as a library I get the following errors: > library(bindata) Loading required package: e1071 Loading required package: mvtnorm Warning messages: 1: There is no package called `e1071' in: library(package, char

Dear all, How can I plot the number of observations per box in a boxplot. Many thanks, Bernard --------------------------------- [[alternative HTML version deleted]]

Daer All, I have a factor variable, X with 5 levels. When I type tables(X) it gives me: table(X) 1 2 3 4 5 10 5 0 0 0 How to drop the levels with zeros such that when I will type: table(X) it will give me: table(X) 1 2 10 5 Thank a lot, Bernard --------------------------------- [[alternative HTML version deleted]]

Hi All: I would like to generate a binomial random variable that correlates with a normal random variables with a specified correlation. Off course, the correlation coefficient would not be same at each run because of randomness. I greatly appreciate your input. Ashraf

Dear All, I wonder if there is any R function to generate a sequence of vectors from existing ones. For example: x 1<- c(1,2,3) x2 <- c(4,5) x3 <- c(6,7,8) The desired output is a list of all 3*2*3 = 18 possible combinations of elements of x1,x2 and x3. One element for example is (1,4,6). Many thanks in advance, Bernard

Dear All, I have written the following programs to find a non-singular (10*10) covariance matrix. Here is the program: nitems <- 10 x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <- try(solve(sigma), TRUE) while(inherits(inverse, "try-error")) { x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <-

Dear All, I wonder how to compute the age from the date of birth and the date of examination. Suppose that have the following data: df <- as.data.frame(rbind(c(1,"10/08/1950","15/03/1998"), c(1,"10/08/1950","20/07/1998"), c(1,"10/08/1950","23/10/1998"))) names(df) <- c("ID", "date_birth",

I recently use gbm for a binary classification problem. As expected, it gets very good results, based on Area under ROC with 7-fold cross validation. However, the application (malware detection) is cost-sensitive, getting a FP (classify a clean sample as a dirty one) is much worse than getting a FN (miss a dirty sample). I would like to tune the gbm model biased to very low FP rate. For this

Dear All, I have a vector x = (1,1,1,2,2,2,3,3,3,3) I am looking for a function to return a vector containing the distinct elements of x i,e y = (1,2,3) The following code gives the desired results: as.numeric(levels(as.factor(x))) Is there any other elegant way? Thanks, B --------------------------------- [[alternative HTML version deleted]]

Dear R Helpers, Is there a way to generate multivariate correlated binomial data in R, similar to how the rmvbin procedure in package bindata can generate multivariate correlated binary data? Thanks for your help, Jonathan Williams

Hi. I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

Dear All, I am wondering if there is an R function to convert a covariance matrix to a correlation matrix. I have a covariance matrix sigma and I want to compute the corresponding correlation matrix R from sigma. Thank you very much, Bernard --------------------------------- [[alternative HTML version deleted]]