similar to: Generating repeated measures data

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]]

Hi R-fellows, I am trying to simulate a multivariate correlated sample via the Gaussian copula method. One variable is a binary variable, that should be autocorrelated. The autocorrelation should be rho = 0.2. Furthermore, the overall probability to get either outcome of the binary variable should be 0.5. Below you can see the R code (I use for simplicity a diagonal matrix in rmvnorm even if it

Can someone point me to a justification for the design decisions, which seem to be similar in lattice and ggplot2, of (1) gray backgrounds and (2) filled points? [I seem to remember seeing somewhere the opposite recommendation for points, i.e. that open points make it much easier to see if there are overlapping values -- and this would be a reason that pch=1 gives an open circle ...] cheers

Hi all, rmvnorm()can be used to generate the random numbers from a multivariate normal distribution with specified means and covariance matrix, but i want to specify the correlation matrix instead of covariance matrix for the multivariate normal distribution. Does anybody know how to generate the random numbers from a multivariate normal distribution with specified correlation matrix? What about

Dear R experts, I am trying to simulate correlated binary data and have stumbled upon the following problem: With the help of "binarySimCLF" or "mvpBinaryEp" I have been able to simulate correlating binary vectors given certain mean values and a desired correlation. My problem is that these procedures do not allow you to specify the exact vector for which you want to generate

Hi all, I'm trying to install the mixdist package with: install.packages("mixdist") But I'm getting the following errors: * installing *source* package ‘mixdist’ ... ** R ** data ** preparing package for lazy loading ** help *** installing help indices ** building package indices ... Warning in utils::data(list = f, package = package, lib.loc = lib.loc, envir = dataEnv) :

Sorry for the off-topic question, but I know there are some talented LaTeX users out there. Which bibliography style gives only the year in text citations (e.g "for further details, see Anderson (1992)" )? Thanks Jason -- Indigo Industrial Controls Ltd. http://www.indigoindustrial.co.nz 64-21-343-545 jasont at indigoindustrial.co.nz

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

CRAN (and crantastic) updates this week New packages ------------ * bdoc (1.0) Michael Anderson http://crantastic.org/packages/bdoc This package contains a function that will classify DNA barcodes as well as a few test and reference data sets. * bdsmatrix (1.0) Terry Therneau http://crantastic.org/packages/bdsmatrix This is a special case of sparse matrices, used by coxme and

MultiMarkdown 2.0.b1 includes an update to the core MultiMarkdown package, the Drag and Drop applications, the TextMate Bundle, and the Sample Document. There are a couple of significant changes in this release, as well as some bug fixes: * change \textwidth to \linewidth in LaTeX export XSLT files (let me know if this causes problems) * add Windoze compatibility to the perl scripts

Hi R-users, I just started learning R. I have a project on lot quality assurance sampling (LQAS). In this project I have to develop LQAS plans to make decision on stopping / continuing a programme. The LQAS plans is based on cluster sampling: selection of k clusters (villages) of m children each for a total sample of n. In this connection I would like to estimate the classification errors ( alpha

Hi R-users, I just started learning R. I have a project on lot quality assurance sampling (LQAS). In this project I have to develop LQAS plans to make decision on stopping / continuing a programme. The LQAS plans is based on cluster sampling: selection of k clusters (villages) of m children each for a total sample of n. In this connection I would like to estimate the classification errors ( alpha