similar to: Deleting a component with a known probability

> Date: Wed, 12 Jan 2000 13:05:21 +0100 (CET) > From: Adriane Leal <loparic at student.fsa.ucl.ac.be> > To: r-help at stat.math.ethz.ch > Subject: [R] Problems creating a random sample > > Hi! > > I'm supposed to create a random sample bivariate normal variates of size > equal to 250 with mean vector (-1,1) and cov matrix (1,0.95,0.95,1) > > In S

Hi Folks, According to the PDF file blogit.pdf in the Zelig documentation: "Use the bivariate logistic regression model ["blogit"] if you have two binary dependent variables (Y1,Y2), and and wish to model them jointly as a function of some explanatory variables. Each pair of dependent variables (Yi1,Yi2) has four potential outcomes, (Yi1=1,Yi2=1), (Yi1=1,Yi2=0),

Dear all, My code is presented as the following. library(MASS) library(rmutil) library(repeated) library(lme4) library(arm) #install.packages("Zelig", repos = "http://gking.harvard.edu") library(Zelig) rm(list = ls()) beta0<-2.5 beta1<--0.3 sigs2<-0.5 I<-4 #numberpatients<-c(40,100,160,200,400,600) numberpatients<-c(1000) #numberpatients<-3 times<-1

Good morning and I appreciate the availability of a help-list. I am a professional hydrologist, but not a professional statistician. Yet I find myself using statistical tools at least part of the time. My discovery of the R-project through a friend has been most helpful. Here is my problem: I'm tasked with fitting a dataset comprising correlated discharges from adjacent watersheds to

Greetings, I'm interested in generating data from various bivariate or mulitivariate distributions (e.g. gamma, t, etc), where I can specify the parameter values, including the correlations among the variables. I haven't been able to dig anything up on the faq, but I probably missed something. A nudge in the right direction would be appreciated. David --

Hello you all from the R Help mailing list! I am working on a PowerBook with Mac Os X and use R 2.5.0. I used the distance function from the analogue package to perform a similarity analysis using the Gowers Index and weighted Variables. My variables are bivariate data and measurements as well as interval data transformed into minimum and maximum variables. I used this Code:

Dear R-List: I`m trying to calculate the probabilities for a bivariate normal distribution while using the mvtnorm-package(dmvnorm) and the adapt-package for multidimensional integration. The problem is that I can`t specify the upper bound in the adapt-package the way I need it because I don`t need a rectangular area. I want to calculate the probability starting at the origin under the line y=x.

Hi everyone, I'm not exactly sure how to ask this question most clearly, but I hope that giving the context in which it occurs for me will help: I'm trying to compare the brain images of two patient populations; each image is composed of voxels (the 3D analogue of pixels), and I have two images per patient, one reflecting grey matter concentration at each voxel, and the other reflecting

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org Reproducible Rterm crash. I drew a complicated graph, then resized the graphics window. R reported > Insufficient memory for resize. Killing device

Dear all, I have a bivariate dataset from a preliminary study. I want to do two things: (1) estimate the probability density of this bivariate distribution using some nonparametric method (kernel, spline etc); (2) sample a big dataset from this bivariate distribution for a simulation study. Is there any good method or package I can use in R for my work? I don?t want parametric models like

Dear users I am reposting this and hope it will be accepted this time. I am using copula package to fit my bivariate data and simulation. As explained in package documentation we can use our own data distribution to feed on copula as long as we have d, p and q (pdf, cdf and quantile) functions are available. Hence my code for those are: # Make the functions for data distribution

This might not be the correct forum for this question for there might be some flaws in my logic so the R function I'm looking for might not be the correct, but I know there?s a lot of smart people in this forum so please correct me if I'm wrong. I have been googling and searching in this forum for something useful but so far I'm out of luck. This is the background to my problem. I

Respected R Users, I looking for help with generating theoretical confidence regions for any of non-symmetric bivariate statistical distributions (bivariate Chi-squared distribution<Wishart distribution>, bivariate F-distribution, or any of the others). I want to to used it as a benchmark to compare a few strategies constructing confidence regions for non-symmetric bivariate data. There is

Dear helpers, I was wondering if there are some bivariate distribution simulation functions in any R packages. Specifically, I want to simulate bivariate log logistic, bivariate Weibull, or other common bivariate life time functions, and I can specify the correlations. Any comments about this will be appreciated. Thanks, Zhu Wang

"Jenny Stadt" <jennystadt at yahoo.ca> asked: > > I am struggling in a bivariate Weibull distribution although I > searched R-Site-Help and found suggestion with Copula. Seems the > maximum likelihood estimate is beyond what I can understand. > > My case is: given two known marginal distribution (both are Weibull), > and the correlation between them. How can I

Am trying to do some study on bivariate binomial distribution. Anyone knows if there is package in R that I can use to calculate the density function of bivariate binomial distribution and to generate random samples of it. Thanks, -- View this message in context: http://n4.nabble.com/How-to-calculate-density-function-of-Bivariate-binomial-distribution-tp992002p992002.html Sent from the R help

Hello, I'm quite new with R and so I would like to know if there is a command to calculate an integral. In particular I simulated a bivariate normal distribution using these simple lines: rbivnorm <- function(n, # sample size mux, # expected value of x muy, # expected value of Y sigmax, # standard deviation of

> On Wed, 2 Feb 2000, Adriane Leal wrote: > > > I'd like to perform a box-cox transformation to a data set and also plot > > lambda versus L(lambda) using R. Does anybody knows how can I do such a > > thing? gnlr3 in my gnlm library does both linear and nonlinear models with Box-Cox transformation. However, it is somewhat nonstandard as it renormalizes to obtain a

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

Dear list members I need a function that calculates the bivariate normal distribution for each observation. It is part of a likelihood function and I have 1000's of cases. As I understand it I cannot use packages like "mvtnorm" because it requres a covariance matrix of the same dimension as the number of observations. Basically what I need is a function that takes as arguments a