similar to: Power test binominal GLM model

Dear all I'm currently facing the following situation. We have run a marketing campaign providing to some members one of two type of coupones. In addition to this, some of these members were already touched in the short past by another campaign. So I have the following dataset, where: -mixpre3M= is a flag variable telling us if the customer had already purchased somethign in the past

Hi I have two vectors, prob and size, and I want to add the random deviates of these two, i.e. sum( sapply( 1:length(prob), function(n){ rbinom(1, size(n), prob(n) } ) ) My problem is that I have to do this for a large number of value combinations. Is there a faster way of doing this? Rainer

Frede Aakmann T?gersen wrote: > Perhaps > > http://stinet.dtic.mil/cgi-bin/GetTRDoc?AD=ADA266969&Location=U2&doc=GetTRDoc.pdf > > is something that you can use? Thanks a lot - that might help. Rainer > > > > Best regards > > Frede Aakmann T?gersen > Scientist > > > UNIVERSITY OF AARHUS > Faculty of Agricultural Sciences > Dept.

Hi, this is my first time using R. I want to simulate the following process: "in a population of size N, there are i individuals bearing genotype A, the number of those bearing A is j in the next generation, which following a binominal distribution (choose j from 2*N, the p is i/2*N), to plot the probability of the next generations, my script is as follows. It cannot run successfully,

I'm a R beginner. Recently I conduct a project about expected return that involves using R for calculations. I encounter three questions as follow: Q1. How to use R to demonstrate bootstrapping and Monte Carlo simulation under 10,000 times of random selection? Q2. How to do a loop in R? Q3. How to use R to do binominal tree calculation under multiple periods? I'm perplexed by the

Hello, I like to fit data against a negative binominal distribution x2<-c(rep(10,14),rep(9,8),rep(8,13),rep(7,11),rep(6,6),rep(5,18),rep(4,7),re p(3,21),rep(2,33),rep(1,55),rep(0,225)) f2<-fitdist(x2,"nbinom",method="mle") plot(f2) summary(f2) gofstat(f2) I receive the following result: Fitting of the distribution ' nbinom ' by maximum

R 2.3.0 Linux, SuSE 10.0 Hi I have two problems with mle - probably I am using it the wrong way so please let me know. I want to fit different distributions to an observed count of seeds and in the next step use AIC or BIC to identify the best distribution. But when I run the script below (which is part of my original script), I get one error message for the first call of mle: Error in