similar to: Sum of binominal distributed random numbers

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 all, I should have given a better explanation of my problem. Here it is. I extracted from my code the bit that gives the error. Place this in a file called test.c #include <math.h> #include <R.h> #include <Rmath.h> #include <float.h> #include <R_ext/Print.h> int main(){ double prob[3] = {0.0, 0.0, 0.0}; double prob_tot = 0.; prob[0] = 0.3*dnorm(2, 0,

Dea'R' helpers I have following data - prob = c(0.1, 0.2, 0.3, 0.4, 0.5) frequency = c(100, 75, 45, 30, 25) no_trials = c(10, 8, 6, 4, 2) freq1 = rbinom(frequency[1], no_trials[1], prob[1]) freq2 = rbinom(frequency[2], no_trials[2], prob[2]) freq3 = rbinom(frequency[3], no_trials[3], prob[3]) freq4 = rbinom(frequency[4], no_trials[4], prob[4]) freq5 = rbinom(frequency[5],

Dear all, I have a questions regarding using the c function rmultinom.c. I got the following error message "rbinom: probability sum should be 1, but is 0.999264" Which is thrown by: if(fabs((double)(p_tot - 1.)) > 1e-7) MATHLIB_ERROR(_("rbinom: probability sum should be 1, but is %g"), (double) p_tot); I understand my probabilities do not sum to one close enough. I

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

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,

Dear R users, I would like to sample from a mixture distribution p1*f(x1)+p2*f(x2). I usually sample variates from both distributions and weight them with their respective probabilities, but someone told me that was wrong. What is the correct way? Vumani

Dear R users: I'm generating the following survival data: set.seed(123) n=200 #sample size x=rbinom(n,size=1,prob=.5) #binomial treatment v=rgamma(n,shape=1,scale=1) #gamma frailty w=rweibull(n,shape=1,scale=1) #Weibull deviates b=-log(2) #treatment's slope t=exp( -x*b -log(v) + log(w) ) #failure times c=rep(1,n) #uncensored indicator id=seq(1:n) #individual frailty indicator

Hi all, I was hoping somebody may know of a function for simulating a large binary sequence (length >10 million) using a (1st order) markov model with known (2x2) transition matrix. It needs to be reasonably fast. I have tried the following; mc<-function(sq,P){ s<-c() x<-row.names(P) n<-length(sq) p1<-sum(sq)/n s[1] <- rbinom(1,1,p1); for ( i in 2:n){ s[i]

Hello all, A friend just showed me how ks.test fails to work with pbinom for small "size". Example: x<-rbinom(10000,10,0.5) x2<-rbinom(10000,10,0.5) ks.test(x,pbinom,10,0.5) ks.test(x,pbinom,size = 10, prob= 0.5) ks.test(x,x2) The tests gives significant p values, while the x did come from binom with size = 10 prob = 0.5. What test should I use instead ? Thanks, Tal

On 08/04/2023 5:53 p.m., Martin Maechler wrote: >>>>>> Christophe Dutang >>>>>> on Sat, 8 Apr 2023 14:21:53 +0200 writes: > > > Dear all, > > > Using rmultinom() in a stochastic model, I found this function returns an error message 'NA in probability' for an infinite probability. > > > Maybe, a more

I've had a need for multinomial "random number generation" occasionally. And other people too. The following code is currently in the (very small ``not very high importance'') CRAN package normix --- which I will rename to "nor1mix" very seen because of a ``name registration'' problem I want to add "this" (well the functionality) to a

Hello, I'm trying to use the bam() function in the R mgcv package for a large set of grouped binary data. However, I have found that this function does not take data in the format of cbind(numerator, denominator) on the left hand side of the formula. As an example, consider the following dat1 <- data.frame(id=rep(1:6, each=3), num=rbinom(18, size=10, prob=0.8), den=rbinom(18, size=5,

Greetings.? My wife is teaching an introductory stat class at UC Davis.? The class emphasizes the use of simulations, rather than mathematics, to get insight into statistics, and R is the mandated tool.?? A student in the class recently inquired about different approaches to sampling from a binomial distribution.? I've appended some code that exhibits the idea, the gist of which is that using

>>>>> Christophe Dutang >>>>> on Sat, 8 Apr 2023 14:21:53 +0200 writes: > Dear all, > Using rmultinom() in a stochastic model, I found this function returns an error message 'NA in probability' for an infinite probability. > Maybe, a more precise message will be helpful when debugging. >> rmultinom(1, 3:5, c(1/2, 1/3,

hello, I want to simulate 200 times the mean of a joint probability (y1) and 200 times the mean of another joint distribution (y2), that is I'm expecting to get 200 means of y1 and 200 means of y2. y1 and y2 are probabilities that I calculate from the marginal prob. (z1 and z2 respectively) multiple by the conditional prob. (x1 and x2 respectively), which I generaterd from the binomial

Dear all, Could someone point me to a function or algorithm to generate random bivariate binomial data? Some details about what I'm trying to do. I have a dataset of trees who were categorised as not damaged or damaged. Each tree is measured twice (once in two consecutive years). The trees can recover from the damage but the data is clearly correlated. As a (un)damaged tree is more likely

Dear All I have run the following GLM binominal model on a dataset composed by the following variables: TRAN_DURING_CAMP_FLG enviados bono_recibido 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1

I am looking for an efficient way to solve the following problem. I have a large matrix of continuous values with a small proportion of missing values. Columns correspond to variables where each variable has two measurements, call them A and B. The matrix is such that the columns are in sequence with respect to the variables. I would like to sum up the two measurements for each variable and each

Hi This is OT, but I need it for my simulation in R. I have a special case for sampling with replacement: instead of sampling once and replacing it immediately, I sample n times, and then replace all n items. So: N entities x samples with replacement each sample consists of n sub-samples WITHOUT replacement, which are all replaced before the next sample is drawn My question is: which