similar to: R commands

Hi All, I'm perplexed by the way the unclass function displays a factor whose labels have been swapped with the relevel function. I realize it won't affect any results and that the relevel did nothing useful in this particular case. I'm just doing it to learn ways to manipulate factors. The display of unclass leaves me feeling that the relevel had failed. I've checked three books

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

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,

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

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.

Hello all. I was wondering if anyone had insight into how I might generate a large number of data sets/vectors, where each of the data sets/vectors have a different name? For example, suppose I wanted to generate N data sets/vectors, each with a different name such as: Random.Data.1 <- rnorm(10, 0, 1) Random.Data.2 <- rnorm(10, 0, 1) Random.Data.3 <- rnorm(10, 0, 1) . . . . .

Hi, We have recently published a paper on floating point accuracy analysis through Monte Carlo Arithmetic. We also released the open-source tool Verificarlo (https://github.com/verificarlo/verificarlo) that relies on LLVM for instrumenting floating point operations. Could you please add our paper to http://llvm.org/pubs/ ? Verificarlo: checking floating point accuracy through Monte Carlo

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

Dear list users, I need to learn about MCMC methods, and since there are several packages in R that deal with this subject, I want to use them. I want to buy a book (or more than one, if necessary) that satisfies the following requirements: - it teaches well MCMC methods; - it is easy to implement numerically the ideas of the book, and notation and concepts are similar to the corresponding R

Hi all, I'm running a monte carlo test of a neural network tool I've developed, and it looks like it's going to take a very long time if I run it in R so I'm interested in translating my code (included below) into something faster like Fortran (which I'll have to learn from scratch). However, as you'll see my code loads the nnet library and uses it quite a bit, and I

Hi All, I am using canonical correspondence analysis to compare a community composition matrix to a matrix of sample spatial relationships and environmental variables. In order to parse out how much variance is explained purely by space (S/E) or the environment (E/S) I am using a conditional (partial) CCA. I want to test significance via Monte Carlo but I can not find a way to do this with a

Hi AlL, I ahve this problem that my objective function is discontinous in the paramaters and I need to use methods such as nelder-mead to get around this. My question is: How do i compute standard errors to a problem that does not have a gradient? Any literature on this is greatly appreciated. Jean,

Hello all, I would like to know what the difference is between chisq.test and fisher.test when using the Monte Carlo method with simulate.p.value=TRUE? Thank you -- View this message in context: http://r.789695.n4.nabble.com/Difference-in-Monte-Carlo-calculation-between-chisq-test-and-fisher-test-tp2322494p2322494.html Sent from the R help mailing list archive at Nabble.com.

Hello, How can I make a monte carlo simulation on R? Regards Adel -- PhD candidate in Computer Science Address 3 avenue lamine, cité ezzahra, Sousse 4000 Tunisia tel: +216 97 246 706 (+33640302046 jusqu'au 15/6) fax: +216 71 391 166 [[alternative HTML version deleted]]

Hi there, I am running Monte Carlo Simulations in R using ordinary "while (condition)" loops. Since the number of iterations is something like 100.000 and within each iteration a given subsample is extended sequentially it takes hours to run the simulation. Does anyone know if there is either a way to avoid using loops in Monte Carlo Simulations or how to include possible faster

I have successfully implemented Samba in the past but I am now perplexed by a new installation which fails smbstatus with the message: "tdb((null)): tdb_open_ex: could not open file /var/cache/samba/brlock.tdb: No such file or directory Failed to open byte range locking database "tdb((null)): tdb_open_ex: could not open file /var/cache/samba/locking.tdb: No such file or directory ERROR:

> Dear R experts, >I am sorry for my inability. >I have the following dataset: > Qtot Itot >1 73 684 >2 64 451 >3 71 378 >4 65 284 >5 47 179 >6 31 117 >7 19 69 > >Now I need to perform Monte Carlo Pertutation test underlaying the following condition. > > >Condition > >In order to choose randomly (5000 times) for the Qtot

Hi, I created several figures and their titles should appear like this: Figure 1: Monte Carlo results for alternative estimators of structural parameters (N = 100, T = 5) Because N and T change across figures, my code includes the following lines: N.set <- 100 T.set <- 5 mtext(“Figure 1: Monte Carlo results for alternative estimators of structural

Hi, R-helpers, I'm trying to use R to do a Monte Carlo simulation and need the help. What I have is a matrix that consists of the probabilities for the persons to choose zones. For example, in the matrix shown below, each column represents a person, and each row represents a zone. So, the probability that the first person will choose the 2nd zone is 30%. 25% 30% 10% 30% 20% 0% 20% 50% 60%

.... Un código más optimizado para la aproximación Monte Carlo de la distribución de IH N=10000 # tamaño simulación Monte Carlo n=5 # numero de uniformes cdf.IH <-function(x,n,N) { z=replicate(N,sum(runif(n))) apply(outer(x,z,">="),1,mean) } x=seq(0,5,.1) y=cdf.IH(x,n=n,N=N) plot(x,y,type="l") Un saludo. Olivier ----- Mensaje original ----- De: