search for: simulators

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

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Hello, All: ????? The default "simulate" method for lm and glm seems to ignore the sampling variance of the parameter estimates;? see the trivial lm and glm examples below.? Both these examples estimate a mean with formula = x~1.? In both cases, the variance of the estimated mean is 1. ??? ??????? * In the lm example with x0 = c(-1, 1), var(x0) = 2, and

I have found the "simulate" method (incorporated in some packages) very handy. As far as I can tell the only class for which simulate is actually implemented in base R is lm ... this is actually a little dangerous for a naive user who might be tempted to try simulate(X) where X is a glm fit instead, because it defaults to simulate.lm (since glm inherits from the lm class), and the

On 2019-12-27 04:34, Duncan Murdoch wrote: > On 26/12/2019 11:14 p.m., Spencer Graves wrote: >> Hello, All: >> >> >> ? ????? The default "simulate" method for lm and glm seems to ignore the >> sampling variance of the parameter estimates;? see the trivial lm and >> glm examples below.? Both these examples estimate a mean with formula = >>

I have been talking with Gabriel Wainer about the possibility of some community building with R and simulation workers. The conference announced below is just before UseR (but in that capital to the north of Washington). If anyone on this list is participating or thinking of participating, perhaps they could let me know off-list. Ottawa-Gatineau R User Group would likely try to set up some

Dear R helpers Suppose I have a portfolio of securities with exposure to Equity, Bonds and Forex (say $ 1000000 each). Is there any fucntion in R that will help me calculate Value at Risk (VaR) using Monte carlo Simulation , Historic simulation and Variance - Covariance Simulation. With regards Maithili

Hi all, I have the following R script : #!/usr/bin/Rscript out_file = "hybrid.pdf" pdf(out_file, height=8.5, width=11) myvalues_1M <- read.csv("hybrid_sims_1M.csv",head=TRUE,sep=",") plot(myvalues_1M$num_sims_per_thread,myvalues_1M$time_per_sim,xlab="Number of Simulations per Thread",ylab="Time per 1 million Simulations(in

Full_Name: Reginaldo Constantino Version: 2.8.0 OS: Ubuntu Hardy (32 bit, kernel 2.6.24) Submission from: (NULL) (189.61.88.2) For many tables, chisq.test with simulate.p.value=TRUE gives a p value that is obviously incorrect and inversely proportional to the number of replicates: > data(HairEyeColor) > x <- margin.table(HairEyeColor, c(1, 2)) >

On 26/12/2019 11:14 p.m., Spencer Graves wrote: > Hello, All: > > > ????? The default "simulate" method for lm and glm seems to ignore the > sampling variance of the parameter estimates;? see the trivial lm and > glm examples below.? Both these examples estimate a mean with formula = > x~1.? In both cases, the variance of the estimated mean is 1. That's how

Dear R users, I need to run 1000 simulations to find maximum likelihood estimates. I print my output as a vector. However, it is taking too long. I am running 50 simulations at a time and it is taking me 30 minutes. Once I tried to run 200 simulations at once, after 2 hours I stopped it and saw that only about 40 of them are simulated in those 2 hours. Is there any way to make my simulations

Hi All, I,m trying to do a GARCH simulation in R 2.3.0 release in Windows XP. I've seen garchsim function but that is for garch (1,1) and ?garch gives an example for ARCH simulation. Can anyone help me how can i extend the help shown in ?garch to GARCH simulation? Please help me in this regard. Thanks, Sumanta Basak.

hi R-help! I would like to know a simple and easy way (if posible) to do the following data manipulation. I have a matrix of experimental data (with replicae in time) >experiment<-data.frame(times=c(0,0,10,10,20,20,30,30),expval=c(1,1,2,2,3,3,4,4)) > experiment times expval 1 0 1 2 0 1 3 10 2 4 10 2 5 20 3 6 20 3 7 30 4 8

Hello, can anyone tell me if R has any special function for simulating the structure of human populations? Something like the genetic algorithm? I need to simulate a sample of a population with a specific structure. Is there something on R that can help me? Thanks to everyone. __________________________________________________________

I'd like to carry out a Monte Carlo simulation test where given data is a contingency table. I think this is something to do with using rmultinonom(), but I'm not sure how to code this, to simulate contingency tables. Could anyone please help with how to use R to simulate contingency tables like this? -- View this message in context:

Dear All, I have been trying to simulate data from a fitted glm using the simulate() function (version details at the bottom). This works for lm() fits and even for lmer() fits (in lme4). However, for glm() fits its output does not make sense to me -- am I missing something or is this a bug? Consider the following count data, modelled as gaussian, poisson and binomial responses: counts

Hello, I created an array to hold the results of a series of simulations I'm running: d.eta <- array(0,dim=c(3,3,200)) <simulation goes here and populates the array but it's not important> Then I tried to save the results using this: save(d.eta,file="D:/Simulation Results/sim 9-23-11 deta") When I later tried to reload them using this: d.eta <-

Hi, sorry for bothering your guys again. I want to simulate 100 AR(1) data with cor(x_t, x_t-1)=rho=0.3. The mean of the first 70 data (x_1 to x_70) is 0 and the mean of the last 30 data (x_71 to x_100) is 2. Can I do it in the following way? x <- arima.sim(list=(ar=0.3), 100) mean <- c(rep(0, 70), rep(2, 30)) xnew <- x+mean If the above code to simulate 100 AR(1) data is right, what

Dear Friends, I want to know if any one could give me help on this. Suppose you want to simulate the p-value of a test statistic but you do not know how the distribution of the test statistic, how can one simulate the p-value. It will be helpful to get an example code in R on this Respectfully Submitted, Clement

Dear R-users, I'm trying to do some spatial simulation. I have two covariates, Z and C. I want to examine their relationship under different spatial distribution. I have no problem simulating completely spatial random process but I'm totally stuck on poisson (cluster) pattern. I already have a dataset with Z and C (obs=575) and I know the relationship between them. Using these 575