similar to: Simulating Change Attribute in Samba

Dear All   I know that you do not have to help me (as this is not a pure R problem) but please do, i am new to R as a CPI compiler, i just need to do a sample to see which sampling method best works in different situations, therefore since this is for practice purposes nobody will finance a real project thats why i need you to help me direct me as to how simulate data (just direct me,not 100%

Dear All   I know that you do not have to help me (as this is not a pure R problem) but please do, i am new to R as a CPI compiler, i just need to do a sample to see which sampling method best works in different situations, therefore since this is for practice purposes nobody will finance a real project thats why i need you to help me direct me as to how simulate data (just direct me,not 100%

Sir, I am working on fitting distribution on multivariate financial data and then simulate observations from that fitted distribution. I use stepAIC.ghyp() function of 'ghyp' library which select the best fitted distribution from generalized hyperbolic distribution class on the given dataset. data(indices) # Multivariate case: aic.mv <- stepAIC.ghyp(indices, dist =

Hello everyone, I have estimated a VEC using functions of package urca. Now I need to simulate several trajectories of the variables of the model based in the information of the model. To do that first I converted the VEC to a VAR using vec2var (from package vars) and then I converted de VAR to an ARMA using ARMA function of package dse. The idea is to use the function simulate (from package

Hello, All: I want to simulate future observations from fits to heteroscedastic data. A simple example is as follows: (DF3_2 <- data.frame(y=c(1:3, 10*(1:3)), gp=factor(rep(1:2, e=3)))) # I want to fit 4 models # and simulate future observations from all 4: fit11 <- lm(y~1, DF3_2) fit21 <- lm(y~gp, DF3_2) library(nlme) (fit12 <- lme(y~1, data=DF3_2,

Dear List, Does anyone know how to simulate data from a GLM object correponding to values of the independent (x) variable that do not occur in the original dataset? I have tried using simulate(), but it generates a new value of the dependent variable corresponding to each of the original x-values, which is not what I need. Ideally I whould like to simulate new values for GLM objects

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:

Hi, I wish to simulate some data from a Weibull distribution. The rweibull function in R uses the parameterisation 'with shape parameter a and scale parameter b has density given by f(x) = (a/b) (x/b)^(a-1) exp(- (x/b)^a)'. However, it would be much more useful for me to simulate data using a different parameterisation of the Weibull, with shape a1 and scale b1, namely f(x) =

Hi, I have a data frame containing a column of human judgments, some of which are missing: > pr[3] label 1 4 2 4 3 4 4 4 5 NA 6 3 7 3 8 3 9 3 10 NA 11 NA 12 NA 13 2 14 2 15 2 16 NA 17 1 18 -1 19 -1 20 -1 Accompanying this is a matrix containing multinomial probabilities for the missing values, note that they

Hey all, A lot of people use factory girl to simulate behavior without affecting database, particularly for testing. I have a situation where this is not for testing but rather someone clicks on button to simulate behavior but we create a User object in order to run the simulation but while I want the results of simulation to be stored in a simulation table I dont want the user to be actually

Dear All ? I know that you do not have to help me but please do, i am new to R as a CPI compiler, i just need to do a sample to see which sampling method best works in different situations, therefore since this is for practice purposes nobody will finance a real project thats why i need you to help me direct me as to how simulate data (just direct me,not 100% help). See my attachment for problem

..for a "unit test" i need a lot of rows in my database, so i simulate. My problem, using Win2k,R.1.7.0, 256RAM is that i'm getting memory-erros go about the 1000.000 border , but i need bigger test data. Ok is approriate buy more RAM, but is there a possibilty simulate a lot of single rows, one after another and between this 2 steps -> Add th row to database and delete them

All, I'd like to simulate a vector that is formed from many distinct distributions and avoid a loop if possible. E.g, consider: mu = c(1, 2, 3) sigma = c(1, 2, 3) n = c(10, 10, 10) And we simulate a vector of length 30 that consists of N(mu[i], sigma[i]) distributed data, each of length n[i]. Of course for just three groups we can simply write it out as: DV = c(rnorm(n[1], mu[1],

On 09/10/2013 12:03 PM, Arun Viswanath wrote: > Hi All, > I'm need to simulate libvirt API's say to mock the libvirt API > responses. (Actually I need to simulate qemu API's response). Because of my > project needs I need to write this simulated libvirt server in Java. I > believe the simulated libvirt can be written as java RPC which should > capable to receive

Hi, I'd like to know if there's anything in R that could help me do that. Let's suppose I have a density function of a random variable, for example f(x) = (x^3)/4 0 < x < 2 and I would like to simulate it. For the common distributions (exponencial, gamma, cauchy) there are the r-functions (rgamma, rexp, runif, rcauchy, and so on).. But when the variable I want to simulate is not

Dear useRs, I have written an ecological model, based on the epidemiology SIR model. I've been trying to simulate it in R. However, I can't simulate it properly. Two guesses: my script isn't right; I'm not setting the parameters properly I have uploaded an image to the model here: http://img24.imageshack.us/img24/743/imagemutr.jpg The script I am using is as it follows:

Dear R-Help List, I'm trying to simulate data from a conditional distribution, and haven't been able to modify my existing code to do so. I searched the archives, but didn't find any previous post that matched my question. n=10000 pop = data.frame(W1 = rbinom(n, 1, .2), W2 = runif(n, min = 3, max = 8), W3 = rnorm(n, mean=0, sd=2)) pop = transform(pop, A = rbinom(n, 1,

Full_Name: Stan Miles Version: 1.5.0. OS: Windows 2000 Server Submission from: (NULL) (130.63.74.220) Short Description of the Problem: I use R to simulate a random variable, with mean 0.14 and variance 0.2 . I simulate 20 sets, 30000 realizations/set. I take the average of each set, and all 20 of the averages are much higher than 0.14. In fact, they are All about 2-3 stdev higher. It

Hello, Using the dse package I have estimated a VAR model using estVARXls(). I can perform forecasts using forecast() with no problems, but when I try to use simulate() with the same model, I get the following error: Error in diag(Cov, p) : 'nrow' or 'ncol' cannot be specified when 'x' is a matrix Can anyone shed some light on the meaning of this error? How can I

Hi, I have a sample of size 100 from a function in interval [0,1] which can be assumed to come from a scalar-valued stationary Gaussian process. There are about 500 observation points in the interval. I need an effective and fast way to simulate from the Gaussian process conditioned on the available data. I can of course estimate the mean and 500x500 covariance matrix from data. I have searched