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:

I'll second Bert's comments, also assuming this is not homework. In addition: Your use of "mid-point" is not a standard one (in my world), nor perhaps is that of "simulate". Let me attempt to re-state your problem: You wish to choose 10-tuples of integers 0 <= k <= 100 satisfying 1) 0 <= k_i <= 100 for i = 1:10; and 2) k_(i+1) - k_(i) >= 5 for i =

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

JRG: I don't think your specification is correct -- perhaps just a thinko. I think a 10-tuple of "reals" (scare quotes because of computer precision) with your specifications is what is wanted. Bert "An educated person is one who can entertain new ideas, entertain others, and entertain herself." On Sat, May 31, 2025 at 3:43?PM JRG <jrg at loesl.us> wrote: >

If this is a real problem and not homework, can you tell us the context? It is not at all clear (to me) what you mean by "simulate", i.e. what your target distribution is, which may depend on/be defined by the context. Bert "An educated person is one who can entertain new ideas, entertain others, and entertain herself." On Sat, May 31, 2025 at 11:52?AM Brian Smith

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,