similar to: Help with arima.sim

Anna Crepes: Traubenzucker + Feldsalat spezielles Dressing (bringt selbst mit?) -------- Weitergeleitete Nachricht -------- Betreff: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: 26. Datum: Thu, 11 Dec 2014 15:34:39 +0100 Von: Markus <universe at truemetal.org> An: universe at truemetal.org Geschenke Moritz: dunkle Schokolade. Geschenke Anna: normale Schokolade. -------- Weitergeleitete

Dear help community, I'm a R-beginner and use it for my master thesis. I've got a mixed model and want to analyse it with lme. There are a lot Cofactors that coult be relevant. To extract the important ones I want to do the stepAIC, but always get an error warning. Structure of my data: data.frame': 72 obs. of 54 variables: $ Block : Factor w/ 3 levels

Hello, I have a question about function arima.sim I tried to somulate a AR(1) process, with no innovation, no error term. I used this code: library(forecast) e=rnorm(100,mean=0,sd=0) series=arima.sim(model=list(ar=0.75),n=100,innov=e)+20 Then I tried to applicate ti this series auto.arima function: mod1<-auto.arima(series,stepwise=FALSE,trace=TRUE,ic='aicc') The best model returned

Hi Everyone, I'm having a problem with arima.sim. Namely specifying inital values for the series. If I generate a random walk > vs = rnorm(100,0,1) > xs = cumsum(vs) and fit an ARIMA(1,0,0) to it > xarima = arima(xs,order=c(1,0,0)) > xarima Call: arima(x = xs, order = c(1, 0, 0)) Coefficients: ar1 intercept 0.9895 8.6341 s.e. 0.0106 6.1869 I should

I am trying to simulate time series data for an ar(1) and ma(1) process. I want the error term to have either a t distribution with 1 degree of freedom or a normal distribution with mean=0 and sd=1. Here is my code: error.model=function(n){rnorm(n,mean=0, sd=1)} data<-arima.sim(model=list(ar=c(0.1)), n=1000, n.start=200, start.innov=rnorm(200,mean=0, sd=1), rand.gen=error.model ) data

Hi all. I have a periodiv arma model and I want to simulate it. In S-plus, the following works for me: phi <- 0.9 theta <- 0 p <- 1 # period model <- list(ar=phi, ma=theta, period=p) Yt <- arima.sim(model, n=250) How do I do something like this "period=12" in R? I read help(arima.sim) but it doesn't tell. Thanks! Philip.

Hi, I tried to simulate an ARIMA model by using arima.sim(), say arima.sim(n=100,list(order=c(1,0,1),ar=0.6,ma=0.9,sd=1), but the acf and pacf of simulated data using acf() and pacf() are so much different from the theoritcal acf and pacf. For instance, in my case, ar=0.6 and ma=0.9, so the acf for all lags should be greater than 0 based on the theoritical calculation, but the acf of simulated

I have been trying to simulate from a time series with trend but I don't see how to include the trend in the arima.sim() call. The following code illustrates the problem: # Begin demonstration program x <- c(0.168766559, 0.186874000, 0.156710548, 0.151809531, 0.144638812, 0.142106888, 0.140961714, 0.134054659, 0.138722419, 0.134037018, 0.122829846, 0.120188714,

Apologies for thickness - I'm sure that this operates as documented and with good reason. However... My understanding of arima.sim() is obviously imperfect. In the example below I assume that x1 and x2 are similar white noise processes with a mean of 5 and a standard deviation of 1. I thought x3 should be an AR1 process but still have a mean of 5 and a sd of 1. Why does x3 have a mean of ~7?

Full_Name: Gang Liang Version: 1.7.1 OS: Debian/Woody Submission from: (NULL) (192.6.19.190) > print(arima.sim(list(ar=.3,order=c(1,1,1)), 30)) [1] 0.00000000 0.10734243 0.02907301 -1.23441659 -0.98819317 -2.82731975 [7] -2.69052512 -4.22884756 -5.02820635 -5.41514613 -6.20486350 -7.01040649 [13] -6.78121289 -5.41111810 -4.96338053 -5.42395408 -6.22741444 -5.75228153 [19] -6.07346580

Hi, I am using the arima.sim function to generate some AR time series. However, the function does not seem to produce exactly the same time series when I specify the innov parameter. For example > r <- rnorm(300) > x <- arima.sim(300, model=list(order=c(1,0,0),ar=c(.96)), innov=r, n.start=10) > y <- arima.sim(300, model=list(order=c(1,0,0),ar=c(.96)), innov=r, n.start=10) >

In some time series simulations I'm doing, I occasionally want the model to be ``white noise'', i.e. no model at all. I thought it would be nice if I could fit this into the arima.sim() context, without making an exceptional case. I.e. one ***could*** do something to the effect if(length(model)==0) x <- rnorm(n) else x <- arima.sim(model,n) but it would be more suave if one

Hello, I have used the arima.sim function to generate a lot of time series, but to day I got som results that I didn't quite understand. Generating two time series z0 and z1 as eps <- rnorm(n, sd=0.03) z0 <- arima.sim(list(ar=c(0.9)), n=n, innov=eps) and z1 <- arima.sim(list(ar=c(0.9)), n=n, sd=0.03), I would expect z0 and z1 to be qualitatively similar. However, with n=10 the

Hi, Before I re-invent the wheel, is there a function in R similar to S+'s arima.sim, i.e., a function that simulates arima processes. ts and tseries packages don't seem to have such function, but I may have overlooked it. Thank you for your time, Alvaro Novo R Version 1.1.1 SuSE 6.4 Linux KDE 2.0 Beta 5 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Hi, I have been using arima.sim from the stats package recently, and I'm wondering why I get different results when using what seem to be the same parameters. For example, I've given examples of three different ways to run arima.sim with what I believe are the same parameters. It's my understanding from the R documentation that rnorm is the default function for rand.gen if not

Hello, I use V 2.11 and my question is: Is one present Sieve with this version or MUST this specially compiles? # 2.0.11 (31d8d43fa6b5): /etc/dovecot/dovecot.conf # OS: Linux 2.6.29.4 i686 Debian wheezy/sid -- Mit freundlichen Gr??en, Jim Knuth P.S.: Bitte senden Sie KEINE HTML-Mails! ##### Zufallszitat: Der Fanatismus ist die einzige 'Willensst?rke', zu der auch die Schwachen

OMG.. how embarassing.. that was my personal reminder E-Mail for x-mas dinner. Not meant for this list. Please ignore. Shame on me.. *blushing* LOL. Am 12.12.2014 um 21:19 schrieb Markus: > Anna Crepes: Traubenzucker > + Feldsalat spezielles Dressing (bringt selbst mit?) > > > > -------- Weitergeleitete Nachricht -------- > Betreff: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: 26.

Dear list subscriber, I am quite puzzled by the behaviour of processing Sweave files within an R session, i.e. Sweave("foo.Rnw") versus R CMD Sweave foo.Rnw In the former the environmental variable 'SWEAVE_STYLEPATH_DEFAULT = TRUE' is obeyed (this is set in etc/Renviron.site as well as under the users home directory in .Renviron). That is the hard-coded path to Sweave.sty is

Dear list subscriber, suppose, I do have a minimal Sweave file 'test.Rnw': \documentclass{article} \begin{document} <<printx>>= x @ \end{document} Within R, I define the following function: f <- function(x){ Sweave("test.Rnw") } The call: f(x = 1:10) results in the following error message: > f(x = 1:10) Writing to file test.tex Processing code chunks

I'm using the arima function to estimate coefficients and also using predict.Arima to forecast. This works nicely and I can see that the results are the same as using SAS's proc arima. I can also take the coefficent estimates for a simple model like ARIMA(2,1,0) and manually compute the forecast. The results agree to 5 or 6 decimal places. I can do this for models with and without