On 23/07/2008, at 7:52 AM, cathelf wrote:
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 should
I do if I
want to simulate 1000 independent group of this data? Each group
contains
100 AR(1) data. So it is a matrix of 1000*100. Each row is a AR(1).
I think
there should be a quicker way to do that? (the easies way is
simulate ar(1)
1000 times, but it waste time, I think).
What else can you do? To simulate 1000 independent realizations
of an AR(1) process you need to, uh, simulate 1000 independent
realizations of an AR(1) process. Like.
For compact ***code*** you could write something like:
junk <- matrix(unlist(lapply(1:1000,function(x){arima.sim(list
(ar=0.3),100)+mean})),nrow=1000,byrow=TRUE)
(as long as ``mean'' is there in your global workspace).
This took 0.619 seconds on my Imac; not too much time wasted.
But by turning your results into a matrix, you lose the time series
attributes of your simulated series. Are you sure you need a matrix?
You could simply create a *list* of length 1000, each entry of which
is a realization of an AR(1) process. Just by doing:
junk <- lapply(1:1000,function(x){arima.sim(list(ar=0.3),100)+mean}))
Each entry of junk will be an object of class "ts" --- which might be
a Good Thing.
cheers,
Rolf Turner
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