Thanks for all the help! Ill give them a shot and compare the results. On Thu, Dec 11, 2014 at 12:29 AM, Rolf Turner <r.tur...@auckland.ac.nz> wrote:
> On 11/12/14 20:09, Michael Selevan wrote: > >> This makes sense, thank you for the thorough response! >> >> One follow up question though. Would your #2 option be the same as, say, >> not using the rand.gen at all and providing the following parameters >> instead? >> >> y3 <- arima.sim(n=10, list(ar=0.8), innov=rnorm(10, sd=0.2)) >> > > No. This will call rand.gen=rnorm() to generate innov.start, so > start.innov will be generated with a standard deviation of 1 rather > than 0.2. > > >> or even >> >> y4 <- arima.sim(n=10, list(ar=0.8), innov=rnorm(10, sd=0.2), >> innov.start=rnorm(10, sd=0.2)) >> > > Why didn't you try it? It gives an error, saying start.innov is too > short. It needs to be of length *28* according to the error message. Note > that "innov.start" should read "start.innov". My bad; > I got the argument name wrong (on the second attempt!) in my previous > posting. > > y4 <- arima.sim(n=10, list(ar=0.8), innov=rnorm(10, sd=0.2), > start.innov=rnorm(28, sd=0.2)) > > should I think be the same as y2. ***You*** try it and see! > > (Set a seed prior to each calculation; that's what seeds are for!) > > cheers, > > Rolf Turner > > <SNIP> > > On Wed, Dec 10, 2014 at 1:04 PM, Rolf Turner <r.tur...@auckland.ac.nz >> <mailto:r.tur...@auckland.ac.nz>> wrote: >> >> >> Please see below. >> >> >> On 10/12/14 20:21, Michael Selevan wrote: >> >> Hello, >> >> I am attempting to plot an AR(1) model with a standard deviation >> and I am a >> little confused as how to do that. I have been looking through the >> interwebs and some documentation and I see that there is >> potentially a few >> different ways to do this. >> >> First, simply using the documentation I came up with the command >> >> arima.sim(n=10, list(ar=0.8), innov=rnorm(10, sd=0.2)) >> >> which would give me the standard deviation I want. Or I believe >> that to be >> the case. However, after some more searching and googling, I saw >> an example >> where someone used this as a means of adding the AR error term >> >> error.model=function(n){rnorm(__n, sd=0.2)} >> >> >> y = arima.sim(n=10, list(ar=0.8), innov=rnorm(10, sd=0.2), >> rand.gen= >> error.model) >> Now, I am a little confused by this. Would having the error term >> in the >> innov parameter as well as the rand.gen be redundant? What would >> be the >> expected differences between the two? Should only 1 be used? >> >> Just looking for some clarification. Been searching and havent >> found too >> many examples that explicitly state how to add the error term to >> an AR(1) >> model. >> >> >> It's a little bit subtle, but in a way that's not too important. >> >> There is, in addition to "innov" a starting innovations vector >> "start.innov" that is needed. If either innov or start.innov is not >> supplied their values get supplied by rand.gen(). So in your second >> call to arima.sim() ***start.innov*** is being supplied by rand.gen() >> (but ***innov*** will be taken to be equal to the argument supplied. >> >> In your first call, where rand.gen() is not specified (and start.innov >> is not specified), the supplied value of innov will be used and >> start.innov will be produced by the *default* value of rand.gen() >> which is rnorm(), you'll get rnorm(n.start,0,1). >> >> Thus in your first call, the starting innovations will be done with >> a different standard deviation than the other innovations. Which is >> probably not what you want. >> >> Hence the second call is correct --- but it *is* kind of redundant >> and confusing to supply "innov" as well as rand.gen(). The code >> would be >> clearer if "innov" were dispensed with and it was just left to >> rand.gen() to do the work. >> >> The following is not important, but it might be mystifying: If you >> leave out "innov" you will get a different result --- even if you >> set a seed for the random number generators a priori. E.g.: >> >> # 1. >> set.seed(42) >> innov <- rnorm(10,0,0.2) >> error.model=function(n){rnorm(__n, sd=0.2)} >> y1 <- arima.sim(n=10, list(ar=0.8), innov=innov, >> rand.gen=error.model) >> >> # 2. >> set.seed(42) >> error.model=function(n){rnorm(__n, sd=0.2)} >> y2 <- arima.sim(n=10, list(ar=0.8),rand.gen=error.__model) >> >> The vectors y1 and y2 are (surprisingly until you think carefully) >> different. >> >> This is because for y1, innov.start is generated *after* innov is >> generated, whereas for y2 innov.start is generated *before* innov is >> generated. The first entry of innov for y1 will be the same as the >> first entry of innov.start for y2. So the sequence of innovations is >> different. >> >> Bottom line: I would recommend *not* using the "innov" argument and >> just specifying rand.gen() to get the standard deviations that you >> want. >> >> HTH >> >> cheers, >> >> Rolf Turner >> >> -- >> Rolf Turner >> Technical Editor ANZJS >> >> >> >> >> -- >> J. Michael Selevan >> > > > -- > Rolf Turner > Technical Editor ANZJS > -- J. Michael Selevan [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.