similar to: Specifying initial values for arima.sim

I have been dabbling with getting some of our Windows applications running under Linux using the CodeWeavers preview 20010629. I am making good progress and am very pleased with the results. However I am currently stuck on one problem. The Windows software is developed using Borland Delphi and the application includes a number of forms that are intended to be displayed full screen. The

Nurza, Try running a while loop steping out until you have a start and finish thats the function is opposite in sign. You need a "start" and "finish" where F is + and - on either side of the loop. Graphing F might help. step<-10 checkme<-F(start)*F(finish+step) while(checkme>0){ initialstep<-initialstep*2 checkme<-F(start)*F(finish+step) }

Hello, A couple of ideas... Im not clear on your whole problem however... Consider making use of the lgamma function, which returns the natural log of the gamma function. This may help. The gamma function gets awfully, big very fast. Also multivariable likelihoods can be bumpy like a mountain range, with minor peaks and valleys. It is possible that your likelihood has such a shape. Maybe

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, 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) >

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, 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 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 This is my first post to this group, so apologies in advance if I get it wrong. I would like to know how the prediction for arima models works in R. I have a time series to which I fit an arima model, of varying AR and MA orders. I then use the predict function to project it forward. I have also written my own function to perform the prediction, but it gives different answers to Arima.predict

Hi, Following convention below: y(t) = Ax(t)+Bu(t)+eps(t) # observation eq x(t) = Cx(t-1)+Du(t)+eta(t) # state eq I modified the following routine (which I copied from: http://www.stat.pitt.edu/stoffer/tsa2/Rcode/Kall.R) to accommodate u(t), an exogenous input to the system. for (i in 2:N){ xp[[i]]=C%*%xf[[i-1]] Pp[[i]]=C%*%Pf[[i-1]]%*%t(C)+Q siginv=A[[i]]%*%Pp[[i]]%*%t(A[[i]])+R

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?

> I have been trying to get speex to work for a while now, and it's been > a real teeter-totter ride. For a long time I noticed that I will get a > project to work and then without changing any code and programming it to > an eprom/flash the project will not work. That's an Heisenbug and the most common cause is uninitialised data. I found innov_save will write over memory

This must have been an enormous pain to track down. The manual alloc routine in the TI directory (user_misc.h) clears the allocated memory, but maybe you have changed this. >> it will just start filling data in, which it shouldn't. I see that >> innov_save is set at the beginning of a for loop at: >> for (sub=0;sub<st->nbSubframes;sub++) >> { > ...

Hi Jean-Marc, Hi Jim, I have also seen some problems with the 1.1.8 release on the C55x. So far I have boiled down the issues to the following: 1) We need our own "fixed_xx.h" header file. I don't know why, and haven't had time to investigate, but there is a definite improvement when I use the attached fixed_c55x.h file which has turned all the maths into inline functions.

hi, I am using speex1.2beta2 on a TI 54x on narrow band I have been trying to get speex to work for a while now, and it's been a real teeter-totter ride. For a long time I noticed that I will get a project to work and then without changing any code and programming it to an eprom/flash the project will not work. It turns out it was a value called innov_save. I found this bugger by zero

Hi, I would like to make a replication of 10 of a linear, first order Autoregressive function, with respect to the replication of its innovation, e. for example: #where e is a random variables of innovation (from GEV distribution-that explains the rgev) #by using the arima.sim model from TSA package, I try to produce Y replicates, with respect to every replicates of e, #means for e[,1], I want

Dear useRs, I am trying to model a time series with a transfer function. I think it can be put into the ARMA framework, and estimated with the 'arima' function (and others have made similar comments on this list). I have tried to do that, but the results have so far been disappointing. Maybe I am trying to make 'arima' do something it can't... The data are time series of

Hi All, This question was brought up some time ago but I never saw a reply so I'd like to bring it up again. When using ROracle package (version 0.5-5), I am unable to run any queries that are greater than 4000 characters in length. If I do, I get the following message: Error in oraPrepareStatement(con, statement, bind=NULL) : RS-DBI driver: (too long a statement -- it must has less than

Dear useRs, I want to simulate a time series (stationary; the distribution of values is skewed to the right; quite a few ARMA absolute standardized residuals above 2 - about 8% of them). Is this the right way to do it? #-------------------------------- load("rdtb") #the time series > summary(rdtb) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.11800 -0.65010 -0.09091

I noticed that in the narrow band and wide band destroy functions only the main pointer is being freed. I think that it should be: void nb_decoder_destroy(void *state) { DecState *st; st=(DecState*)state; speex_free (st->inBuf); speex_free (st->excBuf); speex_free (st->innov); speex_free (st->interp_qlpc); speex_free (st->qlsp); speex_free