similar to: Arima bug?

I guess this is more r-devel than r-help. Note, I am just the messenger - I have no idea what the user is trying to model here. arima() crashes R (segfault) with Linux R-2.7.0, Solaris R-2.6.0: *** caught segfault *** address 42400000, cause 'memory not mapped' Traceback: 1: .Call(R_getQ0, phi, theta) 2: makeARIMA(trarma[[1]], trarma[[2]], Delta, kappa) 3: arima(x, c(1, 0, 1), c(1,

Thanks a lot to everybody that helped me out with this. Conclusions: (1) In order to edit arima in R: >fix(arima) or alternatively: >arima<-edit(arima) (2) This is not contained in the "Introduction to R" manual. (3) A "productive" fix of arima is attached (arma coefficients printed out and error catched so that it doesn't halt parent loops to search for

I have a question on scope/reference/value type of variables with 'R'. The issue cam up first when I look at the arima code. I see code like: myupARIMA <- function(mod, phi, theta) { . . . . mod } Then armafn <- function(p, trans) { . . . . Z <- upARIMA(mod, trarma[[1]], trarma[[2]]) . . . . res <- .Call(R_ARIMA_Like, x,

Hi, the function makeARIMA(), designed to construct some state space representation of an ARIMA model, uses a C function called getQ0, which can be found at the end of arima.c in R source files (library stats). getQ0 takes two arguments, phi and theta, and returns the covariance matrix of the state prediction error at time zero. The reference for getQ0 (cited by help(arima)) is:

Dear R People: I have the following situation. I have observations that are 128 samples per second, which is fine. I want to fit them with ARIMA models, also fine. My question is, please: when I do my forecasting, do I need to do anything special to the "n.ahead" parm, please? Here is the initial setup: > xx <- ts(rnorm(128),start=0,freq=128) > str(xx) Time-Series

Dear Madams/Sirs, Hello. I am using the gls function to specify an arma correlation during estimation in my model. The parameter values which I am sending the corARMA function are from a previous fit using arima. I have had some success with the method, however in other cases I get the following error from gls: "All parameters must be less than 1 in absolute value". None of

Dear All, I have a model to predict time series data for example: data(LakeHuron) Lake.fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t =

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

I am new in R. I can simulate Arma, using Arima.sim However, I want to simulate an Arima Model. Say (1-B)Zt=5+(1-B)at. I do not know how to deal with 5 in this model. Can any one could help me? Thank you very much! Regards, -- View this message in context: http://www.nabble.com/simulate-arima-model-tp23239027p23239027.html Sent from the R help mailing list archive at Nabble.com.

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.

getQ0 function is used in arima. I am trying to recode arima function in perl ( I have to use this function in grid. We have restrictions to install R package in large set of machines ) The getQ0 acts differently for same kind of input ( I hope ). > init [1] 18.368400 0.415422 0.415422 > arma [1] 1 1 0 1 1 1 0 > transform.pars [1] 1 > trarma = .Call(stats:::R_ARIMA_ transPars,

I am calling auto.arima with a time series that is about 186 observations long with a frequency of 52. With some time series I get: 1:last.nonzero: result would be too long a vector Is there something that I can do to the data to avoid this error? Thank you. Kevin

Hi All, Sorry to bother you. I'm trying to estimate a set of discrete choice data in R with mixed logit models where one coefficient is random and normally distributed. I've searched on the R help archive and don't see much information very specific to what I'm doing, so I write the code myself, which involves simulated maximum likelihood. But it doesn't work, as I compare

Dear List, A posting to R-Help exposed this problem with the print method for objects of class Arima: > set.seed(1) > x <- arima.sim(n = 100, list(ar = 0.8897, ma = -0.2279)) > mod <- arima(x, order = c(1,0,1)) > coefs <- coef(mod) > mod2 <- arima(x, order = c(1,0,1), fixed = coefs) > mod2 Call: arima(x = x, order = c(1, 0, 1), fixed = coefs) Coefficients: Error

Dear all, I just have a short question regarding the forecasting of ARIMA models with external regressors. I tried to program a ARX(1) model arx.mod <- arima(reihe.lern, order = c(1, 0, 0), seasonal = list(order = c(0, 0, 0), period = 52), xreg = lern.design, include.mean = TRUE) for which I need to estimate the next (105th) value. Xreg=lern.design is - at this time - 104 rows long. I

Dear List, I am trying to do model averaging for a negative binomial model using the package AICcmodavg. I need to use glm() since the package does not accept glm.nb() models. I can get glm() to work if I first run glm.nb and take theta from that model, but is there a simpler way to estimate theta for the glm model? The two models are: mod.nb<-glm.nb(mantas~site,data=mydata)

Hello all, I use arima to fit the model with fit <- arima(y, order = c(1,0,1), xreg = list.indep, include.mean = TRUE) and would like to use predict() to forecast: chn.forecast <- rep(0,times=num.record) chn.forecast[1] <- y[1] for (j in 2:num.record){ indep <- c(aa=chn.forecast[j-1], list.indep[j,2:num.indep]) # this is the newxreg in the

Hi, could anyone tell me how predict() predicts the new value(s), of a MA(1) arima-modell. its really easy to make it with an AR(1), knowing the last term, but how can i or R know the last error? It would also help if somebody could tell me how to find the "open" source of the function predict(). Thanks and sorry for my poor english. -- View this message in context:

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

I just realized after some tips and a little digging that what I was trying to do "manually" has already been done. I was trying to fit my data using 'lm' then taking the "residual" data and trying to do a spectral estimate (for seasonality) usiing fft and then passing the "residual" of all of that to arima to get the irregular portion of the time series