similar to: Arima with an external regressor

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Hi, I am trying to understand exactly what xreg does in arima. The documentation for xreg says:"xreg Optionally, a vector or matrix of external regressors, which must have the same number of rows as x." What does this mean with regard to the action of xreg in arima? Apparently somehow xreg made the following two arima fit equivalent in R: arima(x, order=c(1,1,1), xreg=1:length(x)) is

I am using arima to develop a time series regression model, I am using arima b/c I have autocorrelated errors. Several of my independent variables are categorical and I have coded them as factors . When I run ARIMA I don't get any warning or error message, but I do not seem to get estimates for all the levels of the factor. Can/how does ARIMA handle factors in xreg? here is some example

As I am reading ?arima, only NA entries in the argument fixed= imports. The following seems to indicate otherwise: x <- arima.sim(model=list(ar=0.8), n=100) + (1:100)/50 > t <- 1:100 > mod1 <- lm(x ~ t) > > init1 <- c(0, coef(mod1)[2]) > fixed1 <- c(as.numeric(NA), 0) > > arima(x, order=c(1,0,0), xreg=t, include.mean=FALSE, init=init1, fixed=fixed1)

Dear R-help-archive.. I am trying to figure out how to make arima prediction when I have a process involving multivariate time series input, and one output time series (output is to be predicted) .. (thus strictly speaking its an ARMAX process). I know that the arima function of R was not designed to handle multivariate analysis (there is dse but it doesnt handle arma multivariate analysis, only

Hello R users, Hope everyone is doing great. I have a dataset that is in .csv format and consists of two columns: one named Period (which contains dates in the format yyyy_mm) and goes from 1995_10 to 2007_09 and the second column named pcumsdry which is a volumetric measure and has been formatted as numeric without any commas or decimals. I imported the dataset as pauldataset and made use of

Hi, I want to give a summary or anova for "arima" model in R, as "summary", and "anova" for "lm". As including various intervention factors in arima(xreg = ) part, I want to assess the significancy of thse factors. I can do it using interrupted analysis of time series by linear regression, but want to see whether arima model works for the data first.

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Hello I want to fit an AR model were two of the coefficients are fixed to zero (the second and third ar-coefficients). I used the "arima" function with the "fixed" argument but the ar3 coefficient is not set to zero: ============================================== > arima(Y, order=c(4,0,0), xreg=1:23, fixed=c(NA,0,0,NA,NA,NA)) Call: arima(x = Y, order = c(4, 0, 0), xreg =

When I run predict.Arima in my code, I get warnings like: Warning message: In cbind(intercept = rep(1, n), xreg) : number of rows of result is not a multiple of vector length (arg 1) I think this is because I'm not running predict.Arima in the same environment that I did the fit, so the data object used in the fit is no longer present. Looking at the predict.Arima source,

Hello, Does anyone know how the parameter estimates are calculated for xreg variables when called as part of an arima() command, or know of any literature that provides this info? In particular, I was wondering if there is a quick way to compare different combinations of "xreg" variables in the arima() fit in the same way that you would in multiple regression (using AIC & R^2

Hello all, I''m having fun again with the arima function. This time I read in: http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm <<It has recently been suggested (by a reliable source) that using xreg in arima() does NOT fit an ARMAX model [insert slap head icon here]. This will be investigated as soon as time permits.>> (by R.H. Shumway & D.S. Stoffer)

Hi, Using arima on data that are in a data frame, especially when adding xreg, would be much easier if the arima function contained 1) a "data=" argument 2) the possibility to include the covariate(s) in a formula style. Ideally the call could be something like > arima(symptome, order=c(1,0,0), xreg=~trait01*mesure0, data=anxiete) ( or arima(symptome~trait01*mesure0,

Hi, All, This is my program ts1.sim <- arima.sim(list(order = c(1,1,0), ar = c(0.7)), n = 200) ts2.sim <- arima.sim(list(order = c(1,1,0), ar = c(0.5)), n = 200) tdata<-ts(c(ts1.sim[-1],ts2.sim[-1])) tre<-c(rep(0,200),rep(1,200)) gender<-rbinom(400,1,.5) x<-matrix(0,2,400) x[1,]<-tre x[2,]<-gender fit <- arima(tdata, c(1, 1, 0), method = "CSS",xreg=t(x))

Hello, I am a beginner user of R and I would like to estimate a model with AR errors. I use arima function: modele <-arima(conso,xreg=var.exogenes,order=c(ordre,0,0),include.mean=TRUE,method ="CSS") My inputs are dummies for each month except one, and the same thing for each day and each hour. I have this error message: Warning message: possible convergence problem: optim gave

This should be very easy, but alas, I'm very new to R. My end goal is to calculate p-values from arima(). Let's say I just ran this: > MyModel <- arima(y[1:58], order=c(1,0,0), xreg=MyData[1:58,7:14], > method="ML") > MyModel And I see: arima(x = y[1:58], order = c(1, 0, 0), xreg = MyData[1:58, 7:14], method = "ML") Coefficients: ar1

Hi people, I´m trying to development a simple routine to run many Arima models result from some parâmeters combination. My data test have one year and daily level. A part of routine is: for ( d in 0:1 ) { for ( p in 0:3 ) { for ( q in 0:3 ) { for ( sd in 0:1 ) { for ( sp in 0:3 ) { for ( sq in 0:3 ) {

On 21 May 2015, at 12:49 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: >>>>>> peter dalgaard <pdalgd at gmail.com> >>>>>> on Thu, 21 May 2015 11:03:05 +0200 writes: > >> On 21 May 2015, at 10:35 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: > >>>> >>>> I noticed that

Hi all, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] So, I run the following code: for (i in 1:rep) { index=sample(4,15,replace=T) final<-do.call(rbind,lapply(index,function(i)

Hola! I got problems using an objects returned from arima (in KalmanSmooth(my.ts, ModArima$model), because my.ts showed up to have storage mode "integer" (is.integer(my.ts was TRUE). Should storage.mode() of a ts be allowed to be integer, should ts() someplace say storage.mode(ts.out) <- "double", or maybe inside arima() storage.mode(x) <- "double"