similar to: Fix for bug in arima function

I noticed that the 3.2.1 release cycle is about to start. Is there any chance that this fix will make it into the next version of R? This bug is fairly serious: getting the wrong variance estimate leads to the wrong log-likelihood and the wrong AIC, BIC etc, which can and does lead to suboptimal model selection. If it's not fixed, this issue will affect every student taking our time series

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

On 21 May 2015, at 10:35 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: >> >> I noticed that the 3.2.1 release cycle is about to start. Is there any >> chance that this fix will make it into the next version of R? >> >> This bug is fairly serious: getting the wrong variance estimate leads to >> the wrong log-likelihood and the wrong

The bug repository is like an elephant: It doesn't forget, but the gestation period is long. In the present case, it is clear that something is not right, but someone needs to have sufficient recall and insight to check that your proposed fix is not unfixing a deliberate change. We should get to it eventually. (For some value of "we" not including "me"...) -pd On 20 Apr

Hi, I have been using this website ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm ) to help me to fit ARIMA models to my data. At the moment I have two possible methods to use. Method 1 If I use arima(ts.data, order=c(1,2,0), xreg=1:length(ts.data)) then the wrong value for the intercept/mean is given (checked on SPSS and Minitab) and

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))

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

>>>>> Patrick Perry <pperry at stern.nyu.edu> >>>>> on Wed, 27 May 2015 23:19:09 -0400 writes: {@PP, you forgot this part:} >>>>> peter dalgaard <pdalgd at gmail.com> >>>>> on Thu, 21 May 2015 14:36:03 +0200 writes: >> I suspect that what we really need is >> >> fitI <- lm(x ~

>>>>> 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 the 3.2.1 release cycle is about to start. Is there any >>> chance that this fix will make it into

I have a package on CRAN now (corpus-0.3.1) that is currently failing tests on Linux, but passing on all other architectures: https://cran.r-project.org/web/checks/check_results_corpus.html I believe that the issue arrises from a namespace class between "xrealloc", which my package provides for internal use, but which R also seems to provide (possibly as part of TRE in

A recent change to r-devel causes an R CMD check warning when a C file includes a "#pragma GCC diagnostic ignored" pragma: https://github.com/wch/r-source/commit/b76c8fd355a0f5b23d42aaf44a879cac0fc31fa4 . This causes the CRAN checks for the "corpus" package to emit a warning: https://www.r-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-clang/corpus-00check.html .

I don't think arima works exactly the way one would expect when there is differencing. What I think should happen is that by default the mean of the differenced series is estimated and if include.mean=F, then it is not. This is not what happens. Instead when there is differencing the include.mean argument is ignored. Now I guess, someone could argue that the mean of the original series

On 11 May 2017 at 12:16, Patrick Perry wrote: | I've done a bit more investigation into this issue. Here is my current | understanding of the situation: | | 1. I have a package on CRAN (corpus-0.3.1) that passes tests on all | platforms except for Linux. | 2. My package defines a C function, "xrealloc", for internal use. | 3. The libreadline library that R links to defines a

Full_Name: Allan McRae Version: 1.6.0 OS: Win 2000 P Submission from: (NULL) (129.215.190.229) When using predict.Arima in library ts(), it appears differencing is only accounted for in the first step of prediction and so any trend is not apparent in the predictions. The example shows the difference between the predictions of an arima(1,1,1) model and the backtransformed predictions of an

Hello, I'm fitting a series of ARIMA models to a data set to compare fits. After taking the logs of the data and then differencing them to induce stationarity, I execute arima( y, order=c( p, 0, q ), seasonal=list( order=c( P, 0, Q ), period=7 ) ) for various values of p, q, P and Q. For one set of these values, I get Error in optim(init[mask], armafn, method = "BFGS", hessian

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

Is it possible to fit a lagged regression, "y[t]=b0+b1*x[t-1]+e", using the function "lag"? If so, how? If not, of what use is the function "lag"? I get the same answer from y~x as y~lag(x), whether using lm or arima. I found it using y~c(NA, x[-length(x)])). Consider the following: > set.seed(1) > x <- rep(c(rep(0, 4), 9), len=9) > y <-

Dear R users, I have an unbalanced panel with an average of I=100 individuals and a total of T=1370 time intervals, i.e. T>>I. So far, I have been using the plm package. I wish to estimate a FE model like: res<-plm(x~c+v, data=pdata_frame, effect="twoways", model="within", na.action=na.omit) ?where c varies over i and t, and v represents an exogenous impact on x

I've posted this before but have not been able to locate what I'm doing wrong. I cannot determine how the forecast is made using the estimated coefficients from a simple AR(2) model when there is an exogenous variable. Does anyone know what the problem is? The help file for arima doesn't show the model with any exogenous variables. I haven't been able to locate any documents

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)