similar to: Method dispatch fails for autoloaded methods (PR#642)

This message is in MIME format --_=XFMail.1.1.p0.Linux:970825095458:252=_ Content-Type: text/plain; charset=us-ascii Here is a patch which fixes some problems with time series functions. Some examples of what goes wrong... > x <- ts(rnorm(100),start=1,deltat=2) > start(x) Error in ts[1] : object is not subsettable > end(x) Error in ts[2] : object is not subsettable > y <-

This message is in MIME format --_=XFMail.1.3.p0.Linux:990702182137:16900=_ Content-Type: text/plain; charset=us-ascii There was some discussion a while back on R-devel between Ross Ihaka, Paul Gilbert and myself about row subsetting in time series. I think the consensus was that "[.ts" should not try to coerce its result back to a time series object (which is underlying the problem

{all recent versions of R} Problem / design bug : diff.default() doesn't deal with "ts" objects properly, diff.ts() does, but that is only available from package "ts" where as the constructor function ts() is in base. It's not sufficient to just move diff.ts() to package base, since it relies itself on lag & lag.default which are also only in package ts.

I'm playing with predict.Arima in the 3/19/02 development snapshot of R-devel. The following produces an error message because x is not of class "ts": R> x <- rnorm(20) R> obj <- arima(x, c(2,0,0)) R> predict(obj) Error in round(x, digits) : Non-numeric argument to mathematical function Granted the documentation for arima says x should be a time-series, but

Hello,guys: Recently, I am working on a seasonal ARIMA model. And I met some problem in the forecasting. Now I just want to know that How does R perform the predict procedure(the predict formula, the initial setting of errors,etc.)? I run the following commands and get the original code of the "predict" command, but I can't read it. Can anybody explain it to me? Thanks! saji from

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

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'm having trouble with column-subsetting a multivariate time series. The underlying problem is with start(). R> x <- ts(matrix(rnorm(100), ncol=2)) R> y <- aggregate(x, ndeltat=2) R> y[,1] Error in ts(y, start = start(x), freq = frequency(x)) : invalid start R> start(y) [1] 2 1 R> frequency(y) [1] 0.5 The point where the error occurs in ts() is:

Hi, I have a matrix and time series "xmat". I have no problem executing any matrix functions except t(xmat) which gives the following error message. My question if "xmat" is a matrix why I can not execute this matrix function?? converting the time series and matrix into a data frame solves the problem >dim(xmat) [1] 98 7 > t(xmat) Error in "tsp<-"(*tmp*,

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,

>>>>> On Fri, 02 Jul 1999, Adrian Trapletti <Adrian.Trapletti@wu-wien.ac.at> said: Adrian> There seems to be a problem with plot.ts (R Version 0.64.2) > x<-cbind(1:10,2:11) > x<-as.ts(x) > plot(x) Adrian> Error: subscript (20) out of bounds, should be at most 10 This is definitely a bug --> CC: R-bugs ALL NOTE : This is *not* new

Hello! Recently I got report that my package mar1s doesn't pass checks any more on R 3.0.2. I started to investigate and found the following difference in multivariate time series handling in R 3.0.2 compared to R 2 (I've checked on 2.14.0). Suppose I wish to calculate seasonal component for time series. In case of multivariate time series, I wish to process each column independently.

This one showed up while looking at one of Ripley's other reports: > data(freeny) > model.frame(y~1,data=freeny,subset=1:10) y 1962.25 8.79236 1962.5 8.79137 1962.75 8.81486 1963 8.81301 1963.25 8.90751 1963.5 8.93673 1963.75 8.96161 1964 8.96044 1964.25 9.00868 1964.5 9.03049 > model.frame(y~1,data=freeny,subset=1:10)$y Warning: Replacement length not a

If I create a bivariate ts series with 3 time points and run na.omit on it expecting it to reduce it to a one point ts series instead I get the following error messasge: R> hh <- ts(matrix(c(NA,1:4,NA),3), start = c(1951,8), freq=12) R> hh Series 1 Series 2 Aug 1951 NA 3 Sep 1951 1 4 Oct 1951 2 NA R> na.omit(hh) Error in

Hi, I am brand new to R and not familiar with the language, though I have been reading the manuals and making some slow going progress. I am working with some source code from a Global Vector Auto -Regressive program written by Ranier Puhr from the R-forge group. I need help interpreting the processes of the following code. I am going to post in parts since it's pretty long: GVAR

aggregate.ts does not behave in the same way as the equivalent method aggregate.rts in S-PLUS. In particular it - changes the start of the time series - tends to have a length which is 1 shorter For example: R> x <- ts(1:10) R> aggregate(x, nfreq=0.5, FUN=min) Time Series: Start = 2 End = 8 Frequency = 0.5 [1] 2 4 6 8 S> x <- rts(1:10) S> aggregate(x, nf=0.5, fun = min) [1]

I have a suggestion for kernapply for ts objects. When we choose the option circular=F, the returned series don't have the correct dates. The removed dates are all at the beginning instead of half at the beginning and half at the end. It is particularly useful when we need to smooth the series (or remove a trend using a filter) before estimating a model (like in macroeconomics) or simply

Dear all, I'm indirectly faced with the fact that setting the 'tsp' attribute of an object modifies its class definition: > class( structure(1:2, tsp=c(1,2,1), class=c("myts","ts")) ) [1] "ts" "myts" In general, this is of really little (ok, I admit: totally no) interest for me because 'myts' class is added just after assigning the

Dear all, Before I file this as a bug, I wanted to check if I didn't miss something. The help page of lag() says that the function returns a time series object. It actually does return something that looks like a ts object (the attribute tsp is set). But when using a vector, the class "ts" is not added to the result: > avec <- 1:10 > lag(avec) [1] 1 2 3 4 5 6 7 8

If value is NULL I think tsp assignment should not return a class ts object. Below is a fixed version. Paul Gilbert ____ "tsp<-" <-function(x, value) {if (is.null(value)) {attr(x, "tsp") <- value if(inherits(x,"ts")) class(x) <- NULL return(x) } attr(x, "tsp") <- value class(x) <- "ts" x }