similar to: kernapply.ts

Hi, There is a small bug in the kernel() function. Everything is fine when we use the format: kernel("name",m,r) but if we want the first argument to be a vector, which is useful is we are interested in using a method not implemented in kernel(), the default value of m is wrong. For example, if we do: s <- rep(1/11,6) k <- kernel(s) we get the error message Error in

This seems like a FAQ, but I can't figure it out. I have a mv ts object: R > tsp(pg) [1] 1982 2003 1 R > dim(pg) [1] 22 12 and a univariate ts: R > tsp(rw) [1] 1690 1996 1 Yet, when I try to intersect them: R > tsp(ts.intersect(rw, pg)) [1] 1982 2176 1 the process goes awry. How to I get rw and pg to be one ts that runs from 1982 to 1996 and has 13 univariate time

I'm trying to convert a column of a table into a ts object. The data is monthly, so I want the ts frequency to be 12. I did this ... > filings.ts = as.ts(Filings.100K, frequency=12) > filings.ts Time Series: Start = 1 End = 311 Frequency = 1 [1] 246.9336 305.6789 ... ... > tsp(filings.ts) [1] 1 311 1 > tsp(filings.ts) <- c(1,311,12) Error in attr(x, "tsp")

I'm probably apporaching this all wrong to start but.... Suppose I have a monthly time series and I want to compute the mean of months 6,7, and 8. I want to plot the original time series and the seasonal time series, one above the other. When I do that as below the time series don't line up for reasons that are obvious. How can I change the base of the seasonal time series so I can make

Hi all, I found a discrepancy between the sum() in R and either a sum done in C or Fortran for vector of just 5 elements. The difference is very small, but this is a very small part of a much larger numerical problem in which first and second derivatives are computed numerically. This is part of a numerical method course I am teaching in which I want to compare speeds of R versus Fortran (We

Hi all, I am looking for a function similar to mclapply() that would work with GPU cores. I have looked at all possible packages related to GPU computing but they are mainly providing functionality for big dataset or big matrices. I use mainly mclapply to speed up simulations by running several simulations in parallel, which works nicely. Is it possible to do the same with a multicore GPU? I

Hi all. Currently, if you try: > lag.plot(1:10) you get superposed labels '1' and '10'. Things go worse in more extreme cases: x <- ts(1:10) x1 <- lag(x, 4) plot(x1, x) This is due to a mistake in plot.ts. My suggestion is the following really minimal patch to plot.ts: @@ -530,7 +530,7 @@ plot.ts <- text(xy, labels =

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so

Thanks for the very detailed explanation. I did not create the series using structure(), that was the result of dump() on an intermediate object created within tsdisagg::ta(), which is where I found the error in the first place. ta() indeed manipulates .Tsp directly, rather than using ts. I guess this is a bug in tsdisagg then. Thanks! -- Andrea Altomani On Sat, Sep 2, 2017 at 12:31 AM

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.

Hi, I am far from experienced in both R and time series hence the question. The code for spec.pgram() seems to involve a circularity of the kernel (see below) yielding new power estimates to all frequencies computed by FFT. " if (!is.null(kernel)) { for (i in 1:ncol(x)) for (j in 1:ncol(x)) pgram[, i, j] <- kernapply(pgram[, i, j], kernel, circular = TRUE)

Hi all- I am trying to use the time function for ts class objects and do not understand the return value. I want to use it to set up a time trend in arima fits. It does not seem to return a correct linear sequence that matches the underlying time series. I am running: R version 2.8.1 (2008-12-22). For example: R> ## create a time series R> x <- rnorm(24) R> (xts <-

On Fri, 1 Sep 2017, Andrea Altomani wrote: > I should have formulated my question in a more specific way. > > 1. I suspect this is a floating point precision issue. I am not very > knowledgeable about R internals, can someone else confirm it? Yes. If you represent a series with increment 1/12 it depends on how you do it. As a simple example consider the following two descriptions

I have a time series x, and two other series obtained from it: x <- structure(2017, .Tsp = c(2017.41666666667, 2017.41666666667, 12), class = "ts") y <- floor(x) z <- x-y I would expect the three series to have exactly the same index. However I get the following > time(x)-time(y) Jun 2017 0 as expected, but > time(x)-time(z) integer(0) Warning message: In

{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.

Marcus Eger <marcus.eger@physik.uni-marburg.de> writes: > > time(sqrt(arrts)) > Time Series: > Start = c(1, 1) > End = c(5, 1) > Frequency = 1 > [1] 1 2 3 4 5 Looks like a bug... > ---------------------------------------------------------------------------- > 2. (At least) boolean indexing with matrices does not seem to work > properly: >

Hello, I am wondering how I should set up the tsp attribute (available through attr(x, "tsp")) of a dataset x? Let's assume that x has 100 points, and I want to set the frequency to 4. I tried: > attr(x,"tsp")<-c(1,100,4) Error in attr(x, "tsp") <- c(1, 100, 4) : invalid time series parameters specified Is there any other way to set the frequency of

Greetings, I am having trouble getting the function reformulate_ATSP_as_TSP to work for me. I have provided a simple example of some of the code I've been using. In particular, I'm not sure why I'm getting the error "Error in dimnames(tsp) <- list(lab, lab) : length of 'dimnames' [1] not equal to array extent" since I created the object ATSP with a valid

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 }

I should have formulated my question in a more specific way. 1. I suspect this is a floating point precision issue. I am not very knowledgeable about R internals, can someone else confirm it? 2. Should this be considered a bug or not, because it is "just a precision issue"? Should I report it? 3. How can it happen? From a quick review of ts.R, it looks like the values of the time