Thank you for your suggestions. But in minimal example I provided I didn't import any package (incl. zoo). However the behavior is different. Something in R has changed between 2.14.0 and 3.0.2.
Best wishes, Andrey Paramonov 2013/10/25 Martyn Plummer <plumm...@iarc.fr> > This has nothing to do with changes in base R. It is due to changes in > the dependent packages. These changes mean that when you call lapply it > does not dispatch the right as.list method. > > The method you want (as.list.ts) is provided by the zoo package. It > splits a multivariate time series into a list of univariate time series > in the way you are expecting. Your package mar1s used to depend on zoo > indirectly through the fda package. But now fda does not depend on zoo, > it only suggests it. So now, when you load your package, zoo is not on > the search path and you get the default as.list method, which produces > the bad results. > > The solution is to add "Imports: zoo" to your DESCRIPTION file and > "import(zoo)" to your NAMESPACE file. > > Martyn > > > On Wed, 2013-10-23 at 22:56 +0400, ÐндÑей ÐаÑамонов wrote: > > 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. > Let > > f be a simple (trivial) model of seasonal component: > > > > f <- function(x) > > return(ts(rep(0, length(x)), start = 0, frequency = frequency(x))) > > > > In previous versions of R, I used the following compact and efficient > > expression to calculate seasonal component: > > > > y <- do.call(cbind, lapply(x, f)) > > > > It worked equally good for univariate and multivariate time series: > > > > > R.Version()$version.string > > [1] "R version 2.14.0 (2011-10-31)" > > > t <- ts(1:10, start = 100, frequency = 10) > > > > > > x <- t > > > y <- do.call(cbind, lapply(x, f)) > > > y > > Time Series: > > Start = c(0, 1) > > End = c(0, 10) > > Frequency = 10 > > [1] 0 0 0 0 0 0 0 0 0 0 > > > > > > x <- cbind(t, t) > > > y <- do.call(cbind, lapply(x, f)) > > > y > > Time Series: > > Start = c(0, 1) > > End = c(0, 10) > > Frequency = 10 > > t t > > 0.0 0 0 > > 0.1 0 0 > > 0.2 0 0 > > 0.3 0 0 > > 0.4 0 0 > > 0.5 0 0 > > 0.6 0 0 > > 0.7 0 0 > > 0.8 0 0 > > 0.9 0 0 > > > > But in version 3, I get some frustrating results: > > > > > R.Version()$version.string > > [1] "R version 3.0.2 (2013-09-25)" > > > t <- ts(1:10, start = 100, frequency = 10) > > > > > > x <- t > > > y <- do.call(cbind, lapply(x, f)) > > > y > > Time Series: > > Start = 0 > > End = 0 > > Frequency = 1 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > > > > > x <- cbind(t, t) > > > y <- do.call(cbind, lapply(x, f)) > > > y > > Time Series: > > Start = 0 > > End = 0 > > Frequency = 1 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > structure(0, .Tsp = c(0, 0, 1), class = "ts") > > 0 0 > > > > I didn't watch R development for quite some time now. Could anyone please > > help me to construct similar expression to what I have used in R 2, for > > multivariate case (or better, for both univariate and multivariate > cases)? > > > > Best wishes, > > Andrey Paramonov > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-devel@r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-devel > > [[alternative HTML version deleted]]
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