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A typo in previous post: for latter, read former. Apologies for the confusion. ********************************************************************** This is a commercial communication from Commerzbank AG.\ \ T...{{dropped}}

I have uploaded to CRAN a new package named 'its' (Irregular Time-Series). It implements irregular time-series as an S4 class, extending the matrix class, and records the time-stamp of each row in the matrix using POSIX. Print...

I have uploaded to CRAN a new package named 'its' (Irregular Time-Series). It implements irregular time-series as an S4 class, extending the matrix class, and records the time-stamp of each row in the matrix using POSIX. Print...

I have a class defined as an extension to matrix: fooCLASS <- setClass("foo",representation("matrix")) and define a method for a generic function (no problem): setMethod("diff",signature(x="foo"),diff.foo <- function(x){x}) but I get a different...

I maintain the Irregular Time Series (its) package on CRAN. I am currently testing a release. Under 1.8.1 I am able to define a S4 generic 'start' as follows: if(!isGeneric("start")) {setGeneric("start", useAsDefault=start)} Under 1...

You might wish to have a look at the 'its' package for irregular time-series on CRAN. If your prices are in an its called price, then the following will get you on your way. Since it is not efficient...

You could take a look at the irregular time-series (its) package on CRAN. Your series is certainly irregular, and possibly a little er... sparse. Anyway, the following might get you started: require(its) mydates <- c("28.8.1962","27...

I want to do a linear regression where the coefficients obey two linear constraints, and also are all non-negative. What is the best way to do this? Computational speed is a consideration as I must do it many times...

I'm not sure what a calendar time series is, but it may be helpful to consider it as an irregular time series, depending on what analysis or display you are wanting to do. As you say, there are packages...

I make quite a lot of use of irregular time-series, and had already spent a bit of time writing an 'its' class when the 'irts' class was released via the package 'tseries'. I have experimented with the 'irts' class...

Although this thread might be considered closed by some, I'd like to make a late contribution, since I contributed the extractIts() function in the 'its' package. See ?itsSubset, which gives details of: extractIts(x,weekday=FALSE,find=c("all...

Assignment to a list in a function does not behave as I would expect. The following function illustrates the point #---------------------------------------- temp _ function() { nf _ 2 ns _ 5 z _ vector("list",ns) x _ matrix(NA,nrow=ns,ncol=nf) for(j in...

You might wish to have a look at the package 'its' for handling irregular time-series. If your data is in a .csv file, the following would enable you to handle the data in its irregular form. its.format("%m...

I have encountered an issue which I have been unable to resolve, involving an S3 generic (print) being declared S4 generic in a package, and the method being exported. This all works fine - the problem occurs when I try to...

I get different results from match.call(), according to whether a function is dispatched via S3 or S4. Specifically, when I use S4 dispatch in the following example, the match.call() result is of length 1 less than I expect...

Thanks for the tips so far. I have modified the example so that (a) I do not define an S4 method for print [I use summary instead, which gets similar treatment in mle as far as I can see] (b...

I was a lttle shocked to see your report of 3.2M hits for the correct speeling versus 1.8M for incorrect. This is a ratio of just 1.8! Google normally has much higher power than this, as the...

One thing you could do is to use the 'its' (irregular time-series) package on CRAN. e.g. using a trivial dataset require(its) its.format("%Y-%m-%d") #defines text format of dates in dimnames df <- data.frame(1...

A solution is at hand using the 'irts' (irreglar time-series) class from package tseries. If your raw data is in a csv file, you could proceed as follows: mydata <- read.csv(filename,header=TRUE) basedate <- as.POSIXct(strptime("2003...

Jan The function readcsvIts reads a csv file into a matrix. To convert the matrix into an 'its', apply the its function to the matrix. So for your example its.format("%m/%d/%Y") test? <- its(readcsvIts(filename)) is.its...