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I have been trying to get a working version of ar, since I have used it in several calculations in the test suite for my time series library. The following limited version (order.max must be specified and other short comings) works more or less, but the results differ by more than I would expect from those given by Splus. I have tried several variations with no success. If anyone can see a reason

Dear all, I have two different data frames, that have two common variables: date and sample. Here is a small extract of both of them > head(traffic) datet sessiont samplet buddleiat 1 07-08-2012 1 1 1 2 07-08-2012 1 1 1 3 07-08-2012 1 1 1 4 07-08-2012 1 2 3 5 07-08-2012 1 2

I have just put up a new executable as a replacement for the one in rseptbeta.zip there have only been a few changes; mostly to the menu's. I am about to start on a major overhaul including getting survival to work and grabbing the 0.60 version once it's stable. Please let me know about other enhancements you want.... robert

I have just put up a new executable as a replacement for the one in rseptbeta.zip there have only been a few changes; mostly to the menu's. I am about to start on a major overhaul including getting survival to work and grabbing the 0.60 version once it's stable. Please let me know about other enhancements you want.... robert

pacf in devel seems by default to return a different number of lags than 1.7.1 for $pacf. I don't see any mention of this in the NEWS file, or any change in the documentation, so I suspect it is and error, though it may be an undocumented improvement. (Newbie question: How is the simplest way to display a function like pacf.default that is not exported from a namespace?) Paul

In some tests I am getting a difference in the 7th or 8th significant digit depending on whether I set DUP=T or DUP=F in the .Fortran call below. While this is not a huge error it is bigger than I would expect and may be a symptom of another problem. Any comments or suggestions? (R1.2.2 on Solaris.) Paul Gilbert _______ genD.ARMA <- function(model, data, d=0.01, eps=1e-4, r=6, warn=F){ n

I am trying to understand the function acf stats:::acf shows me the function I am having trouble understanding the usage "$acf" in the following acf <- array(.C(R_acf, as.double(x), as.integer(sampleT), as.integer(nser), as.integer(lag.max), as.integer(type == "correlation"), acf = double((lag.max + 1L) * nser * nser), NAOK =

In R 0.60.1 when I source a fairly large file of code I am getting: Error in parse(file, n, text, prompt) : syntax error on line 2985 I don't see any error and it doesn't happen if I split the file in two pieces. increasing -v -n does not seem to help and does not change the location of the error. Does anyone have suggestions? Paul Gilbert

Hello! I have two questions. First of all, I have a problem dealing with acf (Autocovariance function) and need help. First I defined a time series, x, which is a vector created by x <- ts(rnorm(200)). So I plugged the series directly into the acf function, acf(x) and an error message popped up as: Error in .C("acf", as.double(x), as.integer(sampleT), as.integer(nser), :

Please pardon me if you see this message twice. The mail server has a bit problem. ***************************************************** Hello! I have two questions. First of all, I have a problem dealing with acf (Autocovariance function) and need help. First I defined a time series, x, which is a vector created by x <- ts(rnorm(200)). So I plugged the series directly into the acf

Hi I'm getting the following error that do not make sense to me, what am Idoing wrong ? > acf(Recsim[1,], lag.max=1) Error in acf(Recsim[1, ], lag.max = 1) : 'lag.max' must be at least 1 Regards EJ

I'm using R-2.9.1, so forgive me if this has already been resolved. One element of the object returned from the acf function is n.used, described in the man page as "The number of observations in the time series". However, I've noticed that this value is set to nrow(x) via the sampleT variable, i.e. the number of rows in the passed-in series. This forces an assumption that all

Macros to use VMI calls from assembly and C languages are introduced. The macros are quite complex, but the end result is rather impressive. The result is that when compiling a VMI kernel, the native code is emitted inline, with no function call overhead, and some wiggle room for register allocation. The hypervisor compatibility code is emitted out of line into a separate section, and patched

Macros to use VMI calls from assembly and C languages are introduced. The macros are quite complex, but the end result is rather impressive. The result is that when compiling a VMI kernel, the native code is emitted inline, with no function call overhead, and some wiggle room for register allocation. The hypervisor compatibility code is emitted out of line into a separate section, and patched