Hi,
On Wed, May 12, 2010 at 12:11 PM, Makada Henry <mhenry_...@msn.com> wrote:
>
> 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:
I'm going to cut it off here and simply ask "what part don't you get"?
Although the formatting is screwy, it just look like a lot of book
keeping type of code to me ...
-steve
>
>
> GVAR <- function (data, tw = NULL, p, q = p, r = NULL, weight, case,
> exo.var = FALSE,
> d = NULL, endo = NULL, ord = NULL, we = NULL,
> method = "max.eigen")
>
> # data ... timeseries data as list (each entry is a matrix of a
> subsystem of variables,
> # if exo.var=TRUE the last entry are exogeneous variables)
> # tw ... time window, vector of start and end point
> # p ... scalar/vector of endogenous lags, (N+1)x1
> # q ... scalar/vector of weakly exogeneous lags, (N+1)x1
> # r ... vector of cointegrating relations
> # weight ... weight matrix of dimension (N+1)x(N+1)
> # case ... scalar/vector of cases ("I" to "V"), (N+1)x1
> # endo ... list of endogenous variables used
> # ord ... list showing the same variables for weakly exogeneous
> analysis
> # we ... list with numbers of weakly exogeneous variables included in
> each VECM,
> # corresponds to numbers in ord
> # exo.var ... if TRUE strictly exogeneous variables are included in the
> model
> # d ... list showing which strictly exogeneous variables enter the
> subsystem equations
> # lex ... scalar/vector of lags of exogenous variables
> # method ... select cointegrating rank by max. eigenvalue ("max.eigen")
> or trace statistic ("trace")
>
>
> # ----- Set subsystems -----
>
>
> cmodel <- list()
>
> N <-
> length(data)-1 # number of
> subsystems i=0,1,...,N
> dims <- vector()
> for (i in 1:(N+1))
>
> {
> if (!is.null(dim(data[[i]])))
> {
>
> dims[i] <- dim(data[[i]])[1]
> } else {
> dims[i]
> <- length(data[[i]])
> }
> }
> max.dim <- max(dims)
>
>
> tsi <- tsp(data[[((1:length(dims))[dims==max(dims)])[1]]])
>
> if (is.null(tw))
> {
> start.ts <- tsi[1]
> end.ts
> <- tsi[2]
> } else {
> start.ts <- tw[1]
> end.ts
> <- tw[2]
> }
> freq <- tsi[3]
> dt <- 1/freq
>
> n.exo <- 0
> ex <- 0
> n.ex <- rep(0,N+1)
>
>
>
>
>
>
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
