[R] Reading R code help--Beginner

[Steve Lianoglou]

Hi,

On Wed, May 12, 2010 at 12:11 PM, Makada Henry <mhenry_888 at 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]]
>
> ______________________________________________
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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.
>



-- 
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