





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:


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)




 		 	   		  


 		 	   		  
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