R/circular.R

In gpiras/sphet: Estimation of Spatial Autoregressive Models with and without Heteroskedastic Innovations

#' Generate circular weigthing matrices
#' @aliases circular
#' @title Generate circular weigthing matrices
#' @description The function generates k-ahead and k-behind weighting matrices as in Kelejian and Prucha (1999)
#'
#' @param nrow Number of rows
#' @param ncol Number of columns
#' @param ab Ahead - behind
#'
#' @return An object of class \code{nb}
#'
#' @examples ab.3 <- circular(10, 10, 3)
#' ab.5 <-  circular(15, 15, 5)
#' @author Gianfranco Piras \email{gpiras@mac.com}
#' @keywords spatial
#' @export




circular <- function(nrow, ncol, ab){
    nrow <- as.integer(nrow)
    if (nrow < 1) 
        stop("nrow nonpositive")
    ncol <- as.integer(ncol)
    if (ncol < 1) 
        stop("nrow nonpositive")
 ab <- as.integer(ab)
	if(ab< 1) 
		stop("ab nonpositive")


N <- nrow*ncol	
vec <- seq(1, N)
fp <- vec[(length(vec) - ab + 1):length(vec)]
lp <- vec[1:ab]
vec <- c(fp, vec, lp)

res<-vector("list",length = N)
rownames <- character(N)

for(i in (ab+1): (N+ab)){
	
	res[[i-ab]] <- sort(vec[(i-ab):(i+ab)][-(ab+1)])
	rownames[i-ab] <- paste(vi2mrc(i-ab, nrow, ncol), collapse = ":")	
		}


class(res)<-"nb"
class(res) <- "nb"
attr(res, "call") <- match.call()
attr(res, "region.id") <- rownames
attr(res, "cell") <- TRUE
res <- sym.attr.nb(res)
res
	
	}