R/absBstdres.R

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

#' Block version of abs-stdres Absolute values of residuals of kernel regressions 
#' of  standardized x on  standardized y, no control variables.
#'
#' 1) Standardize the data to force mean zero and variance unity, 2) kernel
#' regress x on y, with the option `residuals = TRUE' and finally 3) compute
#' the absolute values of residuals.
#'
#' The first argument is assumed to be the dependent variable.  If
#' \code{abs_stdres(x,y)} is used, you are regressing x on y (not the usual y
#' on x). The regressors can be a matrix with 2 or more columns. The missing values
#' are suitably ignored by the standardization.
#'
#' @param x {vector of data on the dependent variable}
#' @param y {data on the regressors which can be a matrix}
#' @param blksiz {block size, default=10, if chosen blksiz >n, where n=rows in matrix
#'      then blksiz=n. That is, no blocking is done}
#' 
#' @importFrom stats sd
#' @return Absolute values of kernel regression residuals are returned after
#' standardizing the data on both sides so that the magnitudes of residuals are
#' comparable between regression of x on y on the one hand and regression of y
#' on x on the other.
### @note %% ~~further notes~~
#' @author Prof. H. D. Vinod, Economics Dept., Fordham University, NY
### @seealso %% ~~objects to See Also as \code{\link{help}}, ~~~
#' @references Vinod, H. D. `Generalized Correlation and Kernel Causality with
#'  Applications in Development Economics' in Communications in
#'  Statistics -Simulation and Computation, 2015,
#'  \doi{10.1080/03610918.2015.1122048}
#' @concept  kernel regression residuals
#' @examples
#'
#' \dontrun{
#' set.seed(330)
#' x=sample(20:50)
#' y=sample(20:50)
#' abs_stdres(x,y)
#' }
#'
#' @export


absBstdres <- function(x, y, blksiz=10) {
    stdx = function(x) (x - mean(x, na.rm = TRUE))/sd(x, na.rm = TRUE)
    # allows y to be a matrix, but not x
    stx = (x - mean(x, na.rm = TRUE))/sd(x, na.rm = TRUE)
    p = NCOL(y)
    n = NROW(y)
    if (blksiz>n) blksiz=n
    ge=getSeq(n,blksiz=blksiz)
    ares=rep(NA,n) #absolute residuals vector
    LO=ge$sqLO
    UP=ge$sqUP
    k=length(LO)
    for (ik in 1:k){
        L1=LO[ik]  
        U1=UP[ik]
        stxx=stdx(x[L1:U1])
        if (p == 1) {
        yy=y[L1:U1]
        sty = (yy - mean(yy, na.rm = TRUE))/sd(yy, na.rm = TRUE)}
    if (p > 1) {
        yy=y[L1:U1,]
        sty = apply(yy, 2, stdx)}
    kk1 = kern(dep.y = stxx, reg.x = sty, residuals = TRUE)
    ares[L1:U1] = abs(kk1$resid)
    } # end ik loop
    return(ares)
}