R/stdres.R

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

#' Residuals of kernel regressions of x on y when both x and
#' y are standardized.
#'
#' 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 residuals. The standardization yields comparable residuals.
#'
#' The first argument is assumed to be the dependent variable.  If
#' \code{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}
#' @importFrom stats sd
#' @return 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 
#' the flipped 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)
#' stdres(x,y)
#' }
#'
#' @export


stdres <- function(x, y) {
    stdx = function(x) (x - mean(x, na.rm = TRUE))/sd(x, na.rm = TRUE)
    # allows y to be a matrix
    stx = (x - mean(x, na.rm = TRUE))/sd(x, na.rm = TRUE)
    p = NCOL(y)
    if (p == 1) 
        sty = (y - mean(y, na.rm = TRUE))/sd(y, na.rm = TRUE)
    if (p > 1) 
        sty = apply(y, 2, stdx)
    kk1 = kern(dep.y = stx, reg.x = sty, residuals = TRUE)
    res = (kk1$resid)
    return(res)
}