R/gmcxy_np.R

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

#' Function to compute generalized correlation coefficients r*(x|y) and 
#' r*(y|x) from two vectors (not matrices)
#'
#' This function uses the `np' package and assumes that there are no missing data.
#'
#'
#' @param x vector of x data
#' @param y vector of y data
#' @return
#' \item{corxy}{r*(x|y)  from regressing x on y, where y is the kernel cause.}
#' \item{coryx}{r*(y|x) from regressing y on x, where x is the cause.}
#' @note This is provided if the user want to avoid calling \code{kern}.
#' @author Prof. H. D. Vinod, Economics Dept., Fordham University, NY
#' @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}
#'
#' @references Vinod, H. D. 'Matrix Algebra Topics in Statistics and Economics
#' Using R,' Chapter 4 in 'Handbook of Statistics: Computational Statistics
#' with R,' Vol.32, co-editors: M. B. Rao and C.R. Rao. New York:
#' North Holland, Elsevier Science Publishers, 2014, pp. 143-176.
#' @concept  kernel regression
#' @concept R* asymmetric correlations
#' @examples
#' \dontrun{
#' set.seed(34);x=sample(1:10);y=sample(2:11)
#' gmcxy_np(x,y)}
#'
#' @export

gmcxy_np <- function(x, y) {
    # np means we call the np library functions
    bw = npregbw(formula = x ~ y, tol = 0.1, ftol = 0.1)
    model = npreg(bws = bw, gradients = FALSE, residuals = TRUE)
    corxy = model$R2
    bw2 = npregbw(formula = y ~ x, tol = 0.1, ftol = 0.1)
    model2 = npreg(bws = bw2, gradients = FALSE, residuals = TRUE)
    coryx = model2$R2
    list(corxy = corxy, coryx = coryx)
}