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#' Vector of generalized partial correlation coefficients (GPCC),
#' always leaving out control variables, if any.
#'
#' This function calls \code{parcor_ijk} function which
#' uses original data to compute
#' generalized partial correlations between \eqn{X_i}, the dependent variable,
#' and \eqn{X_j} which is the current regressor of interest. Note that
#' j can be any one of the remaining
#' variables in the input matrix \code{mtx}. Partial correlations remove the effect of
#' variables \eqn{X_k} other than \eqn{X_i} and \eqn{X_j}.
#' Calculation merges control variable(s) (if any) into \eqn{X_k}.
#' Let the remainder effect
#' from kernel regressions of \eqn{X_i} on \eqn{X_k} equal the
#' residuals u*(i,k). Analogously define u*(j,k). (asterisk for kernel regressions)
#' Now partial correlation is generalized correlation
#' between u*(i,k) and u*(j,k).
#' Calculation merges control variable(s) (if any) into \eqn{X_k}.
#'
#'
#' @param mtx {Input data matrix with p (> or = 3) columns}
#' @param ctrl {Input vector or matrix of data for control variable(s),
#' default is ctrl=0 when control variables are absent}
#' @param verbo Make this TRUE for detailed printing of computational steps
#' @param idep The column number of the dependent variable (=1, default)
#' @return A p by 1 `out' vector containing partials r*(i,j | k).
#'
#' @note Generalized Partial Correlation Coefficients (GPCC) allow comparison of
#' the relative contribution of each \eqn{X_j} to the explanation of \eqn{X_i},
#' because GPCC are scale-free pure numbers
#'
#' @note We want to get all partial
#' correlation coefficient pairs removing other column effects. Vinod (2018)
#' shows why one needs more than one criterion to decide the causal paths or exogeneity.
#' @author Prof. H. D. Vinod, Economics Dept., Fordham University, NY.
#' @seealso See Also \code{\link{parcor_ijk}}.
#' @seealso See Also a hybrid version \code{\link{parcorVecH}}.
#' @concept partial correlations
#' @references Vinod, H. D. 'Generalized Correlations and Instantaneous
#' Causality for Data Pairs Benchmark,' (March 8, 2015)
#' \url{https://www.ssrn.com/abstract=2574891}
#'
#' @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.
#'
#' @references Vinod, H. D. 'New Exogeneity Tests and Causal Paths,'
#' (June 30, 2018). Available at SSRN:
#' \url{https://www.ssrn.com/abstract=3206096}
#'
#' @references Vinod, H. D. (2021) 'Generalized, Partial and Canonical Correlation
#' Coefficients' Computational Economics, 59(1), 1--28.
#'
#' @examples
#' set.seed(234)
#' z=runif(10,2,11)# z is independently created
#' x=sample(1:10)+z/10 #x is partly indep and partly affected by z
#' y=1+2*x+3*z+rnorm(10)# y depends on x and z not vice versa
#' mtx=cbind(x,y,z)
#' parcorVec(mtx)
#'
#'
#' \dontrun{
#' set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
#' colnames(x)=c('V1', 'v2', 'V3')#some names needed
#' parcorVec(x)
#' }
#'
#' @export
parcorVec=
function (mtx, ctrl = 0, verbo = FALSE, idep=1)
{
n = NROW(mtx)
p = NCOL(mtx)
if (p < 3)
stop("stop: input matrix to parcorVec must have 3 or more columns")
nam = colnames(mtx)
if (length(nam) == 0)
nam = paste("V", 1:p, sep = "")
if (verbo)
print(c("gen.zed partial Corr coef GPCC vec", nam[idep], "and one of the others"))
if (verbo)
print(c("Removes effect of control variables in ctrl (if any)"))
out = matrix(1, nrow = 1, ncol = (p-1))#single row output one less column
namj=rep(NA,p-1)
j.other = setdiff(1:p, idep)
namj=nam[j.other]
p.other = length(j.other)
if (verbo)
print(c("j.other,p.other",j.other,p.other))
xi=mtx[,idep]
for (j in 1:p.other) {
myj=j.other[j]
xj = mtx[,myj]
xk = mtx[, c(-idep, -myj)]
if (length(ctrl) > 1) {
p1 = parcor_ijk(xi = xi, xj = xj, xk = cbind(xk, ctrl))
}
if (length(ctrl) == 1) {
p1 = parcor_ijk(xi = xi, xj = xj, xk = xk)
}
out[1, j] = p1$ouij
}
colnames(out) = namj
return(out)
}
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