| parcorBijk {generalCorr} | R Documentation |
Block version of generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.
Description
This function uses data on two column vectors, xi, xj and a third xk which can be a vector or a matrix, usually of the remaining variables in the model, including control variables, if any. It first removes missing data from all input variables. Then, it computes residuals of kernel regression (xi on xk) and (xj on xk). This is a block version of parcor_ijk.
Usage
parcorBijk(xi, xj, xk, blksiz = 10)
Arguments
xi |
Input vector of data for variable xi |
xj |
Input vector of data for variable xj |
xk |
Input data for variables in xk, usually control variables |
blksiz |
block size, default=10, if chosen blksiz >n, where n=rows in matrix then blksiz=n. That is, no blocking is done |
Value
ouij |
Generalized partial correlation Xi with Xj (=cause) after removing xk |
ouji |
Generalized partial correlation Xj with Xi (=cause) after removing xk |
allowing for control variables.
Note
This function calls kern,
Author(s)
Prof. H. D. Vinod, Economics Dept., Fordham University, NY.
See Also
See parcor_ijk.
Examples
## Not run:
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
options(np.messages=FALSE)
parcorBijk(x[,1], x[,2], x[,3], blksi=10)
## End(Not run)#'