gmcmtxZ {generalCorr} | R Documentation |
compute the matrix R* of generalized correlation coefficients.
Description
This function checks for missing data separately for each pair using
kern
function to kernel regress x on y, and conversely y on x. It
needs the library ‘np’ which reports R-squares of each regression. This function
reports their square roots with the sign of the Pearson correlation coefficients.
Its appeal is that it is asymmetric yielding causal direction information.
It avoids the assumption of linearity implicit in the usual correlation
coefficients.
Usage
gmcmtxZ(mym, nam = colnames(mym))
Arguments
mym |
A matrix of data on variables in columns |
nam |
Column names of the variables in the data matrix |
Value
A non-symmetric R* matrix of generalized correlation coefficients
Note
This allows the user to change gmcmtx0
and further experiment with my code.
Author(s)
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
Examples
## Not run:
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')
gmcmtxZ(x)
## End(Not run)