gmcmtxZ function

compute the matrix R* of generalized correlation coefficients.

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.

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

Returns

A non-symmetric R* matrix of generalized correlation coefficients

Note

This allows the user to change gmcmtx0 and further experiment with my code.

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)

References

Vinod, H. D. `Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")

Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY