parcorBMany function

Block version reports many generalized partial correlation coefficients allowing control variables.

This function calls a block version parcorBijk of the function which uses original data to compute generalized partial correlations between $X_{idep}$ and $X_j$

where j can be any one of the remaining variables in the input matrix mtx. Partial correlations remove the effect of variables $X_k$ other than $X_i$ and $X_j$. Calculation further allows for the presence of control variable(s) (if any) to remain always outside the input matrix and whose effect is also removed in computing partial correlations.

parcorBMany(mtx, ctrl = 0, dig = 4, idep = 1, blksiz = 10, verbo = FALSE)

Arguments

• mtx: Input data matrix with at least 3 columns.
• ctrl: Input vector or matrix of data for control variable(s), default is ctrl=0 when control variables are absent
• dig: The number of digits for reporting (=4, default)
• idep: The column number of the dependent variable (=1, default)
• blksiz: block size, default=10, if chosen blksiz >n, where n=rows in matrix then blksiz=n. That is, no blocking is done
• verbo: Make this TRUE for detailed printing of computational steps

Returns

A five column out' matrix containing partials. The first column has the name of the idep` variable. The second column has the name of the j variable, while the third column has partial correlation coefficients r*(i,j | k).The last column reports the absolute difference between two partial correlations.

Note

This function reports all partial correlation coefficients, while avoiding ridge type adjustment.

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)
parcorBMany(mtx, blksiz=10)
 
   
## Not run:

set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')
parcorBMany(x, idep=1)
## End(Not run)

References

Vinod, H. D. 'Generalized Correlations and Instantaneous Causality for Data Pairs Benchmark,' (March 8, 2015) https://www.ssrn.com/abstract=2574891

Vinod, H. D. (2021) 'Generalized, Partial and Canonical Correlation Coefficients' Computational Economics, 59(1), 1--28.

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.

See Also

See Also parcor_ijk, parcorMany.

Author(s)

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