generalCorr (version 1.2.6)

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

Description

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.

Usage

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

Value

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.

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

Author

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

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.

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)
 
   
if (FALSE) {
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')
parcorBMany(x, idep=1)
}