parcorBMany: Block version reports many generalized partial correlation...

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

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)

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

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.

Note

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

Author(s)

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)
 
   
## 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)

generalCorr documentation built on Oct. 10, 2023, 1:06 a.m.