parcorMtx {generalCorr} | R Documentation |
Matrix of generalized partial correlation coefficients, always leaving out control variables, if any.
Description
This function calls parcor_ijk
function which
uses original data to compute
generalized partial correlations between and
where j can be any one of the remaining
variables in the input matrix
mtx
. Partial correlations remove the effect of
variables other than
and
. 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
parcorMtx(mtx, ctrl = 0, dig = 4, verbo = FALSE)
Arguments
mtx |
Input data matrix with p columns. p is 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) |
verbo |
Make this TRUE for detailed printing of computational steps |
Value
A p by p ‘out’ matrix containing partials r*(i,j | k). and r*(j,i | k).
Note
We want to get all partial correlation coefficient pairs removing other column effects. Vinod (2018) shows why one needs more than one criterion to decide the causal paths or exogeneity.
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. '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.
Vinod, H. D. 'New Exogeneity Tests and Causal Paths,' (June 30, 2018). Available at SSRN: https://www.ssrn.com/abstract=3206096
See Also
See Also parcor_ijk
.
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)
parcorMtx(mtx)
## Not run:
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')
parcorMtx(x)
## End(Not run)