parcorBMany {generalCorr} | R Documentation |
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)