| parcor_ridg {generalCorr} | R Documentation |
Compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*. (deprecated)
Description
This function calls parcor_ijkOLD function which
uses a generalized correlation matrix R* as input to compute
generalized partial correlations between X_i and X_j
where j can be any one of the remaining
variables. Computation removes the effect of all other variables in the matrix.
It further adjusts the resulting partial correlation coefficients to be in the
appropriate [-1,1] range by using an additive constant in the fashion
of ridge regression.
Usage
parcor_ridg(gmc0, dig = 4, idep = 1, verbo = FALSE, incr = 3)
Arguments
gmc0 |
This must be a p by p matrix R* of generalized correlation coefficients. |
dig |
The number of digits for reporting (=4, default) |
idep |
The column number of the first variable (=1, default) |
verbo |
Make this TRUE for detailed printing of computational steps |
incr |
incremental constant for iteratively adjusting ‘ridgek’
where ridgek is the constant times the identity matrix used to
make sure that the gmc0 matrix is positive definite. If not iteratively
increas the |
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 r*(i,j | k).
The 4-th column has r*(j,i | k) (denoted partji), and the 5-th column has rijMrji,
that is the difference in absolute values (abs(partij) - abs(partji)).
Note
The ridgek constant created by the function during the first round
may not be large enough to make sure that
that other pairs of r*(i,j | k) are within the [-1,1] interval. The user may have to choose
a suitably larger input incr to get all relevant partial
correlation coefficients in the correct [-1,1] interval.
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. "A Survey of Ridge Regression and Related Techniques for Improvements over Ordinary Least Squares," Review of Economics and Statistics, Vol. 60, February 1978, pp. 121-131.
See Also
See Also parcor_ijkOLD.
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)
g1=gmcmtx0(mtx)
parcor_ijkOLD(g1,1,2) # ouji> ouij implies i=x is the cause of j=y
parcor_ridg(g1,idep=1)
## Not run:
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')
gm1=gmcmtx0(x)
parcor_ridg(gm1, idep=1)
## End(Not run)