allPairs: Report causal identification for all pairs of variables in a matrix (deprecated function). It is better to choose a target variable and pair it with all others, instead of considering all possible targets.

Description

This studies all possible (perhaps too many) causal directions in a matrix. It is deprecated because it uses older criterion 1 by caling abs_stdapd I recommend using causeSummary or its block version cuseSummBlk. This uses abs_stdres, comp_portfo2, etc. and returns a matrix with 7 columns having detailed output. Criterion 1 has been revised as described in Vinod (2019) and is known to work better.

Usage

allPairs(mtx, dig = 6, verbo = FALSE, typ = 1, rnam = FALSE)

Arguments

mtx

Input matrix with variable names

dig

Digits of accuracy in reporting (=6, default)

verbo

Logical variable, set to 'TRUE' if printing is desired

typ

Causal direction criterion number (typ=1 is default) Criterion 1 (Cr1) compares kernel regression absolute values of gradients. Criterion 2 (Cr2) compares kernel regression absolute values of residuals. Criterion 3 (Cr3) compares kernel regression based r*(x|y) with r*(y|x).

rnam

Logical variable, default rnam=FALSE means the user does not want the row names to be (somewhat too cleverly) assigned by the function.

Value

A 7-column matrix called 'outcause' with names of variables X and Y in the first two columns and the name of the 'causal' variable in 3rd col. Remaining four columns report numerical computations of SD1 to SD4, r*(x|y), r*(y|x). Pearson r and p-values for its traditional significance testing.

References

Vinod, H. D.'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, https://doi.org/gffn86

Vinod, H. D. 'New exogeneity tests and causal paths,' Chapter 2 in 'Handbook of Statistics: Conceptual Econometrics Using R', Vol.32, co-editors: H. D. Vinod and C.R. Rao. New York: North Holland, Elsevier Science Publishers, 2019, pp. 33-64.

Examples

Run this code

# NOT RUN {
data(mtcars)
options(np.messages=FALSE)
for(j in 1:3){
a1=allPairs(mtcars[,1:3], typ=j)
print(a1)}

# }