generalCorr (version 1.2.0)

somePairs: Function reporting kernel causality results as a 7-column matrix.(deprecated)

Description

This function lets the user choose one of three criteria to determine causal direction by setting typ as 1, 2 or 3. This function reports results for only one criterion at a time unlike the function some0Pairs which summarizes the resulting causal directions for all criteria with suitable weights. If some variables are `control' variables, use someCPairs, C=control.

Usage

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

Arguments

mtx

The data matrix in the first column is paired with all others.

dig

Number of digits for reporting (default dig=6).

verbo

Make verbo= TRUE for printing detailed steps.

typ

Must be 1 (default), 2 or 3 for the three criteria.

rnam

Make rnam= TRUE if cleverly created rownames are desired.

Value

A matrix containing causal identification results for one criterion. The first column of the input mtx having p columns is paired with (p-1) other columns The output matrix headings are self-explanatory and distinct for each criterion Cr1 to Cr3.

Details

(typ=1) reports ('Y', 'X', 'Cause', 'SD1apd', 'SD2apd', 'SD3apd', 'SD4apd') nameing variables identifying 'cause' and measures of stochastic dominance using absolute values of kernel regression gradients comparing regresson of X on Y with that of Y on X.

(typ=2) reports ('Y', 'X', 'Cause', 'SD1res', 'SD2res', 'SD3res', 'SD4res') and measures of stochastic dominance using absolute values of kernel regression residuals comparing regresson of X on Y with that of Y on X.

(typ=3) reports ('Y', 'X', 'Cause', 'r*X|Y', 'r*Y|X', 'r', 'p-val') containing generalized correlation coefficients r*, 'r' refers to the Pearson correlation coefficient and p-val column has the p-values for testing the significance of Pearson's 'r'.

References

H. D. Vinod 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, 10.1080/03610918.2015.1122048

See Also

The related function some0Pairs may be more useful, since it reports on all three criteria (by choosing typ=1,2,3) and further summarizes their results by weighting to help choose causal paths.

Examples

# NOT RUN {
# }
# NOT RUN {
data(mtcars)
somePairs(mtcars)
# }
# NOT RUN {
# }