some0Pairs: Function reporting detailed kernel causality results in a...

View source: R/some0Pairs.R

some0PairsR Documentation

Function reporting detailed kernel causality results in a 7-column matrix (uses deprecated criterion 1, no longer recommended but may be useful for second and third criterion typ=2,3)


The seven columns produced by this function summarize the results where the signs of stochastic dominance order values (+1 or -1) are weighted by wt=c(1.2,1.1, 1.05, 1) to compute an overall result for all orders of stochastic dominance by a weighted sum for the criteria Cr1 and Cr2. The weighting is obviously not needed for the third criterion Cr3.


  dig = 6,
  verbo = TRUE,
  rnam = FALSE,
  wt = c(1.2, 1.1, 1.05, 1),
  sumwt = 4



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


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


Make verbo= TRUE for printing detailed steps.


Make rnam= TRUE if cleverly created row-names are desired.


Allows user to choose a vector of four alternative weights for SD1 to SD4.


Sum of weights can be changed here =4(default).


The reason for slightly declining weights on the signs from SD1 to SD4 is simply that the local mean comparisons implicit in SD1 are known to be more reliable than local variance implicit in SD2, local skewness implicit in SD3 and local kurtosis implicit in SD4. The source of slightly declining sampling unreliability of higher moments is the higher power of the deviations from the mean needed in their computations. The summary results for all three criteria are reported in one matrix called outVote:

typ=1 reports ('Y', 'X', 'Cause', 'SD1apd', 'SD2apd', 'SD3apd', 'SD4apd') naming variables identifying 'cause' and measures of stochastic dominance using absolute values of kernel regression gradients (or amorphous partial derivatives, apd-s) being minimized by the kernel regression algorithm while comparing the kernel regression 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 regression 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. Pearson correlation coefficient p-val is the p-value for testing the significance of 'r'


Prints three matrices detailing results for Cr1, Cr2 and Cr3. It also returns a grand summary matrix called ‘outVote’ which summarizes all three criteria. In general, a positive sign for weighted sum reported in the column ‘sum’ means that the first variable listed as the input to this function is the ‘kernel cause.’ For example, crime ‘kernel causes’ police officer deployment (not vice versa) is indicated by the positive sign of ‘sum’ (=3.175) reported for that example included in this package.


The output matrix last column for ‘mtcars’ example has the sum of the scores by the three criteria combined. If ‘sum’ is positive, then variable X (mpg) is more likely to have been engineered to kernel cause the response variable Y, rather than vice versa.

The European Crime data has all three criteria correctly suggesting that high crime rate kernel causes the deployment of a large number of police officers.


Prof. H. D. Vinod, Economics Dept., Fordham University, NY.


Vinod, H. D. 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")}

See Also

See Also somePairs


## Not run: 
some0Pairs(mtcars) # first variable is mpg and effect on mpg is of interest

## End(Not run)

## Not run: 

## End(Not run)

generalCorr documentation built on May 1, 2023, 9:06 a.m.