generalCorr (version 1.2.6)

abs_stdapd: Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized.

Description

1) standardize the data to force mean zero and variance unity, 2) kernel regress x on y, with the option `gradients = TRUE' and finally 3) compute the absolute values of gradients

Usage

abs_stdapd(x, y)

Value

Absolute values of kernel regression gradients are returned after standardizing the data on both sides so that the magnitudes of amorphous partial derivatives (apd's) are comparable between regression of x on y on the one hand and regression of y on x on the other.

Arguments

x

vector of data on the dependent variable

y

data on the regressors which can be a matrix

Author

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

Details

The first argument is assumed to be the dependent variable. If abs_stdapd(x,y) is used, you are regressing x on y (not the usual y on x). The regressors can be a matrix with 2 or more columns. The missing values are suitably ignored by the standardization.

Examples

if (FALSE) {
set.seed(330)
x=sample(20:50)
y=sample(20:50)
abs_stdapd(x,y)
}