spearrs {depcoeff}

Spearman regression coefficient for split domains

Description

The function spearrs evaluates the multivariate Spearman regression coefficient for two regressors and split regressor region. It describes how well the target variable can be fit in each split region by a function which is increasing w.r.t. some regressors and decreasing w.r.t. the other regressors.

Usage

spearrs(x,y,splitp=NULL)

Arguments

x

datamatrix of regressor variables with two columns,

y

data vector of the target variable

splitp

vector of length 2 of the splitting points, If p1 is the first component of this vector, then the point splits the domain of the first regressor into a left region of fraction p1 of data items and a right region of the remaining data items. The same is done for the second regressor. As the result we obtain 4 subregions of the regressor domain. default=c(0.5,0.5)

Value

list of Kendall regression coefficients for the 4 split regions and the total coefficient together with the corresponding optimal directions. direction ++ means that y increases whenever both regressors increases direction +- means that y increases whenever the first regressor increases and the other regressor decreases..etc.

References

Eckhard Liebscher (2019). A copula-based dependence measure for regression analysis. submitted

Examples

library(MASS)
data<- gilgais
spearrs(data[,1:2],data[,3],splitp=c(0.4,0.6))