effSizCut {practicalSigni}

Compute Effect Sizes for continuous or categorical data

Description

Psychologists' so-called "effect size" reveals the practical significance of only one regressor. This function generalizes their algorithm to two or more regressors (p>2). Generalization first converts the xi regressor into a categorical treatment variable with only two categories. One imagines that observations larger than the median (xit> median(xi)) are "treated," and those below the median are "untreated." The aim is the measure the size of the (treatment) effect of (xi) on y. Denote other variables with postscript "o" as (xo). Since we have p regressors in our multiple regression, we need to remove the nonlinear kernel regression effect of other variables (xo) on y while focusing on the effect of xi. There are two options in treating (xo) (i) letting xo be as they are in the data (ii) converting xo to binary at the median. One chooses the first option (i) by setting the logical argument ane=TRUE in calling the function. ane=TRUE is the default. Set ane=FALSE for the second option.

Usage

effSizCut(y, bigx, ane = TRUE)

Arguments

y

A (T x 1) vector of dependent variable data values.

bigx

A (T x p) data matrix of xi regressor variables associated with the regression.

ane

logical variable controls the treatment of other regressors. If ane=TRUE (default), other regressors are used in kernel regression without forcing them to be binary variables. When ane=FALSE, the kernel regression removes the effect of other regressors when other regressors are also binary type categorical variables

Value

out vector with p values of t-statistics for p regressors

Note

The aim is to answer the following question. Which regressor has the largest effect on the dependent variable? We assume that the signs of regressors are already adjusted such that a numerically larger effect size suggests that the corresponding regressor is most important, having the largest effect size in explaining y the dependent variable.

Author(s)

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

See Also

pracSig13

Examples

set.seed(9)
 y=sample(1:15,replace = TRUE)
 x1=sample(2:16, replace = TRUE)
 x2=sample(3:17, replace = TRUE)
effSizCut(y,bigx=cbind(x1,x2),ane=TRUE)