stat_indeptest: Compute the test statistic for the robust independence test...
In obouaziz/robusTest0: Calibrated correlation, two samples tests

Description Usage Arguments Details Value See Also Examples

For two continuous variables compute the maximal distance between the joint empirical cumulative distribution function and the product of the marginal empirical cumulative distribution functions.

1	stat_indeptest(x, y)

x, y

the two continuous variables. Must be of same length.

Let (x1,y1), ..., (x_n,y_n) be a bivariate sample of n continuous variables. Its corresponding bivariate e.c.d.f. (empirical cumulative distribution function) Fn is defined as:

Fn(t1,t2) = #{xi<=t1,yi<=t2}/n = sum_{i=1}^n Indicator(xi<=t1,yi<=t2)/n.

Let Fn(t1) and Fn(t2) be the marginals e.c.d.f. The function returns the value of:

n^(1/2) sup_{t1,t2} |Fn(t1,t2)-Fn(t1)*Fn(t2)|.

Returns the test statistic of the robust independent test.

indeptest, simulecdf, ecdf2D.

#Simulated data 1
x<-c(0.2, 0.3, 0.1, 0.4)
y<-c(0.5, 0.4, 0.05, 0.2)
stat_indeptest(x,y)

#Simulated data 2
n<-40
x<-rnorm(n)
y<-x^2+0.3*rnorm(n)
plot(x,y)
stat_indeptest(x,y)

#Application on the Evans dataset
data(Evans)
with(Evans,stat_indeptest(CHL[CDH==1],DBP[CDH==1]))