tdcc: A Measure of Top-Down Correlation
In ODEsensitivity: Sensitivity Analysis of Ordinary Differential Equations

Description Usage Arguments Details Value Author(s) References Examples

With the use of Savage scores, the Top-Down Correlation Coefficient TDCC compares b rankings.

1	tdcc(ranks, pearson = FALSE, plot = FALSE)

`ranks`	[`matrix(nrow = b, ncol = k)`] `(bxk)`-matrix of the ranks of the `k` variables for each of the `b` sensitivity analyses, ties are neglected, must be integers.
`pearson`	[`logical(1)`] Should the ordinary Pearson coefficient with Savage scores be computed (`b = 2`)? Default is `FALSE`.
`plot`	[`logical(1)`] Should scatter plots showing rankings and Savage scores be created (`b = 2`)? Default is `FALSE`.

NOTE: As the implementation of the coefficient of concordance is still defective, please use the Pearson coefficient!

A named vector with components:

kendall: Coefficient of concordance.
pearson: Pearson coefficient (only if pearson = TRUE).

Stefan Theers

R. L. Iman and W. J. Conover, A Measure of Top-Down Correlation, Technometrics, Vol. 29, No. 3 (Aug., 1987), pp. 351–357.

# b=2 sensitivity analysis techniques A and B that rate the influence of 
# k=20 variables/ input parameters (example taken from Iman and Conover, 1987):
ranking <- rbind(A = 1:20,
                 B = c(1,3,2,4,16,10,19,12,18,17,
                       20,5,14,7,8,11,6,15,9,13))
tdcc(ranking, pearson = TRUE, plot = TRUE)