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

## Description

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

## Usage

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

## Arguments

 `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`.

## Details

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

## Value

A named vector with components:

• `kendall`: Coefficient of concordance.

• `pearson`: Pearson coefficient (only if `pearson = TRUE`).

Stefan Theers

## References

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

## Examples

 ```1 2 3 4 5 6``` ```# 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) ```

ODEsensitivity documentation built on May 1, 2019, 6:32 p.m.