sensitivitymv (version 1.4.4)

multrnks: Approximate scores for ranks.

Description

Of limited interest to most users, this function is sometimes called by senmv. The function calculates the large sample approximation to a rank score transformation in Lemma 1, expression (9) of Rosenbaum (2011). See also Rosenbaum (2014) and the sensitivitymw package.

Usage

multrnks(rk, m1 = 2, m2 = 2, m = 2)

Value

Vector of length(rk) containing the scores for the ranks in rk.

Arguments

rk

A vector of ranks that may include average ranks for ties.

m1

One of three rank score parameters in Rosenbaum (2011), specifically m1 = underline(m).

m2

One of three rank score parameters in Rosenbaum (2011), specifically m2 = overline(m).

m

One of three rank score parameters in Rosenbaum (2011), specifically m = m.

Author

Paul R. Rosenbaum

References

Rosenbaum, P. R. (2011) <doi:10.1111/j.1541-0420.2010.01535.x> A new u-statistic with superior design sensitivity in matched observational studies. Biometrics 67, 1017-1027.

Rosenbaum, P. R. (2014) <doi:10.1080/01621459.2013.879261> Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls. Journal of the American Statistical Association, 109(507), 1145-1158.

Rosenbaum, P. R. (2024) <doi:10.1080/01621459.2023.2221402> Bahadur efficiency of observational block designs. Journal of the American Statistical Association, 119(547), 1871-1881.

Examples

	multrnks(1:10)
	multrnks(1:10,m1=12,m2=20,m=20)