multrnks: Approximate scores for ranks.

In sensitivitymv: Sensitivity Analysis in Observational Studies

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)

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.

Value

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

Author(s)

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)

sensitivitymv documentation built on June 8, 2025, 12:34 p.m.