FisherSens: Rosenbaum Sensitivity Analysis for Fisher's Exact Test

In rbounds: Perform Rosenbaum Bounds Sensitivity Tests for Matched and Unmatched Data

Rosenbaum Sensitivity Analysis for Fisher's Exact Test

Description

Calculates sensitivity to hidden bias for Fisher's exact test for a two-by-two contingency table, following the method described in Rosenbaum (2002, sec. 4.4).

Usage

FisherSens(totalN, treatedN, totalSuccesses, treatedSuccesses, Gammas)

Arguments

totalN	total number of observations
treatedN	number of treated observations
totalSuccesses	total number of “successes”
treatedSuccesses	number of successes in treatment group
Gammas	vector of Gammas (bounds on the differential odds of treatment) at which to test the significance of the results

Value

Returns a matrix with three columns and number of rows equal to the length of "Gammas". Each row indicates the upper and lower bounds for the (one-sided) p-value for a given value of Gamma.

Author(s)

Devin Caughey, MIT, [e-mail]

See Also

See also binarysens, hlsens, mcontrol

Examples

## Fisher's Lady Tasting Tea: milk first or tea first?
LadyTastingTea <- matrix(c(4, 0, 0, 4), nrow = 2,
                         dimnames = list(Guess = c("Milk", "Tea"),
                                         Truth = c("Milk", "Tea")))
## Define "Milk" as "treated"/"success"
FisherSens(totalN = sum(LadyTastingTea), 
           treatedN = sum(LadyTastingTea["Milk", ]),
           totalSuccesses = sum(LadyTastingTea[, "Milk"]), 
           treatedSuccesses = sum(LadyTastingTea["Milk", "Milk"]), 
           Gammas = seq(1, 2, .2))
## Interpretation: Rejection of the null hypothesis 
## (that the lady cannot discriminate between milk-first and tea-first) 
## is insensitive to bias as large as Gamma = 2.

rbounds documentation built on April 30, 2022, 1:07 a.m.