probUnDes: Computing the probability of having desirability scores of...

In randomizeR: Randomization for Clinical Trials

Computing the probability of having desirability scores of zero

Description

Computing the probability of having desirability scores of zero for each desirability function applied to an issue.

Usage

probUnDes(desScore)

## S4 method for signature 'desScores'
probUnDes(desScore)

Arguments

desScore

an object of the class desScores, i.e. an object resulting from applying the function getDesScores

Details

The function probUnDes expects an object that results from the getDesScores function. For each issue it computes the probability that it achieves an undesirable score, i.e. a desirability score of 0. In doing so, it weights the zero desirability scores with the probability that the sequence occurs.

Value

S4 object of class probUnDesirable computing the probability of getting undesirable scores, i.e. desirability scores of 0.

See Also

Representation of randomization procedures: randPar

Generation of randomization sequences: genSeq

issues for the desirability of randomization sequences

Other desirability topics: derFunc, evaluate(), getDesScores(), plotDes(), plotEv()

Examples

# compare Random Allocation Rule to Big Stick Design with respect to different issues
# and their corresponding desirability functions
RAR <- getAllSeq(rarPar(4))
issue1 <- corGuess("CS")
issue2 <- corGuess("DS")
A1 <- assess(RAR, issue1, issue2)

d1 <- derFunc(TV = 0.1, 0.7, 2)
d2 <- derFunc(0.5, c(0.3, 0.8), c(1, 1))
DesScore <- getDesScores(A1, d1, d2, weights = c(5/6, 1/6))

probUnDes(DesScore)

randomizeR documentation built on Sept. 19, 2023, 1:08 a.m.