randomizeR (version 1.4.2)

getDesScores: Applying desirability functions on issues of individual randomization sequences

Description

Applying desirability function on issues of individual randomization sequences.

Usage

getDesScores(assess, ..., weights)
# S4 method for assessment,missing
getDesScores(assess, ..., weights)
# S4 method for assessment,numeric
getDesScores(assess, ..., weights)

Arguments

assess

object of class assessment.

...

at least one object of class derFunc or a list of objects of the class derFunc.

weights

weights for computing the geometric mean of several desirability scores. If missing, the issues are automatically equally weighted.

Value

S4 object of class desirability summarizing the desirability of the randomization procedure.

Details

Randomization sequences behave differently with respect to issues like selection bias, chronological bias, or loss in power estimation. The getDesScores function evaluates the behaviour of randomization sequences with respect to these issues. The difference to the assess function is that it scales them to [0,1] and makes them easier interpretable. The first argument should be a result of the assess function. The second argument should be any number of derFunc objects that represent the desirability functions. The last argument weights may be provided if the desirability functions should be weighted differently.

See Also

Representation of randomization procedures: randPar

Generation of randomization sequences: genSeq

issues for the desirability of randomization sequences

Other desirability topics: derFunc, evaluate, plotDes, plotEv, probUnDes

Examples

# NOT RUN {
# Compute the desire-function for the full set of Random Allocation Rule for N=4 patients
sequences <- getAllSeq(rarPar(4))
issue1 <- corGuess("CS")
issue2 <- chronBias("linT", 0.25, "exact")
endp <- normEndp(mu = c(0,0), sigma = c(1,1))
A <- assess(sequences, issue1, issue2, endp = endp)
d1 <- derFunc(0.5, 0.75, 1)
d2 <- derFunc(0.05, 0.1, 1)

D1 <- getDesScores(A, d1, d2)
summary(D1)

D2 <- getDesScores(A, d1, d2, weights = c(3/4, 1/4))
summary(D2)

# }