Applying desirability functions on issues of individual randomization sequences
Applying desirability function on issues of individual randomization sequences.
getDesScores(assess, ..., weights) ## S4 method for signature 'assessment,missing' getDesScores(assess, ..., weights) ## S4 method for signature 'assessment,numeric' getDesScores(assess, ..., weights)
Randomization sequences behave differently with respect to issues
like selection bias, chronological bias, or loss in power estimation.
The getDesScores function evaluates the behavior 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.
S4 object of class desirability summarizing the desirability of the
randomization procedure.
Representation of randomization procedures: randPar
Generation of randomization sequences: genSeq
issues for the desirability of randomization sequences
# compute the desire-function for the full set of Random Allocation Rule for N = 4
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.