# Evaluation of several randomization procedures with respect to certain desirability functions applied to specified issues. ```r evaluate(..., statistic) ## S4 method for signature 'missing' evaluate(..., statistic) ## S4 method for signature 'character' evaluate(..., statistic) ``` ## Arguments - `...`: at least one object of the class `desScores` or a list of objects of the class `desScores`. - `statistic`: character string that specifies on the basis of which statistic the `evaluate` function should be applied. The statistic can be chosen from "mean", "median", "min" or "max". ## Returns `S4` object of class `evaluation` Comparison of randomization procedures with respect to desirability functions applied to specified issues, summarized by a prespecified statistic. ## Details The `evaluate` function allows the user to compare and evaluate different randomization procedures. It expects a number of objects that result when applying the `getDesScores` function to an assess object and specified desirability functions. The `evaluate` function summarizes the desirability scores of each randomization procedure on the basis of a prespecified statistic and incorporates them into a data frame. If no statistic is specified then it is automatically set to `mean`. If the function is applied to only one object it corresponds simply to `summary(getDesScores(...))`. ## Examples ```r # compare Random Allocation Rule to Big Stick Design with respect to different issues # and their corresponding desirability functions issue1 <- corGuess("CS") issue2 <- corGuess("DS") RAR <- getAllSeq(rarPar(4)) BSD <- getAllSeq(bsdPar(4, mti = 2)) A1 <- assess(RAR, issue1, issue2) A2 <- assess(BSD, 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)) DesScore2 <- getDesScores(A2, d1, d2, weights = c(5/6, 1/6)) evaluate(DesScore, DesScore2) evaluate(DesScore, DesScore2, statistic = "max") ``` ## References D. Schindler **Assessment of Randomization Procedures in the Presence of Selection and Chronological Bias**. PhD Thesis. ## See Also Representation of randomization procedures: `randPar` Generation of randomization sequences: `genSeq` `issues` for the desirability of randomization sequences Other desirability topics: `derFunc`, `getDesScores()`, `plotDes()`, `plotEv()`, `probUnDes()`

evaluate function