# WMWssp_minimize: Minimizing samplesize for a given Type I and II error rate... In WMWssp: Wilcoxon-Mann-Whitney Sample Size Planning

## Description

This function minimizes the sample size for a given power and type-I error rate with respect to the allocation rate t = n_1/N.

## Usage

 ```1 2``` ```WMWssp_minimize(x, y, alpha = 0.05, power = 0.8, simulation = FALSE, nsim = 10^4) ```

## Arguments

 `x` a vector of prior information for the first group `y` a vector of prior information for the second group `alpha` Type I error rate `power` Power to detect a relative effect based on the prior information `simulation` TRUE if a power simulation should be carried out `nsim` number of simulations for the power simulation

## Value

Returns an object from class WMWssp containing

 `result` A dataframe with the results. `t` The optimal allocation rate for minimizing the sample size. `alpha` The type-I error rate which was used. `power` The power which was used. `N` The minimized sample size.

## References

Brunner, E., Bathke A. C. and Konietschke, F. Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS. Springer Verlag. to appear.

Happ, M., Bathke, A. C., & Brunner, E. (2019). Optimal Sample Size Planning for the Wilcoxon-Mann-Whitney-Test. Statistics in medicine, 38(3), 363-375.

## Examples

 ```1 2 3 4 5 6 7 8``` ```# Prior information for the reference group x <- c(315,375,356,374,412,418,445,403,431,410,391,475,379) # generate data for treatment group based on a shift effect y <- x - 20 # calculate optimal t ssp <- WMWssp_minimize(x, y, alpha = 0.05, power = 0.8) summary(ssp) ```

WMWssp documentation built on July 9, 2019, 5:03 p.m.