| WMWssp_minimize {WMWssp} | R Documentation |
Minimizing samplesize for a given Type I and II error rate and prior information x and y
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
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
# 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)