ss_best_normal {ssutil} | R Documentation |
Sample Size for Selecting the Best Treatment in a Normal Response (Indifference-Zone)
Description
Calculates the minimum common sample size per group needed to achieve a specified probability (power) of correctly selecting the best group using the indifference-zone approach. This method assumes normally distributed responses with a known and common standard deviation.
Usage
ss_best_normal(power, dif, sd, ngroups, seed = NULL)
Arguments
power |
Numeric. Desired probability of correctly selecting the best group. |
dif |
Numeric. Indifferent-zone. Minimum difference that is considered meaningful. |
sd |
Numeric. Common standard deviation of the response variable. |
ngroups |
Integer. Number of groups (treatments) being compared. |
seed |
Optional. Integer seed to use in the internal call to |
Details
The indifference-zone approach guarantees that the probability of correct selection
is at least power
, assuming the best group's mean exceeds the others by at
least dif
. The calculation is based on Bechhofer's Procedure Nb.
Value
Integer. Sample size required per group to achieve the specified power.
Note
The function uses the quantile function multz()
, which computes critical values
for the selection procedure. This implementation assumes equal variances and independent samples.
References
Bechhofer, R.E., Santner, T.J., & Goldsman, D.M. (1995). Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. Wiley Series in Probability and Statistics. ISBN: 0-471-57427-9.
Examples
ss_best_normal( power = 0.8, dif = 0.5, sd = 1, ngroups = 3)