Power curves for different parameter settings (sample size and effect size) in balanced one-way ANOVA models
Draw power curves for different parameter settings in balanced one-way ANOVA models.
pwr.plot(n=n, k=k, f=f, alpha=alpha)
n |
Sample size per group |
k |
Number of groups |
f |
Effect size |
alpha |
Significant level (Type I error probability) |
This function demonstrates drawing power curves for different sample size and effect size settings. N and f can be either a single value or a sequence of values, but they cannot be single values simultaneously. The combination of them could be (a sequence of n, a sequence of f), (a sequence of n, a single f), or (a single n, a sequence of f).
Pengcheng Lu, Junhao Liu, and Devin Koestler.
Angela Dean & Daniel Voss (1999). Design and Analysis of Experiments. Springer.
## Example 1 n <- seq(2, 30, by=4) f <- 0.5 pwr.plot(n=n, k=5, f=f, alpha=0.05) ## Example 2 n <- 20 f <- seq(0.1, 1.0, length.out=10) pwr.plot(n=n, k=5, f=f, alpha=0.05) ## Example 3 n <- seq(2, 30, by=4) f <- seq(0.1, 1.0, length.out=10) pwr.plot(n=n, k=5, f=f, alpha=0.05)
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