OptSig (version 1.0)

Opt.sig.anova.test: Optimal significance level calculation for balanced one-way analysis of variance tests

Description

Computes the optimal significance level for the test for balanced one-way analysis of variance tests

Usage

Opt.sig.anova.test(K = NULL, n = NULL, f = NULL, p = 0.5, k = 1)

Arguments

K

Number of groups

n

Number of observations (per group)

f

Effect size

p

prior probability for H0, default is p = 0.5

k

relative loss from Type I and II errors, k = L2/L1, default is k = 1

Value

alpha.opt

Optimal level of significance

beta.opt

Type II error probability at the optimal level

Details

Refer to Kim and Choi (2017) for the details of k and p

References

Kim and Choi, 2017, Choosing the Level of Significance: A Decision-theoretic Approach: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2652773

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

Stephane Champely (2017). pwr: Basic Functions for Power Analysis. R package version 1.2-1. https://CRAN.R-project.org/package=pwr

See Also

Kim and Choi, 2017, Choosing the Level of Significance: A Decision-theoretic Approach

Examples

# NOT RUN {
Opt.sig.anova.test(f=0.28,K=4,n=20)
# }