Computes the optimal significance level for the test for balanced one-way analysis of variance tests
OptSig.anova(K = NULL, n = NULL, f = NULL, p = 0.5, k = 1, Figure = TRUE)
Number of groups
Number of observations (per group)
Effect size
prior probability for H0, default is p = 0.5
relative loss from Type I and II errors, k = L2/L1, default is k = 1
show graph if TRUE (default); No graph if FALSE
Optimal level of significance
Type II error probability at the optimal level
Refer to Kim and Choi (2020) for the details of k and p
For the value of f, refer to Cohen (1988) or Champely (2017)
Kim and Choi, 2020, Choosing the Level of Significance: A Decision-theoretic Approach: Abacus: a Journal of Accounting, Finance and Business Studies. Wiley. <https://doi.org/10.1111/abac.12172>
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
Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician. <https://doi.org/10.1080/00031305.2020.1750484.>
# NOT RUN {
OptSig.anova(f=0.28,K=4,n=20)
# }
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