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OptSig (version 2.1)

OptSig.anova: 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

OptSig.anova(K = NULL, n = NULL, f = NULL, p = 0.5, k = 1, Figure = TRUE)

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

Figure

show graph if TRUE (default); No graph if FALSE

Value

alpha.opt

Optimal level of significance

beta.opt

Type II error probability at the optimal level

Details

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)

References

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

See Also

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.>

Examples

Run this code
# NOT RUN {
OptSig.anova(f=0.28,K=4,n=20)
# }

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