Description Usage Arguments Value References Examples
View source: R/power_twoway_between.R
Analytic power calculation for two-way between designs.
1 | power_twoway_between(design_result, alpha_level = 0.05)
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design_result |
Output from the ANOVA_design function |
alpha_level |
Alpha level used to determine statistical significance |
mu = means
sigma = standard deviation
n = sample size
alpha_level = alpha level
Cohen_f_A = Cohen's f for main effect A
Cohen_f_B = Cohen's f for main effect B
Cohen_f_AB = Cohen's f for the A*B interaction
f_2_A = Cohen's f squared for main effect A
f_2_B = Cohen's f squared for main effect B
f_2_AB = Cohen's f squared for A*B interaction
lambda_A = lambda for main effect A
lambda_B = lambda for main effect B
lambda_AB = lambda for A*B interaction
critical_F_A = critical F-value for main effect A
critical_F_B = critical F-value for main effect B
critical_F_AB = critical F-value for A*B interaction
power_A = power for main effect A
power_B = power for main effect B
power_AB = power for A*B interaction
df_A = degrees of freedom for main effect A
df_B = degrees of freedom for main effect B
df_AB = degrees of freedom for A*B interaction
df_error = degrees of freedom for error term
eta_p_2_A = partial eta-squared for main effect A
eta_p_2_B = partial eta-squared for main effect B
eta_p_2_AB = partial eta-squared for A*B interaction
mean_mat = matrix of the means
too be added
1 2 3 4 | design_result <- ANOVA_design(design = "2b*2b", n = 40, mu = c(1, 0, 1, 0),
sd = 2, labelnames = c("condition", "cheerful", "sad",
"voice", "human", "robot"))
power_result <- power_twoway_between(design_result, alpha_level = 0.05)
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