View source: R/power_twoway_between.R

power_twoway_between | R Documentation |

Analytic power calculation for two-way between designs.

power_twoway_between(design_result, alpha_level = 0.05)

`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

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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