Description Usage Arguments Value References Examples
Simulation function used to perform the simulation
1 2 | ANOVA_power(design_result, alpha_level = 0.05, correction = "none",
p_adjust = "none", nsims = 1000, seed = NULL, verbose = TRUE)
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design_result |
Output from the ANOVA_design function |
alpha_level |
Alpha level used to determine statistical significance |
correction |
Set a correction of violations of sphericity. This can be set to "none", "GG" Grennhouse-Geisser, and "HF" Huynh-Feldt |
p_adjust |
Correction for multiple comparisons |
nsims |
number of simulations to perform |
seed |
Set seed for reproducible results |
verbose |
Set to FALSE to not print results (default = TRUE) |
Returns dataframe with simulation data (p-values and effect sizes), anova results and simple effect results, plots of p-value distribution, p_adjust = p_adjust, nsims, and alpha_level.
too be added
1 2 3 4 5 6 7 8 9 10 | ## Set up a within design with 2 factors, each with 2 levels,
## with correlation between observations of 0.8,
## 40 participants (who do all conditions), and standard deviation of 2
## with a mean pattern of 1, 0, 1, 0, conditions labeled 'condition' and
## 'voice', with names for levels of "cheerful", "sad", amd "human", "robot"
design_result <- ANOVA_design(design = "2w*2w", n = 40, mu = c(1, 0, 1, 0),
sd = 2, r = 0.8, labelnames = c("condition", "cheerful",
"sad", "voice", "human", "robot"))
power_result <- ANOVA_power(design_result, alpha_level = 0.05,
p_adjust = "none", seed = 2019, nsims = 10)
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