Simulation function used to estimate power
ANOVA_power(
design_result,
alpha_level = Superpower_options("alpha_level"),
correction = Superpower_options("correction"),
p_adjust = "none",
nsims = 1000,
seed = NULL,
verbose = Superpower_options("verbose"),
emm = Superpower_options("emm"),
emm_model = Superpower_options("emm_model"),
contrast_type = Superpower_options("contrast_type"),
emm_p_adjust = "none",
emm_comp = NULL
)Output from the ANOVA_design function
Alpha level used to determine statistical significance
Set a correction of violations of sphericity. This can be set to "none", "GG" Greenhouse-Geisser, and "HF" Huynh-Feldt
Correction for multiple comparisons. This will adjust p values for ANOVA/MANOVA level effects; see ?p.adjust for options
number of simulations to perform
Set seed for reproducible results
Set to FALSE to not print results (default = TRUE)
Set to FALSE to not perform analysis of estimated marginal means
Set model type ("multivariate", or "univariate") for estimated marginal means
Select the type of comparison for the estimated marginal means. Default is pairwise. See ?emmeans::`contrast-methods` for more details on acceptable methods.
Correction for multiple comparisons; default is "none". See ?summary.emmGrid for more details on acceptable methods.
Set the comparisons for estimated marginal means comparisons. This is a factor name (a), combination of factor names (a+b), or for simple effects a | sign is needed (a|b)
Returns dataframe with simulation data (p-values and effect sizes), anova results (type 3 sums of squares) and simple effect results, and plots of p-value distribution.
"sim_data"Output from every iteration of the simulation
"main_result"The power analysis results for ANOVA effects.
"pc_results"The power analysis results for pairwise comparisons.
"manova_results"Default is "NULL". If a within-subjects factor is included, then the power of the multivariate (i.e. MANOVA) analyses will be provided.
"emm_results"The power analysis results of the estimated marginal means.
"plot1"Distribution of p-values from the ANOVA results.
"plot2"Distribution of p-values from the pairwise comparisons results.
"correction"The correction for sphericity applied to the simulation results.
"p_adjust"The p-value adjustment applied to the simulation results for ANOVA/MANOVA omnibus tests and t-tests.
"emm_p_adjust"The p-value adjustment applied to the simulation results for the estimated marginal means.
"nsims"The number of simulations run.
"alpha_level"The alpha level, significance cut-off, used for the power analysis.
"method"Record of the function used to produce the simulation
too be added
if (FALSE) {
## 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)
}