ANOVA_power {Superpower} | R Documentation |
Simulation function used to estimate power
Description
Simulation function used to estimate power
Usage
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
)
Arguments
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" Greenhouse-Geisser, and "HF" Huynh-Feldt |
p_adjust |
Correction for multiple comparisons. This will adjust p values for ANOVA/MANOVA level effects; see ?p.adjust for options |
nsims |
number of simulations to perform |
seed |
Set seed for reproducible results |
verbose |
Set to FALSE to not print results (default = TRUE) |
emm |
Set to FALSE to not perform analysis of estimated marginal means |
emm_model |
Set model type ("multivariate", or "univariate") for estimated marginal means |
contrast_type |
Select the type of comparison for the estimated marginal means. Default is pairwise. See ?emmeans::'contrast-methods' for more details on acceptable methods. |
emm_p_adjust |
Correction for multiple comparisons; default is "none". See ?summary.emmGrid for more details on acceptable methods. |
emm_comp |
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) |
Value
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
References
too be added
Examples
## Not run:
## 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)
## End(Not run)