ANOVA_exact | R Documentation |
Simulates an exact dataset (mu, sd, and r represent empirical, not population, mean and covariance matrix) from the design to calculate power
ANOVA_exact( design_result, correction = Superpower_options("correction"), alpha_level = Superpower_options("alpha_level"), verbose = Superpower_options("verbose"), emm = Superpower_options("emm"), emm_model = Superpower_options("emm_model"), contrast_type = Superpower_options("contrast_type"), liberal_lambda = Superpower_options("liberal_lambda"), emm_comp ) ANOVA_exact2( design_result, correction = Superpower_options("correction"), alpha_level = Superpower_options("alpha_level"), verbose = Superpower_options("verbose"), emm = Superpower_options("emm"), emm_model = Superpower_options("emm_model"), contrast_type = Superpower_options("contrast_type"), emm_comp, liberal_lambda = Superpower_options("liberal_lambda") )
design_result |
Output from the ANOVA_design function |
correction |
Set a correction of violations of sphericity. This can be set to "none", "GG" Greenhouse-Geisser, and "HF" Huynh-Feldt |
alpha_level |
Alpha level used to determine statistical significance |
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. |
liberal_lambda |
Logical indicator of whether to use the liberal (cohen_f^2\*(num_df+den_df)) or conservative (cohen_f^2\*den_df) calculation of the noncentrality (lambda) parameter estimate. Default is FALSE. |
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) |
Returns dataframe with simulation data (power and effect sizes!), anova results and simple effect results, plot of exact data, and alpha_level. Note: Cohen's f = sqrt(pes/1-pes) and the noncentrality parameter is = f^2*df(error)
"dataframe"
A dataframe of the simulation result.
"aov_result"
aov
object returned from aov_car
.
"aov_result"
emmeans
object returned from emmeans
.
"main_result"
The power analysis results for ANOVA level effects.
"pc_results"
The power analysis results for the pairwise (t-test) comparisons.
"emm_results"
The power analysis results of the pairwise comparison results.
"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.
"alpha_level"
The alpha level, significance cut-off, used for the power analysis.
"method"
Record of the function used to produce the simulation
"plot"
A plot of the dataframe from the simulation; should closely match the meansplot in ANOVA_design
ANOVA_exact2
: An extension of ANOVA_exact that uses the effect sizes calculated from very large sample size empirical simulation. This allows for small sample sizes, where ANOVA_exact cannot, while still accurately estimating power. However, model objects (emmeans and aov) are not included as output, and pairwise (t-test) results are not currently supported.
Varying the sd or r (e.g., entering multiple values) violates assumptions of homoscedascity and sphericity respectively
## 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")) exact_result <- ANOVA_exact(design_result, alpha_level = 0.05)
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