SimAnova | R Documentation |
Given the results from a simulation with runSimulation
form an ANOVA table (without
p-values) with effect sizes based on the eta-squared statistic. These results provide approximate
indications of observable simulation effects, therefore these ANOVA-based results are generally useful
as exploratory rather than inferential tools.
SimAnova(formula, dat, subset = NULL, rates = TRUE)
formula |
an R formula generally of a form suitable for |
dat |
an object returned from |
subset |
an optional argument to be passed to |
rates |
logical; does the dependent variable consist of rates (e.g., returned from
|
Phil Chalmers rphilip.chalmers@gmail.com
Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations
with the SimDesign Package. The Quantitative Methods for Psychology, 16
(4), 248-280.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")}
Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte
Carlo simulation. Journal of Statistics Education, 24
(3), 136-156.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}
data(BF_sim)
# all results (not usually good to mix Power and Type I results together)
SimAnova(alpha.05.F ~ (groups_equal + distribution)^2, BF_sim)
# only use anova for Type I error conditions
SimAnova(alpha.05.F ~ (groups_equal + distribution)^2, BF_sim, subset = var_ratio == 1)
# run all DVs at once using the same formula
SimAnova(~ groups_equal * distribution, BF_sim, subset = var_ratio == 1)
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