R/SimAnova.R
SimAnova.RdGiven 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)an R formula generally of a form suitable for lm or
aov. However, if the dependent variable (left size of the equation) is omitted
then all the dependent variables in the simulation will be used and the result will return
a list of analyses
an object returned from runSimulation of class 'SimDesign'
an optional argument to be passed to subset with the same name. Used to
subset the results object while preserving the associated attributes
logical; does the dependent variable consist of rates (e.g., returned from
ECR or EDR)? Default is TRUE, which will use the logit of the DV
to help stabilize the proportion-based summary statistics when computing the parameters and
effect sizes
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.
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.
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)
#> SS df MS F p sig eta.sq eta.sq.part
#> groups_equal 0.080 1 0.080 0.022 0.885 . 0.001 0.001
#> distribution 17.790 3 5.930 1.590 0.223 . 0.192 0.193
#> groups_equal:distribution 0.006 3 0.002 0.001 1.000 . 0.000 0.000
#> Residuals 74.598 20 3.730 NA NA 0.806 NA
# only use anova for Type I error conditions
SimAnova(alpha.05.F ~ (groups_equal + distribution)^2, BF_sim, subset = var_ratio == 1)
#> SS df MS F p sig eta.sq eta.sq.part
#> groups_equal 0.027 1 0.027 0.281 0.610 . 0.001 0.034
#> distribution 30.755 3 10.252 108.101 0.000 *** 0.975 0.976
#> groups_equal:distribution 0.002 3 0.001 0.006 0.999 . 0.000 0.002
#> Residuals 0.759 8 0.095 NA NA 0.024 NA
# run all DVs at once using the same formula
SimAnova(~ groups_equal * distribution, BF_sim, subset = var_ratio == 1)
#> $alpha.05.F
#> SS df MS F p sig eta.sq eta.sq.part
#> groups_equal 0.027 1 0.027 0.281 0.610 . 0.001 0.034
#> distribution 30.755 3 10.252 108.101 0.000 *** 0.975 0.976
#> groups_equal:distribution 0.002 3 0.001 0.006 0.999 . 0.000 0.002
#> Residuals 0.759 8 0.095 NA NA 0.024 NA
#>
#> $alpha.05.Jacknife
#> SS df MS F p sig eta.sq eta.sq.part
#> groups_equal 0.077 1 0.077 3.644 0.093 . 0.016 0.313
#> distribution 4.462 3 1.487 70.265 0.000 *** 0.933 0.963
#> groups_equal:distribution 0.072 3 0.024 1.140 0.390 . 0.015 0.300
#> Residuals 0.169 8 0.021 NA NA 0.035 NA
#>
#> $alpha.05.Layard
#> SS df MS F p sig eta.sq eta.sq.part
#> groups_equal 0.004 1 0.004 0.056 0.818 . 0.000 0.007
#> distribution 10.111 3 3.370 46.579 0.000 *** 0.943 0.946
#> groups_equal:distribution 0.023 3 0.008 0.104 0.955 . 0.002 0.038
#> Residuals 0.579 8 0.072 NA NA 0.054 NA
#>
#> $alpha.05.Levene
#> SS df MS F p sig eta.sq eta.sq.part
#> groups_equal 0.024 1 0.024 1.615 0.239 . 0.006 0.168
#> distribution 4.263 3 1.421 93.943 0.000 *** 0.960 0.972
#> groups_equal:distribution 0.030 3 0.010 0.661 0.599 . 0.007 0.199
#> Residuals 0.121 8 0.015 NA NA 0.027 NA
#>
#> $alpha.05.W10
#> SS df MS F p sig eta.sq eta.sq.part
#> groups_equal 0.040 1 0.040 0.573 0.471 . 0.021 0.067
#> distribution 1.263 3 0.421 5.956 0.020 . 0.669 0.691
#> groups_equal:distribution 0.019 3 0.006 0.092 0.963 . 0.010 0.033
#> Residuals 0.566 8 0.071 NA NA 0.299 NA
#>
#> $alpha.05.W50
#> SS df MS F p sig eta.sq eta.sq.part
#> groups_equal 0.073 1 0.073 3.005 0.121 . 0.048 0.273
#> distribution 1.164 3 0.388 16.002 0.001 ** 0.763 0.857
#> groups_equal:distribution 0.095 3 0.032 1.304 0.338 . 0.062 0.328
#> Residuals 0.194 8 0.024 NA NA 0.127 NA
#>