Description Usage Arguments Value References Examples

Simulation function used to estimate power

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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
)
``` |

`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) |

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

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## 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)
``` |

```
Registered S3 methods overwritten by 'lme4':
method from
cooks.distance.influence.merMod car
influence.merMod car
dfbeta.influence.merMod car
dfbetas.influence.merMod car
Power and Effect sizes for ANOVA tests
power effect_size
anova_condition 10 0.03039
anova_voice 100 0.53424
anova_condition:voice 10 0.03689
Power and Effect sizes for pairwise comparisons (t-tests)
power
p_condition_cheerful_voice_human_condition_cheerful_voice_robot 100
p_condition_cheerful_voice_human_condition_sad_voice_human 20
p_condition_cheerful_voice_human_condition_sad_voice_robot 100
p_condition_cheerful_voice_robot_condition_sad_voice_human 100
p_condition_cheerful_voice_robot_condition_sad_voice_robot 0
p_condition_sad_voice_human_condition_sad_voice_robot 100
effect_size
p_condition_cheerful_voice_human_condition_cheerful_voice_robot -0.7714
p_condition_cheerful_voice_human_condition_sad_voice_human -0.0143
p_condition_cheerful_voice_human_condition_sad_voice_robot -0.7790
p_condition_cheerful_voice_robot_condition_sad_voice_human 0.7929
p_condition_cheerful_voice_robot_condition_sad_voice_robot 0.0132
p_condition_sad_voice_human_condition_sad_voice_robot -0.7242
Within-Subject Factors Included: Check MANOVA Results
```

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