library(faux)
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Welcome to faux. For support and examples visit:
https://debruine.github.io/faux/
- Get and set global package options with: faux_options()
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1 Introduction
1.1 Why simulate data?
1.1.1 Pre-registration
1.1.2 Power and sensitivity
- You can get empirical power analyses for simple designs, like a paired samples t-test or 1-way ANOVA.
- This gets trickier is you have unbalanced subject numbers, although {Superpower} can do this (by using a simulation method that was derived from an early version of faux)
- However, if you can simulate your data, you can analyse it with any method and calculate power, sensitivity, or any other metric you are interested in.
- The caveat here is that it is not always straightforward to know how to simulate data realistically, often because you only have summary data from existing literature.
- Example of this in Section 2.1.2 below.
1.1.3 Reproducible examples
- Although open data sharing should be something we strive to achieve, it is not always legally or ethically possible
- Datasets can also be too big to feasibly transfer or analyse
- You can use simulation to create a dataset that has the same summary parameters at the original, but distinct individual values or is much smaller.
1.1.4 Enhance understanding
- Using summary parameters to generate data can enhance understanding
- Plot or analyse the simulated data to see how the summary parameters are reflected in the plots or results
- Change simulation parameters to see how plots and analyses change
- Example of this in Section 2.2 below.
1.1.5 Demo teaching data
1.2 Existing methods
- base R
- excel
- simr
1.3 Faux
- what gaps does faux fill?
- focus on summary stats for simulation
- flexible wide and long data designs
- shiny app
2 Examples
2.1 Fixed design
2.1.1 Simulate data
In [2]:
fixed <- sim_design(
within = list(time = c(day = "Day", night = "Night")),
between = list(pet = c(cat = "Cats", dog = "Dogs")),
mu = c(day_cat = 10, night_cat = 15, day_dog = 20, night_dog = 10),
sd = 3.5,
n = c(cat = 20, dog = 25),
vardesc = c(pet = "Pet Type", time = "Time"),
dv = list(score = "Awakeness Score"),
long = TRUE
)
In [3]:
plot(fixed)Ignoring unknown labels:
• colour : "Time"
In [4]:
get_params(fixed)In [5]:
get_design(fixed)* [DV] score: Awakeness Score
* [ID] id: id
* Within-subject variables:
* time: Time:
* day: Day
* night: Night
* Between-subject variables:
* pet: Pet Type:
* cat: Cats
* dog: Dogs
* Parameters:
|pet |time | day| night| n| mu| sd|
|:---|:-----|---:|-----:|--:|--:|---:|
|cat |day | 1| 0| 20| 10| 3.5|
|cat |night | 0| 1| 20| 15| 3.5|
|dog |day | 1| 0| 25| 20| 3.5|
|dog |night | 0| 1| 25| 10| 3.5|