
Create a partially or fully-crossed data object reflecting the unique simulation design conditions. Each row of the returned object represents a unique simulation condition, and each column represents the named factor variables under study.
createDesign(..., subset, tibble = TRUE, stringsAsFactors = FALSE)# S3 method for Design
print(x, list2char = TRUE, ...)
comma separated list of named input objects representing the simulation
factors to completely cross. Note that these arguments are passed to
expand.grid
to perform the complete crossings
(optional) a logical vector indicating elements or rows to keep to create a partially crossed simulation design
logical; return a tibble
object instead of a
data.frame
? Default is TRUE
logical; should character variable inputs be coerced
to factors when building a data.frame
? Default is FALSE
object returned by createDesign
logical; for tibble
object re-evaluate list elements
as character vectors for better printing of the levels? Note that this
does not change the original classes of the object, just how they are printed.
Default is TRUE
a tibble
or data.frame
containing the simulation experiment
conditions to be evaluated in runSimulation
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.
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.
10.1080/10691898.2016.1246953
# NOT RUN {
# modified example from runSimulation()
Design <- createDesign(N = c(10, 20),
SD = c(1, 2))
Design
# remove N=10, SD=2 row from initial definition
Design <- createDesign(N = c(10, 20),
SD = c(1, 2),
subset = !(N == 10 & SD == 2))
Design
# example with list inputs
Design <- createDesign(N = c(10, 20),
SD = c(1, 2),
combo = list(c(0,0), c(0,0,1)))
Design # notice levels printed (not typical for tibble)
print(Design, list2char = FALSE) # standard tibble output
Design <- createDesign(N = c(10, 20),
SD = c(1, 2),
combo = list(c(0,0), c(0,0,1)),
combo2 = list(c(5,10,5), c(6,7)))
Design
print(Design, list2char = FALSE) # standard tibble output
# }
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