SimDesign (version 2.21)

createDesign: Create the simulation design object

Description

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.

Usage

createDesign(
  ...,
  subset,
  fractional = NULL,
  tibble = TRUE,
  stringsAsFactors = FALSE,
  fully.crossed = TRUE
)
# S3 method for Design
print(x, list2char = TRUE, pillar.sigfig = 5, show.IDs = FALSE, ...)
# S3 method for Design
[(x, i, j, ..., drop = FALSE)
rbindDesign(..., keep.IDs = FALSE)

Value

a tibble or data.frame containing the simulation experiment conditions to be evaluated in runSimulation

Arguments

...

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

subset

(optional) a logical vector indicating elements or rows to keep to create a partially crossed simulation design

fractional

a fractional design matrix returned from the FrF2 package. Note that the order of the factor names/labels are associated with the respective ... inputs

tibble

logical; return a tibble object instead of a data.frame? Default is TRUE

stringsAsFactors

logical; should character variable inputs be coerced to factors when building a data.frame? Default is FALSE

fully.crossed

logical; create a fully-crossed design object? Setting to FALSE will attempt to combine the design elements column-wise via data.frame(...) instead of expand.grid(...)

x

object of class 'Design'

list2char

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

pillar.sigfig

number of significant digits to print. Default is 5

show.IDs

logical; print the internally stored Design ID indicators?

i

row index

j

column index

drop

logical; drop to lower dimension class?

keep.IDs

logical; keep the internal ID variables in the Design objects? Use this when row-binding conditions that are matched with previous conditions (e.g., when using expandDesign)

Author

Phil Chalmers

References

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. 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. tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")

See Also

expandDesign

Examples

if (FALSE) {

# 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

# design without crossing (inputs taken-as is)
Design <- createDesign(N = c(10, 20),
                       SD = c(1, 2), cross=FALSE)
Design   # only 2 rows

##########

## fractional factorial example

library(FrF2)
# help(FrF2)

# 7 factors in 32 runs
fr <- FrF2(32,7)
dim(fr)
fr[1:6,]

# Create working simulation design given -1/1 combinations
fDesign <- createDesign(sample_size=c(100,200),
                        mean_diff=c(.25, 1, 2),
                        variance.ratio=c(1,4, 8),
                        equal_size=c(TRUE, FALSE),
                        dists=c('norm', 'skew'),
                        same_dists=c(TRUE, FALSE),
                        symmetric=c(TRUE, FALSE),
                        # remove same-normal combo
                        subset = !(symmetric & dists == 'norm'),
                        fractional=fr)
fDesign

}