The function `GRD()`

generates a data frame containing
random data suitable for analyses.
The data can be from within-subject or between-group designs.
Within-subject designs are in wide format. The function was originally
presented in Calderini and Harding (2019)
.

```
GRD(
RenameDV = "DV",
SubjectsPerGroup = 100,
BSFactors = "",
WSFactors = "",
Effects = list(),
Population = list(mean = 0, stddev = 1, rho = 0, scores =
"rnorm(1, mean = GM, sd = STDDEV)"),
Contaminant = list(mean = 0, stddev = 1, rho = 0, scores =
"rnorm(1, mean = CGM, sd = CSTDDEV)", proportion = 0)
)
```

- RenameDV
provide a name for the dependent variable (default DV)

- SubjectsPerGroup
indicates the number of simulated scores per group (default 100 in each group)

- BSFactors
a string indicating the between-subject factor(s) with, between parenthesis, the number of levels or the list of level names. Multiple factors are separated with a colon ":" or enumerated in a vector of strings.

- WSFactors
a string indicating the within-subject factor(s) in the same format as the between-subject factors

- Effects
a list detailing the effects to apply to the data

- Population
a list providing the population characteristics (default is a normal distribution with a mean of 0 and standard deviation of 1)

- Contaminant
a list providing the contaminant characteristics and the proportion of contaminant (default 0)

a data.frame with the simulated scores.

Note that the `range`

effect specification has been renamed
`extent`

to avoid masking the base function `base::range`

.

Calderini M, Harding B (2019).
“GRD for R: An intuitive tool for generating random data in R.”
*The Quantitative Methods for Psychology*, **15**(1), 1--11.
doi:10.20982/tqmp.15.1.p001
.

```
# Simplest example using all the default arguments:
dta <- GRD()
head(dta)
#> id DV
#> 1 1 2.8051766
#> 2 2 -0.7002566
#> 3 3 -1.7011049
#> 4 4 -1.4484719
#> 5 5 1.2012080
#> 6 6 1.6314297
hist(dta$DV)
# Renaming the dependant variable and setting the group size:
dta <- GRD( RenameDV = "score", SubjectsPerGroup = 200 )
hist(dta$score )
# Examples for a between-subject design and for a within-subject design:
dta <- GRD( BSFactors = '3', SubjectsPerGroup = 20)
dta <- GRD( WSFactors = "Moment (2)", SubjectsPerGroup = 20)
# A complex, 3 x 2 x (2) mixed design with a variable amount of participants in the 6 groups:
dta <- GRD(BSFactors = "difficulty(3) : gender (2)",
WSFactors="day(2)",
SubjectsPerGroup=c(20,24,12,13,28,29)
)
# Defining population characteristics :
dta <- GRD(
RenameDV = "IQ",
SubjectsPerGroup = 20,
Population=list(
mean=100, # will set GM to 100
stddev=15 # will set STDDEV to 15
)
)
hist(dta$IQ)
# This example adds an effect along the "Difficulty" factor with a slope of 15
dta <- GRD(BSFactors="Difficulty(5)", SubjectsPerGroup = 100,
Population=list(mean=50,stddev=5),
Effects = list("Difficulty" = slope(15) ) )
# show the mean performance as a function of difficulty:
superb(DV ~ Difficulty, dta )
# An example in which the moments are correlated
dta <- GRD( BSFactors = "Difficulty(2)",WSFactors = "Moment (2)",
SubjectsPerGroup = 250,
Effects = list("Difficulty" = slope(3), "Moment" = slope(1) ),
Population=list(mean=50,stddev=20,rho=0.85)
)
# the mean plot on the raw data...
superb(cbind(DV.1,DV.2) ~ Difficulty, dta, WSFactors = "Moment(2)",
plotStyle="line",
adjustments = list (purpose="difference") )
# ... and the mean plot on the decorrelated data;
# because of high correlation, the error bars are markedly different
superb(cbind(DV.1,DV.2) ~ Difficulty, dta, WSFactors = "Moment(2)",
plotStyle="line",
adjustments = list (purpose="difference", decorrelation = "CM") )
```