Description Usage Arguments Value Note References Examples

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 \insertCitech19;textualsuperb.

1 2 3 4 5 6 7 8 9 10 11 | ```
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`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ```
# Simplest example using all the default arguments:
dta <- GRD()
head(dta)
hist(dta$DV)
# Renaming the dependant variable and setting the group size:
dta <- GRD( RenameDV = "score", SubjectsPerGroup = 1000 )
hist(dta$score )
# Examples for a between-subject design and for a within-subject design:
dta <- GRD( BSFactors = '3')
dta <- GRD( WSFactors = "Moment (2)")
# 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",
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:
superbPlot(dta, BSFactors = "Difficulty", variables="DV")
# An example in which the moments are correlated
dta <- GRD( BSFactors = "Difficulty(2)",WSFactors = "Moment (2)",
SubjectsPerGroup = 1000,
Effects = list("Difficulty" = slope(3), "Moment" = slope(1) ),
Population=list(mean=50,stddev=20,rho=0.85)
)
# the mean plot on the raw data...
superbPlot(dta, BSFactors = "Difficulty", WSFactors = "Moment(2)",
variables=c("DV.1","DV.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
superbPlot(dta, BSFactors = "Difficulty", WSFactors = "Moment(2)",
variables=c("DV.1","DV.2"), plotStyle="line",
adjustments = list (purpose="difference", decorrelation = "CM") )
``` |

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