superb (version 0.95.19)

GRD: Generate random data

Description

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 ch19;textualsuperb.

Usage

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)
)

Value

a data.frame with the simulated scores.

Arguments

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. The effects can be given with a list of "factorname" = effect_specification or "factorname1*factorname2" = effect_specification pairs, in which effect_specification can either be slope(), extent(), custom() and Rexpression(). For slope and extent, provide a range, for custom, indicate the deviation from the grand mean for each cell, finally, for Rexpression, give between quote any R commands which returns the deviation from the grand mean, using the factors. See the last example below.

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)

References

Examples

 # 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 = 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") )
 
 # This example creates a dataset in a 3 x 2 design. It has various effects,
 # one effect of difficulty, with an overall effect of 10 more (+3.33 per level),
 # one effect of gender, whose slope is 10 points (+10 points for each additional gender),
 # and finally one interacting effect, which is 0 for the last three cells of the design:
 GRD(
     SubjectsPerGroup = 10,
     BSFactors  = c("difficulty(3)","gender(2)"), 
     Population = list(mean=100,stddev=15), 
     Effects    = list(
         "difficulty" = extent(10),
         "gender"=slope(10),
         "difficulty*gender"=custom(-300,+200,-100,0,0,0) 
     ) 
 )