superb (version 0.9.4.2)

GRD: GRD

Description

Generate Random Data (GRD) is a function that 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),
  Debug = FALSE,
  Summary = FALSE
)

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 ":"

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)

Debug

a boolean to produced detailed debugging information (default FALSE); useful for the maintainer only really

Summary

a boolean printing a summary of the design (default FALSE)

Value

a data.frame with the simulated scores.

References

Examples

# NOT RUN {
 # 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: 
 dtaBS <- GRD( BSFactors = '3')
 dtaWS <- 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 = 1000,
     Population=list(mean=50,stddev=5), 
     Effects = list("Difficulty" = slope(15) )  )
 # show the mean performance as a function of difficulty:
 superbPlot(dta, BSFactor = "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, BSFactor = "Difficulty", WSFactor = "Moment(2)", 
     variables=c("DV.1","DV.2"), plotStyle="line",
     adjustments = list (purpose="difference") )
 # the mean plot on the decorrelated data; 
 # because of correlation, the error bars are markedly different
 superbPlot(dta, BSFactor = "Difficulty", WSFactor = "Moment(2)", 
     variables=c("DV.1","DV.2"), plotStyle="line",
     adjustments = list (purpose="difference", decorrelation = "CM") )
 



# }