calculateCAF {DMCfun} R Documentation

calculateCAF

Description

Calculate conditional accuracy function (CAF). The DataFrame should contain columns defining the participant, compatibility condition, RT and error (Default column names: "Subject", "Comp", "RT", "Error"). The "Comp" column should define compatibility condition (Default: c("comp", "incomp")) and the "Error" column should define if the trial was an error or not (Default: c(0, 1) ).

Usage

calculateCAF(
dat,
nCAF = 5,
columns = c("Subject", "Comp", "RT", "Error"),
compCoding = c("comp", "incomp"),
errorCoding = c(0, 1)
)


Arguments

 dat DataFrame with columns containing the participant number, condition compatibility, RT data (in ms) and an Error column. nCAF Number of CAF bins. columns Name of required columns Default: c("Subject", "Comp", "RT", "Error") compCoding Coding for compatibility Default: c("comp", "incomp") errorCoding Coding for errors Default: c(0, 1))

Value

calculateCAF returns a DataFrame with conditional accuracy function (CAF) data (Bin, comp, incomp, effect)

Examples

# Example 1
dat <- createDF(nSubjects = 1, nTrl = 10000, design = list("Comp" = c("comp", "incomp")))
RT = list("Comp_comp"   = c(500, 80, 100),
"Comp_incomp" = c(600, 80, 140)),
Error = list("Comp_comp"   = c( 5, 4, 3, 2, 1),
"Comp_incomp" = c(20, 8, 6, 4, 2)))
caf <- calculateCAF(dat)

# Example 2
dat <- createDF(nSubjects = 1, nTrl = 10000, design = list("Congruency" = c("cong", "incong")))