dmcObservedData | R Documentation |
Basic analysis to create data object required for observed data. Example raw *.txt files are flankerData.txt and simonData.txt. There are four critical columns:
column containing subject number
column coding for compatible or incompatible
column with RT (in ms)
column indicating of the response was correct
dmcObservedData(
dat,
nCAF = 5,
nDelta = 19,
pDelta = vector(),
tDelta = 1,
outlier = c(200, 1200),
columns = c("Subject", "Comp", "RT", "Error"),
compCoding = c("comp", "incomp"),
errorCoding = c(0, 1),
quantileType = 5,
deltaErrors = FALSE,
keepRaw = FALSE,
delim = "\t",
skip = 0
)
dat |
A text file(s) containing the observed data or an R DataFrame (see createDF/addDataDF) |
nCAF |
The number of CAF bins. |
nDelta |
The number of delta bins. |
pDelta |
An alternative option to nDelta (tDelta = 1 only) by directly specifying required percentile values (vector of values 0-100) |
tDelta |
The type of delta calculation (1=direct percentiles points, 2=percentile bounds (tile) averaging) |
outlier |
Outlier limits in ms (e.g., c(200, 1200)) |
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)) |
quantileType |
Argument (1-9) from R function quantile specifying the algorithm (?quantile) |
deltaErrors |
TRUE/FALSE Calculate RT delta for error trials. |
keepRaw |
TRUE/FALSE |
delim |
Single character used to separate fields within a record if reading from external text file. |
skip |
The number of lines to skip before reading data if reading from external text file. |
dmcObservedData returns an object of class "dmcob" with the following components:
summarySubject |
DataFrame within individual subject data (rtCor, perErr, rtErr) for compatibility condition |
summary |
DataFrame within aggregated subject data (rtCor, sdRtCor, seRtCor, perErr, sdPerErr, sePerErr, rtErr, sdRtErr, seRtErr) for compatibility condition |
cafSubject |
DataFrame within individual subject conditional accuracy function (CAF) data (Bin, accPerComp, accPerIncomp, meanEffect) |
caf |
DataFrame within aggregated subject conditional accuracy function (CAF) data (Bin, accPerComp, accPerIncomp, meanEffect, sdEffect, seEffect) |
deltaSubject |
DataFrame within individual subject distributional delta analysis data correct trials (Bin, meanComp, meanIncomp, meanBin, meanEffect) |
delta |
DataFrame within aggregated subject distributional delta analysis data correct trials (Bin, meanComp, meanIncomp, meanBin, meanEffect, sdEffect, seEffect) |
deltaErrorsSubject |
Optional: DataFrame within individual subject distributional delta analysis data incorrect trials (Bin, meanComp, meanIncomp, meanBin, meanEffect) |
deltaErrors |
Optional: DataFrame within aggregated subject distributional delta analysis data incorrect trials (Bin, meanComp, meanIncomp, meanBin, meanEffect, sdEffect, seEffect) |
# Example 1
plot(flankerData) # flanker data from Ulrich et al. (2015)
plot(simonData) # simon data from Ulrich et al. (2015)
# Example 2 (Basic behavioural analysis from Ulrich et al. )
flankerDat <- cbind(Task = "flanker", flankerData$summarySubject)
simonDat <- cbind(Task = "simon", simonData$summarySubject)
datAgg <- rbind(flankerDat, simonDat)
datAgg$Subject <- factor(datAgg$Subject)
datAgg$Task <- factor(datAgg$Task)
datAgg$Comp <- factor(datAgg$Comp)
aovErr <- aov(perErr ~ Comp*Task + Error(Subject/(Comp*Task)), datAgg)
summary(aovErr)
model.tables(aovErr, type = "mean")
aovRt <- aov(rtCor ~ Comp*Task + Error(Subject/(Comp*Task)), datAgg)
summary(aovRt)
model.tables(aovRt, type = "mean")
# Example 3
dat <- createDF(nSubjects = 50, nTrl = 500, design = list("Comp" = c("comp", "incomp")))
dat <- addDataDF(dat,
RT = list("Comp_comp" = c(500, 75, 120),
"Comp_incomp" = c(530, 75, 100)),
Error = list("Comp_comp" = c(3, 2, 2, 1, 1),
"Comp_incomp" = c(21, 3, 2, 1, 1)))
datOb <- dmcObservedData(dat)
plot(datOb)
plot(datOb, subject = 1)
# Example 4
dat <- createDF(nSubjects = 50, nTrl = 500, design = list("Congruency" = c("cong", "incong")))
dat <- addDataDF(dat,
RT = list("Congruency_cong" = c(500, 75, 100),
"Congruency_incong" = c(530, 100, 110)),
Error = list("Congruency_cong" = c(3, 2, 2, 1, 1),
"Congruency_incong" = c(21, 3, 2, 1, 1)))
datOb <- dmcObservedData(dat, nCAF = 5, nDelta = 9,
columns = c("Subject", "Congruency", "RT", "Error"),
compCoding = c("cong", "incong"))
plot(datOb, labels = c("Congruent", "Incongruent"))
plot(datOb, subject = 1)
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