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\name{add.ps}
\alias{add.ps}
\title{
add p-values from t-tests
}
\description{
Adds p-values comparing the different cells at each x-axis position with a
reference cell. Uses a syntax similar to \samp{raw.means.plot2}.
}
\usage{
add.ps(data, col.id, col.offset, col.x, col.value, fun.aggregate = "mean",
ref.offset = 1, prefixes,alternative = c("two.sided", "less", "greater"),
mu = 0, paired = FALSE, var.equal = FALSE, lty = 0, ...)
}
\arguments{
\item{data}{
A \samp{data.frame}
}
\item{col.id}{
\samp{character} vector specifying the id column.
}
\item{col.offset}{
\samp{character} vector specifying the offset column.
}
\item{col.x}{
\samp{character} vector specifying the x-axis column.
}
\item{col.value}{
\samp{character} vector specifying the data column.
}
\item{fun.aggregate}{
Function or function name used for aggregating the results. Default is
\samp{"mean"}.
}
\item{ref.offset}{
Scalar \samp{numeric} indicating the reference level to be tested against.
The default is 1 corresponding to \samp{levels(factor(d[,col.offset]))[1]}.
}
\item{prefixes}{
\samp{character} vector of the indices for the p-values. If missing
corresponds to \samp{levels(factor(d.new[,col.offset]))[-ref.offset]}.
}
\item{alternative}{
same as in \link{t.test}
}
\item{mu}{
same as in \link{t.test}
}
\item{paired}{
same as in \link{t.test}
}
\item{var.equal}{
same as in \link{t.test}
}
\item{lty}{
line type of axis, Default is 0 (i.e., no line).
}
\item{\dots}{
further arguments passed to axis.
}
}
\details{
This function computes t-tests comparing the values at each x-axis position
for each condition against the reference condition at and adds the p-values
to the axis.
This functions uses the same syntax as \link{raw.means.plot2} and
should be used in addition to it. Note that values are ordered according to
the \samp{col.id} so \samp{paired = TRUE} should be fine.
}
\value{
axis is plotted.
}
\author{
Henrik Singmann
}
\seealso{
\link{raw.means.plot} as the accompanying main functions.
}
\examples{
\dontrun{
#The examples uses the OBrienKaiser dataset from car and needs reshape.
# This extends the examples from raw.means.plot
require(reshape)
require(car)
data(OBrienKaiser)
OBKnew <- cbind(factor(1:nrow(OBrienKaiser)), OBrienKaiser)
colnames(OBKnew)[1] <- "id"
OBK.long <- melt(OBKnew)
OBK.long[, c("measurement", "time")] <-
t(vapply(strsplit(as.character(OBK.long$variable), "\\\."), "[", c("", "")))
# For this example the position at each x-axis are within-subject comparisons!
raw.means.plot2(OBK.long, "id", "measurement", "gender", "value")
add.ps(OBK.long, "id", "measurement", "gender", "value", paired = TRUE)
#reference is "fup"
raw.means.plot2(OBK.long, "id", "measurement", "gender", "value")
add.ps(OBK.long, "id", "measurement", "gender", "value", ref.offset = 2,
paired = TRUE) #reference is "post"
# Use R's standard (i.e., Welch test)
raw.means.plot2(OBK.long, "id", "treatment", "gender", "value")
add.ps(OBK.long, "id", "treatment", "gender", "value",
prefixes = c("p(control vs. A)", "p(control vs. B)"))
# Use standard t-test
raw.means.plot2(OBK.long, "id", "treatment", "gender", "value")
add.ps(OBK.long, "id", "treatment", "gender", "value", var.equal = TRUE,
prefixes = c("p(control vs. A)", "p(control vs. B)"))
}
}
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