pplot.t.test {pplot} | R Documentation |
Generate p-plot from a series of data points
Description
Takes a series of data points and computes either a chronological or ordered p-plot for testing the mean of the data points against 0 with a one-sample t-test.
Usage
pplot.t.test(data,
sort = FALSE,
startindex = 2,
produce.plot = TRUE,
xtitle = "Observation",
plottype = 3,
alpha = 0.05,
alternative = "two.sided",
n.sig = TRUE,
n.sig.adjust = -1,
ylim.p = c(0,1),
ylim.es = -1,
conf.level = 0.95,
pcol.alpha = 125)
Arguments
data |
Vector of data points. |
sort |
Logical. |
startindex |
Number of observations to begin the p-plot with (minimum 2). |
produce.plot |
Logical whether to produce plot or not. Setting to |
xtitle |
Axis title for the x-axis of the p-plot. |
plottype |
1 = only p-plot, 2 = only effect size plot, 3 = both plots. |
alpha |
Significance threshold to be used for determining oversampling. |
alternative |
Argument passed on to the |
n.sig |
Logical: Show vertical line at last crossing of the significance threshold? |
n.sig.adjust |
-1 = auto, 0 = left-adjusted, 0.5 = centered, 1 = right. |
ylim.p |
Plot limits on the y-axis of the p-plot. Vector of min, max. |
ylim.es |
Plot limits on the y-axis of the effect size plot. Either a vector of min and max, or only one value to be used as minimum with maximum being determined as a function of the data. |
conf.level |
Confidence level for effect size CIs. |
pcol.alpha |
Transparency of the data points; set to 255 for solid fill. |
Details
pplot.t.test
generates a chronological or ordered p-plot from a vector of data points. It returns a vector of p-values, or a data frame containing p-values and effect size estimates.
Value
pplot.t.test()
returns either a vector of p-values, a data frame containing effect size estimates, or both (depending on the plottype
argument).
Author(s)
Roland Pfister
See Also
Examples
# Show p-plot for significant test (the simulation
# has a power of > 88% to return a significant
# effect).
testdata <- rnorm(64, mean = 0.4, sd = 1)
pplot.t.test(testdata)
# Show p-plot for non-significant test (simulation
# does not include a true effect).
testdata <- rnorm(64, mean = 0.0, sd = 1)
pplot.t.test(testdata)