pplot (version 0.9)

pplot.pvals: Generate p-plot from a series of p-values

Description

Takes a series of p-values and plots them against the corresponding number of observations included in a test. Adds reference lines at the chosen alpha level (horizontal line) and at the latest point in time the p-plot crosses the significance threshold (if any).

Usage

pplot.pvals(xs,
            ps,
            xtitle = "Observation",
            alpha = 0.05,
            n.sig = TRUE,
            ylim.p = c(0,1),
            pcol.alpha = 125)

Value

pplot.pvals() returns a single integer with the number of oversampled datasets (-1 for a non-significant test at the full sample size).

Arguments

xs

Vector of observations that the p values are computed on.

ps

vector of p-values.

xtitle

Axis title for the x-axis of the p-plot.

alpha

Significance threshold to be used for determining oversampling.

n.sig

Logical: Show vertical line at last crossing of the significance threshold?

ylim.p

Plot limits on the y-axis.

pcol.alpha

Transparency of the data points; set to 255 for solid fill.

Author

Roland Pfister

Details

pplot.pvals generates a p-plot from two vectors: xs containing the sample sizes underlying the test results, and ps as the resulting p-values.

See Also

pplot.t.test;

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)
ps <- matrix(data = NA, nrow = 60, ncol = 1)
for (itest in c(2:64)) {
  ps[itest] <- t.test(testdata[1:itest])$p.value
}
xs <- c(1:64)
pplot.pvals(xs,ps)

# Show p-plot for non-significant test (simulation 
# does not include a true effect).
testdata <- rnorm(64, mean = 0.0, sd = 1)
ps <- matrix(data = NA, nrow = 60, ncol = 1)
for (itest in c(2:64)) {
  ps[itest] <- t.test(testdata[1:itest])$p.value
}
xs <- c(1:64)
pplot.pvals(xs,ps)