mv.plots.SM {CLAST} | R Documentation |
Plots mean value of upper limit, lower limit and interval width for four different ranking methods. This function is basically a wrapper for mv.plot.
mv.plots.SM(n, a, b, type = "interval",
B = 100, offset = TRUE, plt = c(1, 1, 1), p0 = NULL, p1 = NULL, focus = FALSE)
n |
Design vector of planned sample sizes |
a |
Design vector of lower futility boundaries |
b |
Design vector of upper superiority boundaries |
type |
Either "upper", "lower" or "interval" (default) |
B |
Integer controlling fineness of plot (default=100) |
offset |
if TRUE then ML mean value is subtracted |
plt |
Logical vector indicating output plots of upper, lower and interval (default=c(1,1,1)) |
p0 |
Lower (null) benchmark for success probability |
p1 |
Upper (alternative) benchmark for success probability |
focus |
Logical. If true, plots are restricted to p between p0 and p1. (default=FALSE) |
NULL
Chris J. Lloyd
Lloyd, C.J. (2021) Exact confidence limits after a group sequential single arm binary trial. Statistics in Medicine, Volume 38, 2389-2399. doi: 10.1002/sim.8909
# Figure 2 in Lloyd (2020)
n=c(5,6,5,9)
a=c(2,4,5,12)
b=c(5,9,11,13)
p0=.4
p1=.75
mv.plots.SM(n,a,b,p0=p0,p1=p1)
# Produces three panel graphic identical to Figure 2 in reference
mv.plots.SM(n,a,b,p0=p0,p1=p1,focus=TRUE)
# Produces alternative graphic focussed on relevant values of p.
# In both cases LR (in blue) appears best. CP can perform poorly
# for values of p outside the range of interest.