Nothing
# Let's use an 8-dose design, and a somewhat asymmetric CDF
exampleF = pweibull(1:8, shape = 2, scale = 4)
# You can plot if you want: plot(exampleF)
# Here's how the transition matrix looks for the median-finding classic up-and-down
round(classicmat(exampleF), 2)
# Note how the only nonzero diagonals are at the opposite corners. That's how
# odd-n and even-n distributions communicate (see examples for vector functions).
# Also note how "up" probabilities (the 1st upper off-diagnoal) are decreasing,
# while "down" probabilities (1st lower off-diagonal) are increasing, and
# start exceeding "up" moves at row 4.
# Now, let's use the same F to target the 90th percentile, which is often
# the goal of anesthesiology dose-finding studies.
# We use the biased-coin design (BCD) presented by Durham and Flournoy (1994):
round(bcdmat(exampleF, target = 0.9), 2)
# Note that now there's plenty of probability mass on the diagonal (i.e., repeating same dose).
# Another option, actually with somewhat better operational characteristics,
# is "k-in-a-row". Let's see what k to use:
ktargOptions(.9, tolerance = 0.05)
# Even though nominally k=7's target is closest to 0.9, it's generally preferable
# to choose a somewhat smaller k. So let's go with k=6.
# We must also specify whether this is a low (<0.5) or high target.
round(kmatMarg(exampleF, k = 6, lowTarget = FALSE), 2)
# Compare and contrast with the BCD matrix above! At what dose do the "up" and "down"
# probabilities flip?
# Lastly, if you want to see a 43 x 43 matrix - the full state matrix for k-in-a-row,
# run the following line:
\donttest{
round(kmatFull(exampleF, k = 6, lowTarget = FALSE), 2)
}
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