Produces a plot that provides a graphical representation of triallevel surrogacy based on the output of the TrialLevelIT()
function (informationtheoretic framework).
1 2 3 4 
x 
An object of class 
Xlab.Trial 
The legend of the Xaxis of the plot that depicts triallevel surrogacy. Default "Treatment effect on the surrogate endpoint (α_{i})". 
Ylab.Trial 
The legend of the Yaxis of the plot that depicts triallevel surrogacy. Default "Treatment effect on the true endpoint (β_{i})". 
Main.Trial 
The title of the plot that depicts triallevel surrogacy. Default "Triallevel surrogacy". 
Par 
Graphical parameters for the plot. Default 
... 
Extra graphical parameters to be passed to 
Wim Van der Elst, Ariel Alonso, & Geert Molenberghs
Buyse, M., Molenberghs, G., Burzykowski, T., Renard, D., & Geys, H. (2000). The validation of surrogate endpoints in metaanalysis of randomized experiments. Biostatistics, 1, 4967.
UnifixedContCont, BifixedContCont, UnifixedContCont, BimixedContCont, TrialLevelIT
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # Generate vector treatment effects on S
set.seed(seed = 1)
Alpha.Vector < seq(from = 5, to = 10, by=.1) + runif(min = .5, max = .5, n = 51)
# Generate vector treatment effects on T
set.seed(seed=2)
Beta.Vector < (Alpha.Vector * 3) + runif(min = 5, max = 5, n = 51)
# Apply the function to estimate R^2_{h.t}
Fit < TrialLevelIT(Alpha.Vector=Alpha.Vector,
Beta.Vector=Beta.Vector, N.Trial=50, Model="Reduced")
# Plot the results
plot(Fit)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.