View source: R/plot.TrialLevelIT.R
plot TrialLevelIT | R Documentation |
TrialLevelIT()
function
Produces a plot that provides a graphical representation of trial-level surrogacy based on the output of the TrialLevelIT()
function (information-theoretic framework).
## S3 method for class 'TrialLevelIT'
plot(x, Xlab.Trial,
Ylab.Trial, Main.Trial, Par=par(oma=c(0, 0, 0, 0),
mar=c(5.1, 4.1, 4.1, 2.1)), ...)
x |
An object of class |
Xlab.Trial |
The legend of the X-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the surrogate endpoint ( |
Ylab.Trial |
The legend of the Y-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the true endpoint ( |
Main.Trial |
The title of the plot that depicts trial-level surrogacy. Default "Trial-level 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 meta-analysis of randomized experiments. Biostatistics, 1, 49-67.
UnifixedContCont, BifixedContCont, UnifixedContCont, BimixedContCont, TrialLevelIT
# 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)
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