plot MinSurrContCont: Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case

Description

This function provides a plot that displays the frequencies, percentages, or cumulative percentages of \(\rho_{min}^{2}\) for a fixed value of \(\delta\) (given the observed variances of the true endpoint in the control and experimental treatment conditions and a specified grid of values for the unidentified parameter \(\rho_{T_{0}T_{1}}\); see MinSurrContCont). For details, see the online appendix of Alonso et al., submitted.

Usage

# S3 method for MinSurrContCont
plot(x, main, col, Type="Percent", Labels=FALSE, 
Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), ...)

Arguments

x: An object of class MinSurrContCont. See MinSurrContCont.
main: The title of the plot.
col: The color of the bins.
Type: The type of plot that is produced. When Type=Freq or Type=Percent, the Y-axis shows frequencies or percentages of \(\rho_{min}^{2}\). When Type=CumPerc, the Y-axis shows cumulative percentages of \(\rho_{min}^{2}\). Default "Percent".
Labels: Logical. When Labels=TRUE, the percentage of \(\rho_{min}^{2}\) values that are equal to or larger than the midpoint value of each of the bins are displayed (on top of each bin). Only applies when Type=Freq or Type=Percent. Default FALSE.
Par: Graphical parameters for the plot. Default par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)).
...: Extra graphical parameters to be passed to hist().

Author

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., Van der Elst, W., Molenberghs, G., Buyse, M., & Burzykowski, T. (submitted). On the relationship between the causal inference and meta-analytic paradigms for the validation of surrogate markers.

Examples

Run this code

# compute rho^2_min in the setting where the variances of T in the control
# and experimental treatments equal 100 and 120, delta is fixed at 50,
# and the grid G={0, .01, ..., 1} is considered for the counterfactual 
# correlation rho_T0T1:
MinSurr <- MinSurrContCont(T0T0 = 100, T1T1 = 120, Delta = 50,
T0T1 = seq(0, 1, by = 0.01))

# Plot the results (use percentages on Y-axis)
plot(MinSurr, Type="Percent")

# Same plot, but add the percentages of ICA values that are equal to or 
# larger than the midpoint values of the bins
plot(MinSurr, Labels=TRUE)