plot.MaxEntICABinBin: Plots the sensitivity-based and maximum entropy based...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are binary outcomes

Description

This function provides a plot that displays the frequencies or densities of the individual causal association (ICA; R^2_{H}) as identified based on the sensitivity- (using the functions ICA.BinBin, ICA.BinBin.Grid.Sample, or ICA.BinBin.Grid.Full) and maximum entropy-based (using the function MaxEntICABinBin) approaches.

Usage

## S3 method for class 'MaxEntICA.BinBin'
plot(x, ICA.Fit, 
Type="Density", Xlab, col, Main, ...)

Arguments

x	An object of class MaxEntICABinBin. See MaxEntICABinBin.
ICA.Fit	An object of class ICA.BinBin. See ICA.BinBin.
Type	The type of plot that is produced. When Type="Freq", the Y-axis shows frequencies of R^2_{H}. When Type="Density", the density is shown.
Xlab	The legend of the X-axis of the plot.
col	The color of the bins (frequeny plot) or line (density plot). Default col <- c(8).
Main	The title of the plot.
...	Other arguments to be passed to plot()

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., & Van der Elst, W. (2015). A maximum-entropy approach for the evluation of surrogate endpoints based on causal inference.

See Also

ICA.BinBin, MaxEntICABinBin

Examples

# Sensitivity-based ICA results using ICA.BinBin.Grid.Sample
ICA <- ICA.BinBin.Grid.Sample(pi1_1_=0.341, pi0_1_=0.119, pi1_0_=0.254,
pi_1_1=0.686, pi_1_0=0.088, pi_0_1=0.078, Seed=1, 
Monotonicity=c("No"), M=5000)

# Maximum-entropy based ICA
MaxEnt <- MaxEntICABinBin(pi1_1_=0.341, pi0_1_=0.119, pi1_0_=0.254,
pi_1_1=0.686, pi_1_0=0.088, pi_0_1=0.078)

# Plot results
plot(x=MaxEnt, ICA.Fit=ICA)

Surrogate documentation built on June 22, 2024, 9:16 a.m.