plot MaxEntICA BinBin {Surrogate}

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)