plot MaxEntSPF BinBin: Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes.

Description

Plots the sensitivity-based (Alonso et al., 2015a) and maximum entropy based (Alonso et al., 2015b) surrogate predictive function (SPF), i.e., \(r(i,j)=P(\Delta T=i|\Delta S=j)\), in the setting where both \(S\) and \(T\) are binary endpoints. For example, \(r(-1,1)\) quantifies the probability that the treatment has a negative effect on the true endpoint (\(\Delta T=-1\)) given that it has a positive effect on the surrogate (\(\Delta S=1\)).

Usage

# S3 method for MaxEntSPF.BinBin
plot(x, SPF.Fit, Type="All.Histograms", Col="grey", ...)

Arguments

x: A fitted object of class MaxEntSPF.BinBin. See MaxEntSPFBinBin.
SPF.Fit: A fitted object of class SPF.BinBin. See SPF.BinBin.
Type: The type of plot that is requested. Possible choices are: Type="All.Histograms", the histograms of all \(9\) \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors arranged in a \(3\) by \(3\) grid; Type="All.Densities", plots of densities of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors. Default Type="All.Densities".
Col: The color of the bins or lines when histograms or density plots are requested. Default "grey".
...: Other arguments to be passed to the plot() function.

Author

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (2015a). Assessing a surrogate effect predictive value in a causal inference framework.

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

Examples

Run this code

# 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)

# Sensitivity-based SPF
SPFSens <- SPF.BinBin(ICA)

# Maximum-entropy based SPF
SPFMaxEnt <- MaxEntSPFBinBin(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=SPFMaxEnt, SPF.Fit=SPFSens)