Surrogate (version 1.1)

plot.comb27.BinBin: Plots the distribution of prediction error functions in decreasing order of appearance.

Description

The function plot.comb27.BinBin plots each of the selected prediction functions in decreasing order in the single-trial causal-inference framework when both the surrogate and the true endpoints are binary outcomes. The distribution of frequencies at which each of the 27 possible predicton functions are selected provides additional insights regarding the association between \(S\) (\(\Delta_S\)) and \(T\) (\(\Delta_T\)).. See Details below.

Usage

# S3 method for comb27.BinBin
plot(x,lab,...)

Arguments

x

An object of class comb27.BinBin. See comb27.BinBin.

lab

a supplementary label to the graph.

...

Other arguments to be passed

Value

An object of class comb27.BinBin with components,

index

count variable

Monotonicity

The vector of Monotonicity assumptions

Pe

The vector of the prediction error values.

combo

The vector containing the codes for the each of the 27 prediction functions.

R2_H

The vector of the \(R_H^2\) values.

H_Delta_T

The vector of the entropies of \(\Delta_T\).

H_Delta_S

The vector of the entropies of \(\Delta_S\).

I_Delta_T_Delta_S

The vector of the mutual information of \(\Delta_S\) and \(\Delta_T\).

Details

Each of the 27 prediction functions is coded as x/y/z with x, y and z taking values in \({-1,0,1}\). As an example, the combination 0/0/0 represents the prediction function that projects every value of \(\Delta_S\) to 0. Similarly, the combination -1/0/1 is the identity function projecting every value of \(\Delta_S\) to the same value for \(\Delta_T\).

References

Alonso A, Van der Elst W, Molenberghs G, Buyse M and Burzykowski T. (2016). An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference.

Alonso A, Van der Elst W and Meyvisch P (2016). Assessing a surrogate predictive value: A causal inference approach.

See Also

comb27.BinBin

Examples

# NOT RUN {
 # time consuming code part
CIGTS_27 <- comb27.BinBin(pi1_1_ = 0.3412, pi1_0_ = 0.2539, pi0_1_ = 0.119, 
                       pi_1_1 = 0.6863, pi_1_0 = 0.0882, pi_0_1 = 0.0784,  
                       Seed=1,Monotonicity=c("No"), M=500000) 
plot.comb27.BinBin(CIGTS_27,lab="CIGTS")
# }