Surrogate (version 1.1)

plot.Fano.BinBin: Plots the distribution of $R_{HL}^2$ either as a density or as function of ${\pi }_{10}$ in the setting where both $S$ and $T$ are binary endpoints

Description

The function plot.Fano.BinBin plots the distribution of $R_{HL}^2$ which is fully identifiable for given values of ${\pi }_{10}$. See Details below.

Usage

# S3 method for Fano.BinBin
plot(x,Type="Density",Xlab.R2_HL,main.R2_HL,
ylab="density",Par=par(mfrow=c(1,1),oma=c(0,0,0,0),mar=c(5.1,4.1,4.1,2.1)),
Cex.Legend=1,Cex.Position="top", lwd=3,linety=c(5,6,7),color=c(8,9,3),...)

Arguments

x

An object of class Fano.BinBin. See Fano.BinBin.

Type

The type of plot that is produced. When Type="Freq", a histogram of $R_{HL}^2$ is produced. When Type="Density", the density of $R_{HL}^2$ is produced. When Type="Scatter", a scatter plot of $R_{HL}^2$ is produced as a function of ${\pi }_{10}$. Default Type="Scatter".

Xlab.R2_HL

The label of the X-axis when density plots or histograms are produced.

main.R2_HL

Title of the density plot or histogram.

ylab

The label of the Y-axis when density plots or histograms are produced. Default ylab="density".

Par

Graphical parameters for the plot. Default par(mfrow=c(1,1),oma=c(0,0,0,0),mar=c(5.1,4.1,4.1,2.1)).

Cex.Legend

The size of the legend. Default Cex.Legend=1.

Cex.Position

The position of the legend. Default Cex.Position="top".

lwd

The line width for the density plot . Default lwd=3.

linety

The line types corresponding to each level of fano_delta . Default linety=c(5,6,7).

color

The color corresponding to each level of fano_delta . Default color=c(8,9,3).

...

Other arguments to be passed.

Value

An object of class Fano.BinBin with components,

R2_HL

The sampled values for $R_{HL}^2$.

H_Delta_T

The sampled values for $H\mathrm{\Delta }T$.

minpi10

The minimum value for ${\pi }_{10}$.

maxpi10

The maximum value for ${\pi }_{10}$.

samplepi10

The sampled value for ${\pi }_{10}$.

delta

The specified vector of upper bounds for the prediction errors.

uncertainty

Indexes the sampling of $pi1\mathrm{\_}$.

pi_00

The sampled values for ${\pi }_{00}$.

pi_11

The sampled values for ${\pi }_{11}$.

pi_01

The sampled values for ${\pi }_{01}$.

pi_10

The sampled values for ${\pi }_{10}$.

Details

Values for ${\pi }_{10}$ have to be uniformly sampled from the interval $[0,min({\pi }_{1\cdot },{\pi }_{\cdot 0})]$. Any sampled value for ${\pi }_{10}$ will fully determine the bivariate distribution of potential outcomes for the true endpoint.

The vector $\text{bold}{\pi }_{km}$ fully determines $R_{HL}^2$.

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (2014). Validation of surrogate endpoints: the binary-binary setting from a causal inference perspective.

See Also

Fano.BinBin

Examples

# NOT RUN {
# Conduct the analysis assuming no montonicity
# for the true endpoint, using a range of
# upper bounds for prediction errors 
FANO<-Fano.BinBin(pi1_ = 0.5951 ,  pi_1 = 0.7745, 
fano_delta=c(0.05, 0.1, 0.2), M=1000)

plot(FANO, Type="Scatter",color=c(3,4,5),Cex.Position="bottom")
# }