plot.Fano.BinBin: Plots the distribution of R^2_{HL} either as a density or as...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

plot.Fano.BinBin

R Documentation

Plots the distribution of `R^2_{HL}` 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^2_{HL} which is fully identifiable for given values of \pi_{10}. See Details below.

Usage

## S3 method for class '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^2_{HL}` is produced. When `Type="Density"`, the density of `R^2_{HL}` is produced. When `Type="Scatter"`, a scatter plot of `R^2_{HL}` 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.

Details

Values for \pi_{10} have to be uniformly sampled from the interval [0,\min(\pi_{1\cdot},\pi_{\cdot0})]. Any sampled value for \pi_{10} will fully determine the bivariate distribution of potential outcomes for the true endpoint.

The vector \bold{\pi_{km}} fully determines R^2_{HL}.

Value

An object of class Fano.BinBin with components,

`R2_HL`	The sampled values for `R^2_{HL}`.
`H_Delta_T`	The sampled values for `H{\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\_`.
`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}`.

Author(s)

Paul Meyvisch, Wim Van der Elst, Ariel Alonso

References

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

Examples

# 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")

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