plot.ICABinBin: Plots the (Meta-Analytic) Individual Causal Association and...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

plot Causal-Inference BinBin

R Documentation

Plots the (Meta-Analytic) Individual Causal Association and related metrics when S and T are binary outcomes

Description

This function provides a plot that displays the frequencies, percentages, cumulative percentages or densities of the individual causal association (ICA; R^2_{H} or R_{H}), and/or the odds ratios for S and T (\theta_{S} and \theta_{T}).

Usage

## S3 method for class 'ICA.BinBin'
plot(x, R2_H=TRUE, R_H=FALSE, Theta_T=FALSE, 
Theta_S=FALSE, Type="Density", Labels=FALSE, Xlab.R2_H, 
Main.R2_H, Xlab.R_H, Main.R_H, Xlab.Theta_S, Main.Theta_S, Xlab.Theta_T, 
Main.Theta_T, Cex.Legend=1, Cex.Position="topright",  
col, Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), ylim, ...)

Arguments

`x`	An object of class `ICA.BinBin`. See `ICA.BinBin`.
`R2_H`	Logical. When `R2_H=TRUE`, a plot of the `R^2_{H}` is provided. Default `TRUE`.
`R_H`	Logical. When `R_H=TRUE`, a plot of the `R_{H}` is provided. Default `FALSE`.
`Theta_T`	Logical. When `Theta_T=TRUE`, a plot of the `\theta_{T}` is provided. Default `FALSE`.
`Theta_S`	Logical. When `Theta_S=TRUE`, a plot of the `\theta_{S}` is provided. Default `FALSE`.
`Type`	The type of plot that is produced. When `Type="Freq"` or `Type="Percent"`, the Y-axis shows frequencies or percentages of `R^2_{H}`, `R_{H}`, `\theta_{T}`, or `\theta_{S}`. When `Type="CumPerc"`, the Y-axis shows cumulative percentages. When `Type="Density"`, the density is shown. When the fitted object of class `ICA.BinBin` was obtained using a general analysis (i.e., using the `Monotonicity=c("General")` argument in the function call), sperate plots are provided for the different monotonicity scenarios. Default `"Density"`.
`Labels`	Logical. When `Labels=TRUE`, the percentage of `R^2_{H}`, `R_{H}`, `\theta_{T}`, or `\theta_{S}` values that are equal to or larger than the midpoint value of each of the bins are displayed (on top of each bin). Default `FALSE`.
`Xlab.R2_H`	The legend of the X-axis of the `R^2_{H}` plot.
`Main.R2_H`	The title of the `R^2_{H}` plot.
`Xlab.R_H`	The legend of the X-axis of the `R_{H}` plot.
`Main.R_H`	The title of the `R_{H}` plot.
`Xlab.Theta_S`	The legend of the X-axis of the `\theta_{S}` plot.
`Main.Theta_S`	The title of the `\theta_{S}` plot.
`Xlab.Theta_T`	The legend of the X-axis of the `\theta_{T}` plot.
`Main.Theta_T`	The title of the `\theta_{T}` plot.
`Cex.Legend`	The size of the legend when `Type="All.Densities"` is used. Default `Cex.Legend=1`.
`Cex.Position`	The position of the legend, `Cex.Position="topright"` or `Cex.Position="topleft"`. Default `Cex.Position="topright"`.
`col`	The color of the bins. Default `col <- c(8)`.
`Par`	Graphical parameters for the plot. Default `par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1))`.
`ylim`	The (min, max) values for the Y-axis

...

Extra graphical parameters to be passed to hist().

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., Van der Elst, W., Molenberghs, G., Buyse, M., & Burzykowski, T. (submitted). A causal-inference approach for the validation of surrogate endpoints based on information theory and sensitivity analysis.

Examples

# Compute R2_H given the marginals, 
# assuming monotonicity for S and T and grids
# pi_0111=seq(0, 1, by=.001) and 
# pi_1100=seq(0, 1, by=.001)
ICA <- ICA.BinBin.Grid.Sample(pi1_1_=0.261, pi1_0_=0.285, 
pi_1_1=0.637, pi_1_0=0.078, pi0_1_=0.134, pi_0_1=0.127,  
Monotonicity=c("General"), M=2500, Seed=1)
           
# Plot the results (density of R2_H):
plot(ICA, Type="Density", R2_H=TRUE, R_H=FALSE, 
Theta_T=FALSE, Theta_S=FALSE)

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