Surrogate (version 0.6)

plot Causal-Inference BinCont: Plots the (Meta-Analytic) Individual Causal Association and related metrics when S is continuous and T is binary

Description

This function provides a plot that displays the frequencies, percentages, cumulative percentages or densities of the individual causal association (ICA; \(R^2_{H}\)) in the setting where S is continuous and T is binary.

Usage

# S3 method for ICA.BinCont
plot(x, Xlab, Main=" ", Type="Percent", Labels=FALSE, …)

Arguments

x

An object of class ICA.BinCont. See ICA.BinCont.

Xlab

The legend of the X-axis of the plot.

Main

The title of the plot.

Type

The type of plot that is produced. When Type="Freq" or Type="Percent", the Y-axis shows frequencies or percentages of \(R^2_{H}\). When Type="CumPerc", the Y-axis shows cumulative percentages. When Type="Density", the density is shown

Labels

Logical. When Labels=TRUE, the percentage of \(R^2_{H}\) 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.

…

Extra graphical parameters to be passed to hist().

References

Alonso, A., Van der Elst, W., & Meyvisch, P. (2016). Surrogate markers validation: the continuous-binary setting from a causal inference perspective.

See Also

ICA.BinCont

Examples

# NOT RUN {
# Time consuming code part
Fit <- ICA.BinCont(Dataset = Schizo, Surr = BPRS, True = PANSS_Bin, 
Treat=Treat, M=50, Seed=1)

summary(Fit)
plot(Fit)
# }