plot.ICA.BinCont: Plot the individual causal association (ICA) in the causal-inference single-trial setting in the binary-continuous case.

Description

This function is used to a plot that displays the frequencies, percentages, cumulative percentages or densities of the individual causal association (ICA) in the single-trial setting within the causal-inference framework when the surrogate endpoint is continuous (normally distributed) and the true endpoint is a binary outcome. In addition, several plots to evaluate the goodness-of-fit of the mixture model used to fit the conditional distribution of potential outcomes on the surrogate endpoint can also be provided. For details, see Alonso Abad et al. (2023).

Usage

# S3 method for ICA.BinCont
plot(x, Histogram.ICA=TRUE, Mixmean=TRUE, Mixvar=TRUE, Deviance=TRUE,
                             Type="Percent", Labels=FALSE, ...)

Arguments

x: A fitted object of class ICA.BinCont. See ICA.BinCont or ICA.BinCont.BS.
Histogram.ICA: Logical. Should a histogram of ICA be provided? Default Histogram.ICA=TRUE.
Mixmean: Logical. Should a plot of the calculated means of the fitted mixtures for $S_{0}$ and $S_{1}$ across different iterations be provided? Default Mixmean=TRUE.
Mixvar: Logical. Should a plot of the calculated variances of the fitted mixtures for $S_{0}$ and $S_{1}$ across different iterations be provided? Default Mixvar=TRUE.
Deviance: Logical. Should a boxplot of the deviances for the fitted mixtures of $S_{0}$ and $S_{1}$ be provided? Default Deviance=TRUE.
Type: The type of plot that is produced for the histogram of ICA. When Type="Freq" or Type="Percent", the Y-axis shows frequencies or percentages of $R_{H}^{2}$ . When Type="CumPerc", the Y-axis shows cumulative percentages. When Type="Density", the density is shown.
Labels: Logical. When Labels=TRUE, the percentage of $R_{H}^{2}$ values that are equal to or larger than the midpoint value of each of the bins are added in the histogram of ICA (on top of each bin). Default Labels=FALSE.
...: Extra graphical parameters to be passed to plot() or hist().

Author

Wim Van der Elst, Fenny Ong, Ariel Alonso, and Geert Molenberghs

References

Alonso Abad, A., Ong, F., Stijven, F., Van der Elst, W., Molenberghs, G., Van Keilegom, I., Verbeke, G., & Callegaro, A. (2023). An information-theoretic approach for the assessment of a continuous outcome as a surrogate for a binary true endpoint based on causal inference: Application to vaccine evaluation.

Examples

Run this code

if (FALSE) # Time consuming code part
data(Schizo)
Fit <- ICA.BinCont.BS(Dataset = Schizo, Surr = BPRS, True = PANSS_Bin, nb = 10,
Theta.S_0=c(-10,-5,5,10,10,10,10,10), Theta.S_1=c(-10,-5,5,10,10,10,10,10),
Treat=Treat, M=50, Seed=1)

summary(Fit)
plot(Fit)

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