Surrogate (version 3.2.5)

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, Histogram.ICA=TRUE, Mixmean=TRUE,
Mixvar=TRUE, Deviance=TRUE, 
Type="Percent", Labels=FALSE, ...)

Arguments

x

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

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 the different runs 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 the different runs be provided? Default Mixvar=TRUE.

Deviance

Logical. Should a box plot 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 \(R^2_{H}\). 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 added in the histogram of ICA (on top of each bin). Default FALSE.

...

Extra graphical parameters to be passed to hist().

Author

Wim Van der Elst, Paul Meyvisch, & Ariel Alonso

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

if (FALSE) # Time consuming code part
Fit <- ICA.BinCont(Dataset = Schizo, Surr = BPRS, True = PANSS_Bin, 
Treat=Treat, M=50, Seed=1)

summary(Fit)
plot(Fit)