plot Causal-Inference BinCont {Surrogate} | R Documentation |
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 class 'ICA.BinCont'
plot(x, Histogram.ICA=TRUE, Mixmean=TRUE,
Mixvar=TRUE, Deviance=TRUE,
Type="Percent", Labels=FALSE, ...)
Arguments
x |
An object of class |
Histogram.ICA |
Logical. Should a histogram of ICA be provided? Default |
Mixmean |
Logical. Should a plot of the calculated means of the fitted mixtures for |
Mixvar |
Logical. Should a plot of the calculated variances of the fitted mixtures for |
Deviance |
Logical. Should a box plot of the deviances for the fitted mixtures of |
Type |
The type of plot that is produced for the histogram of ICA. When |
.
Labels |
Logical. When |
... |
Extra graphical parameters to be passed to |
Author(s)
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
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)
## End(Not run)