CausalDiagramContCont: Draws a causal diagram depicting the median correlations between the counterfactuals for a specified range of values of ICA or MICA in the continuous-continuous setting

Description

This function provides a diagram that depicts the medians of the correlations between the counterfactuals for a specified range of values of the individual causal association (ICA; $ρ_{Δ}$ ) or the meta-analytic individual causal association (MICA; $ρ_{M}$ ).

Usage

CausalDiagramContCont(x, Min=-1, Max=1, Cex.Letters=3, Cex.Corrs=2, 
Lines.Rel.Width=TRUE, Col.Pos.Neg=TRUE, Histograms.Counterfactuals=FALSE)

Arguments

x: An object of class ICA.ContCont or MICA.ContCont. See ICA.ContCont or MICA.ContCont.
Min: The minimum values of (M)ICA that should be considered. Default= $- 1$ .
Max: The maximum values of (M)ICA that should be considered. Default= $1$ .
Cex.Letters: The size of the symbols for the counterfactuals ( $S_{0}$ , $S_{1}$ ), $T_{0}$ , $T_{1}$ ). Default= $3$ .
Cex.Corrs: The size of the text depicting the median correlations between the counterfactuals. Default= $2$ .
Lines.Rel.Width: Logical. When Lines.Rel.Width=TRUE, the widths of the lines that represent the correlations between the counterfactuals are relative to the size of the correlations (i.e., a smaller line is used for correlations closer to zero whereas a thicker line is used for (absolute) correlations closer to $1$ ). When Lines.Rel.Width=FALSE, the width of all lines representing the correlations between the counterfactuals is identical. Default=TRUE.
Col.Pos.Neg: Logical. When Col.Pos.Neg=TRUE, the color of the lines that represent the correlations between the counterfactuals is red for negative correlations and black for positive ones. When Col.Pos.Neg=FALSE, all lines are in black. Default=TRUE.
Histograms.Counterfactuals: Should plots that shows the densities for the inidentifiable correlations be shown? Default =FALSE.

Author

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., Van der Elst, W., Molenberghs, G., Buyse, M., & Burzykowski, T. (submitted). On the relationship between the causal inference and meta-analytic paradigms for the validation of surrogate markers.

Van der Elst, W., Alonso, A., & Molenberghs, G. (submitted). An exploration of the relationship between causal inference and meta-analytic measures of surrogacy.

Examples

Run this code

if (FALSE)  #Time consuming (>5 sec) code parts
# Generate the vector of ICA values when rho_T0S0=.91, rho_T1S1=.91, and when the
# grid of values {0, .1, ..., 1} is considered for the correlations
# between the counterfactuals:
SurICA <- ICA.ContCont(T0S0=.95, T1S1=.91, T0T1=seq(0, 1, by=.1), T0S1=seq(0, 1, by=.1), 
T1S0=seq(0, 1, by=.1), S0S1=seq(0, 1, by=.1))

#obtain a plot of ICA

# Obtain a causal diagram that provides the medians of the 
# correlations between the counterfactuals for the range
# of ICA values between .9 and 1 (i.e., which assumed 
# correlations between the counterfactuals lead to a 
# high ICA?)
CausalDiagramContCont(SurICA, Min=.9, Max=1)

# Same, for low values of ICA
CausalDiagramContCont(SurICA, Min=0, Max=.5)

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