CausalPCA.ContCont {EffectTreat}

Show a causal diagram of the median correlation between the counterfactuals in the continuous-continuous setting

Description

This function provides a diagram that depicts the estimable correlations \rho(_{T_0, S}) and \rho(_{T_1, S}), and median of the correlation \rho(_{T_0, T_1}) for a specified range of values of the predictive causal association (PCA; \rho_{\psi}).

Usage

CausalPCA.ContCont(x, Min=-1, Max=1, Cex.Letters=3, Cex.Corrs=2, 
Lines.Rel.Width=TRUE, Col.Pos.Neg=TRUE)

Arguments

x

An object of class PCA.ContCont. See PCA.ContCont.

Min

The minimum values of the PCA that should be considered. Default=-1.

Max

The maximum values of the PCA that should be considered. Default=1.

Cex.Letters

The size of the symbols for S, T_{0}, and T_{1} in the diagram. Default=3.

Cex.Corrs

The size of the text depicting the (median) correlations in the diagram. Default=2.

Lines.Rel.Width

Logical. When Lines.Rel.Width=TRUE, the widths of the lines that represent the correlations in the diagram 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 in the diagram is red for negative correlations and black for positive ones. When Col.Pos.Neg=FALSE, all lines are in black. Default=TRUE.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (submitted). Validating predictors of therapeutic success: a causal inference approach.

See Also

PCA.ContCont

Examples

# Generate the vector of PCA.ContCont values when rho_T0S=.3, rho_T1S=.9, 
# sigma_T0T0=2, sigma_T1T1=2,sigma_SS=2, and the grid of values {-1, -.99, 
# ..., 1} is considered for the correlations between T0 and T1:
PCA <- PCA.ContCont(T0S=.3, T1S=.9, T0T0=2, T1T1=2, SS=2, 
T0T1=seq(-1, 1, by=.01))

# Obtain causal diagram for PCA score range [-1; 1]:
CausalPCA.ContCont(PCA,  Min=-1, Max=1)

# Obtain causal diagram for PCA score range [0.5; 1]:
CausalPCA.ContCont(PCA,  Min=0.5, Max=1)