plot.Predict.Treat.T0T1.ContCont function

Plots the distribution of the individual causal effect based on $S$ for a specific assumed correlation between the counterfactuals.

Plots the distribution of $\Delta T_j$|$S_j$ and the $1-\alpha$% CIs for a user-requested $\rho_{T0T1}$ value). The function is similar to plot.Predict.Treat.ContCont, but it is applied to an object of class Predict.Treat.T0T1.ContCont (rather than to an object of class Predict.Treat.ContCont). This object contains only one $\rho_{T0T1}$ value (rather than a vector of $\rho_{T0T1}$ values), and thus the plot automatically uses the considered $\rho_{T0T1}$ value in the object x to compute the $1-\alpha$% CI for $\Delta T_j$|$S_j$.

## S3 method for class 'Predict.Treat.T0T1.ContCont'
plot(x, Xlab, Main, alpha=0.05, Cex.Legend=1, ...)

Arguments

• x: An object of class Predict.Treat.T0T1.ContCont. See Predict.Treat.T0T1.ContCont.
• Xlab: The legend of the X-axis of the plot. Default "$\Delta T_j$|$S_j$".
• Main: The title of the PCA plot. Default " ".
• alpha: The $\alpha$ level to be used in the computation of the CIs. Default $0.05$.
• Cex.Legend: The size of the legend of the plot. Default $1$.
• ...: Other arguments to be passed to the $plot()$ function.

References

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

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

See Also

Predict.Treat.T0T1.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 the predicted value T for a patient who scores S = 10, using beta=5,
# SS=2, mu_S=4, assuming rho_T0T1=.6
indiv <- Predict.Treat.T0T1.ContCont(x=PCA, S=10, Beta=5, SS=2, mu_S=4, T0T1=.6)
summary(indiv)

# obtain a plot with the 95% CI around delta T_j | S_j (assuming rho_T0T1=.6)
plot(indiv, xlim=c(5, 12))