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

Description

Plots the distribution of Δ T_j|S_j and the 1-α% CIs for a user-requested ρ_{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 ρ_{T0T1} value (rather than a vector of ρ_{T0T1} values), and thus the plot automatically uses the considered ρ_{T0T1} value in the object x to compute the 1-α% CI for Δ T_j|S_j.

Usage

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## 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 "Δ T_j|S_j".

Main

The title of the PCA plot. Default " ".

alpha

The α 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.

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

Predict.Treat.T0T1.ContCont

Examples

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# 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))