| plot.Predict.Treat.T0T1.ContCont {EffectTreat} | R Documentation |
Plots the distribution of the individual causal effect based on S for a specific assumed correlation between the counterfactuals.
Description
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.
Usage
## S3 method for class 'Predict.Treat.T0T1.ContCont'
plot(x, Xlab, Main, alpha=0.05, Cex.Legend=1, ...)
Arguments
x |
An object of class |
Xlab |
The legend of the X-axis of the plot. Default " |
Main |
The title of the PCA plot. Default " ". |
alpha |
The |
Cex.Legend |
The size of the legend of the plot. Default |
... |
Other arguments to be passed to the |
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
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))