plot.PCA.ContCont function

Plots the Predictive Causal Association in the continuous-continuous case

This function provides a plot that displays the frequencies, percentages, or cumulative percentages of the Predictive Causal Association (PCA; $\rho_{\psi}$, $R^2_{\psi}$). These figures are useful to examine the sensitivity of the obtained results with respect to the assumptions regarding the correlations between the counterfactuals (for details, see Alonso et al., submitted). Optionally, it is also possible to obtain plots that are useful in the examination of the plausibility of finding a good pretreatment predictor (in the univariate case).

## S3 method for class 'PCA.ContCont'
plot(x, Xlab.PCA, Main.PCA, Type="Percent", 
Labels=FALSE, PCA=TRUE, Good.Pretreat=FALSE, EffectT0T1=FALSE, 
R2_psi_g=FALSE, Main.Good.Pretreat, Par=par(oma=c(0, 0, 0, 0), 
mar=c(5.1, 4.1, 4.1, 2.1)), col, ...)

Arguments

• x: An object of class PCA.ContCont or Multivar.PCA.ContCont. See PCA.ContCont and Multivar.PCA.ContCont.
• Xlab.PCA: The legend of the X-axis of the PCA plot. Default $\rho_{\psi}$ (univariate predictor case) or $R^2_{\psi}$ (multivariate predictor case).
• Main.PCA: The title of the PCA plot. Default "PCA".
• Type: The type of plot that is produced. When Type=Freq or Type=Percent, the Y-axis shows frequencies or percentages of PCA and/or $\delta$. When Type=CumPerc, the Y-axis shows cumulative percentages of PCA and/or $\delta$. Default "Percent".
• Labels: Logical. When Labels=TRUE, the percentage of $\rho_{\psi}$, $R^2_{\psi}$ and/or $\delta$ values that are equal to or larger than the midpoint value of each of the bins are displayed (on top of each bin). Default FALSE.
• PCA: Logical. When PCA=TRUE, a plot of the PCA is provided. Default TRUE.
• Good.Pretreat: Logical. When Good.Pretreat=TRUE, a plot of $\delta$ is provided. This plot is useful in the context of examinating the plausibility of finding a good pretretment predictor endpoint. For details, see Alonso et al. (submitted). Can only be requested for fitted objects of class object of class PCA.ContCont. Default FALSE.
• EffectT0T1: Logical. When EffectT0T1=TRUE, a plot depicting the relation between $\rho[T0T1]$ and PCA is provided. Default FALSE.
• R2_psi_g: Logical. When R2_psi_g=TRUE, a plot depicting the relation between $\rho[T0T1]$ and $R^2_{\psi g}$ is provided. Default FALSE.
• Main.Good.Pretreat: The title of the plot of $\delta$. For details, see Alonso et al. (submitted).
• Par: Graphical parameters for the plot. Default par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)).
• col: The color of the bins. Default col <- c(8).
• ...: Extra graphical parameters to be passed to hist() or plot().

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

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))

# Plot the results:
plot(PCA)

# Same plot but add the percentages of PCA values that are equal to or larger 
# than the midpoint values of the bins
plot(PCA, Labels=TRUE)

# Plot of the cumulative distribution of PCA
plot(PCA, Typ="CumPerc")