plot.PCA.ContCont: Plots the Predictive Causal Association in the...
In EffectTreat: Prediction of Therapeutic Success

Description Usage Arguments Author(s) References See Also Examples

This function provides a plot that displays the frequencies, percentages, or cumulative percentages of the Predictive Causal Association (PCA; ρ_{ψ}, R^2_{ψ}). 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, ...)

`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 ρ_{ψ} (univariate predictor case) or R^2_{ψ} (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 δ. When `Type=CumPerc`, the Y-axis shows cumulative percentages of PCA and/or δ. Default "Percent".
`Labels`	Logical. When `Labels=TRUE`, the percentage of ρ_{ψ}, R^2_{ψ} and/or δ 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 δ 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 ρ[T0T1] and PCA is provided. Default `FALSE`.
`R2_psi_g`	Logical. When `R2_psi_g=TRUE`, a plot depicting the relation between ρ[T0T1] and R^2_{ψ g} is provided. Default `FALSE`.
`Main.Good.Pretreat`	The title of the plot of δ. 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()`.

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

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

PCA.ContCont

# 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")