EffectTreat (version 1.1)

plot GoodPretreatContCont: Graphically illustrates the theoretical plausibility of finding a good pretreatment predictor in the continuous-continuous case

Description

This function provides a plot that displays the frequencies, percentages, or cumulative percentages of \(\rho_{min}^{2}\) for a fixed value of \(\delta\) (given the observed variances of the true endpoint in the control and experimental treatment conditions and a specified grid of values for the unidentified parameter \(\rho(_{T_{0},T_{1}})\); see GoodPretreatContCont). For details, see the online appendix of Alonso et al., submitted.

Usage

# S3 method for GoodPretreatContCont
plot(x, main, col, Type="Percent", Labels=FALSE, 
Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), …)

Arguments

x

An object of class GoodPretreatContCont. See GoodPretreatContCont.

main

The title of the plot.

col

The color of the bins.

Type

The type of plot that is produced. When Type=Freq or Type=Percent, the Y-axis shows frequencies or percentages of \(\rho_{min}^{2}\). When Type=CumPerc, the Y-axis shows cumulative percentages of \(\rho_{min}^{2}\). Default "Percent".

Labels

Logical. When Labels=TRUE, the percentage of \(\rho_{min}^{2}\) values that are equal to or larger than the midpoint value of each of the bins are displayed (on top of each bin). Only applies when Type=Freq or Type=Percent. Default FALSE.

Par

Graphical parameters for the plot. Default par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)).

…

Extra graphical parameters to be passed to hist().

References

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

See Also

GoodPretreatContCont

Examples

# NOT RUN {
# compute rho^2_min in the setting where the variances of T in the control
# and experimental treatments equal 100 and 120, delta is fixed at 50,
# and the grid G={0, .01, ..., 1} is considered for the counterfactual 
# correlation rho_T0T1:

MinPred <- GoodPretreatContCont(T0T0 = 100, T1T1 = 120, Delta = 50,
T0T1 = seq(0, 1, by = 0.01))

# Plot the results (use percentages on Y-axis)
plot(MinPred, Type="Percent")

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

Run the code above in your browser using DataCamp Workspace