plot SPF BinBin: Plots the surrogate predictive function (SPF) in the binary-binary settinf.

Description

Plots the surrogate predictive function (SPF), i.e., \(r(i,j)=P(\Delta T=i|\Delta S=j)\), in the setting where both \(S\) and \(T\) are binary endpoints. For example, \(r(-1,1)\) quantifies the probability that the treatment has a negative effect on the true endpoint (\(\Delta T=-1\)) given that it has a positive effect on the surrogate (\(\Delta S=1\)).

Usage

# S3 method for SPF.BinBin
plot(x, Type="All.Histograms", Specific.Pi="r_0_0", Col="grey", 
Box.Plot.Outliers=FALSE, Legend.Pos="topleft", Legend.Cex=1, ...)

Arguments

x: A fitted object of class SPF.BinBin. See ICA.BinBin.
Type: The type of plot that is requested. Possible choices are: Type="All.Histograms", the histograms of all \(9\) \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors arranged in a \(3\) by \(3\) grid; Type="All.Densities", plots of densities of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors; Type="Histogram", the histogram of a particular \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vector (the Specific.Pi= argument has to be used to specify the desired \(r(i,j)\)); Type="Density", the density of a particular \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vector (the Specific.Pi= argument has to be used to specify the desired \(r(i,j)\)); Type="Box.Plot", a box plot of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors; Type="Lines.Mean", a line plot the depicts the means of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors; Type="Lines.Median", a line plot the depicts the medians of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors; Type="Lines.Mode", a line plot the depicts the modes of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors; Type="3D.Mean", a 3D bar plot the depicts the means of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors; Type="3D.Median", a 3D bar plot the depicts the medians of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors; Type="3D.Mode", a 3D bar plot the depicts the modes of all \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vectors.
Specific.Pi: When Type="Histogram" or Type="Density" , the histogram/density of a particular \(r(i,j)=P(\Delta T=i|\Delta S=j)\) vector is shown. The Specific.Pi= argument is used to specify the desired \(r(i,j)\)). Default r_0_0.
Col: The color of the bins or lines when histograms or density plots are requested. Default "grey".
Box.Plot.Outliers: Logical. Should outliers be depicted in the box plots?. Default FALSE.
Legend.Pos: Position of the legend when a type="Box.Plot", type="Lines.Mean", type="Lines.Median", or type="Lines.Mode" is requested. Default "topleft".
Legend.Cex: Size of the legend when a type="Box.Plot", type="Lines.Mean", type="Lines.Median", or type="Lines.Mode" is requested. Default 1.
...: Arguments to be passed to the plot, histogram, ... functions.

Author

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (2015). Assessing a surrogate effect predictive value in a causal inference framework.

Examples

Run this code

if (FALSE) {
# Generate plausible values for Pi  
ICA <- ICA.BinBin.Grid.Sample(pi1_1_=0.341, pi0_1_=0.119,
pi1_0_=0.254, pi_1_1=0.686, pi_1_0=0.088, pi_0_1=0.078, Seed=1,
Monotonicity=c("General"), M=2500)
           
# Compute the surrogate predictive function (SPF)
SPF <- SPF.BinBin(ICA)

# Explore the results
summary(SPF)

# Examples of plots 
plot(SPF, Type="All.Histograms")
plot(SPF, Type="All.Densities")
plot(SPF, Type="Histogram", Specific.Pi="r_0_0")
plot(SPF, Type="Box.Plot", Legend.Pos="topleft", Legend.Cex=.7)
plot(SPF, Type="Lines.Mean")
plot(SPF, Type="Lines.Median")
plot(SPF, Type="3D.Mean")
plot(SPF, Type="3D.Median")
plot(SPF, Type="3D.Spinning.Mean")
plot(SPF, Type="3D.Spinning.Median")
}