plot.SPPBinBin: Plots the surrogate predictive function (SPF) in the...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

plot SPF BinBin

R Documentation

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 class '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(s)

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

## Not run: 
# 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")

## End(Not run)

Surrogate documentation built on June 22, 2024, 9:16 a.m.