Surrogate (version 2.0)

plot SPF BinCont: Plots the surrogate predictive function (SPF) in the binary-continuous setting.

Description

Plots the surrogate predictive function (SPF) based on sensitivity-analyis, i.e., $P(\mathrm{\Delta }T|\mathrm{\Delta }S\in I[ab])$, in the setting where $S$ is continuous and $T$ is a binary endpoint.

Usage

# S3 method for SPF.BinCont
plot(x, Type="Frequency", Col="grey", Main, Xlab=TRUE, ...)

Arguments

x

A fitted object of class SPF.BinCont. See ICA.BinCont.

Type

The type of plot that is requested. The argument Type="Frequency" requests histograms for $P(\mathrm{\Delta }T|\mathrm{\Delta }S\in I[ab])$. The argument Type="Percentage" requests relative frequenties for $P(\mathrm{\Delta }T|\mathrm{\Delta }S\in I[ab])$. The argument Type="Most.Likely.DeltaT" requests a histogram of the most likely $\mathrm{\Delta }T$ values. For example, when in one run of the sensitivity analysis, $P(\mathrm{\Delta }T=-1|\mathrm{\Delta }S\in I[ab])=.6$, $P(\mathrm{\Delta }T=0|\mathrm{\Delta }S\in I[ab])=.3$, and $P(\mathrm{\Delta }T=-1|\mathrm{\Delta }S\in I[ab])=.1$, the most likely outcome in this run would be $P(\mathrm{\Delta }T=-1$. The argument Type="Most.Likely.DeltaT" generates a plot with percentages for the most likely $P(\mathrm{\Delta }T)$ value across all obtained values in the sensitivity analysis.

Col

The color of the bins or lines when histograms or density plots are requested. Default "grey".

Main

The title of the plot.

Xlab

Logical. Should labels on the X-axis be shown? Default Xlab=TRUE.

…

Arguments to be passed to the plot, histogram, ... functions.

References

Alonso, A., Van der Elst, W., Molenberghs, G., & Verbeke, G. (2017). Assessing the predictive value of a continuous surogate for a binary true endpoint based on causal inference.

See Also

SPF.BinCont

Examples

# NOT RUN {
 # time consuming code part
data(Schizo_BinCont)
# Use ICA.BinCont to examine surrogacy
Result_BinCont <- ICA.BinCont(M = 1000, Dataset = Schizo_BinCont,
Surr = PANSS, True = CGI_Bin, Treat=Treat, Diff.Sigma=TRUE)

# Obtain SPF
Fit <- SPF.BinCont(x=Result_BinCont, a = -30, b = -3)

# examine results
summary(Fit1)
plot(Fit1)

plot(Fit1, Type="Most.Likely.DeltaT")
# }