Surrogate (version 1.7)

plot ISTE.ContCont: Plots the individual-level surrogate threshold effect (STE) values and related metrics

Description

This function plots the individual-level surrogate threshold effect (STE) values and related metrics, e.g., the expected \(\Delta T\) values for a vector of \(\Delta S\) values.

Usage

# S3 method for ISTE.ContCont
plot(x, Outcome="ISTE", breaks=50, …)

Arguments

x

An object of class ISTE.ContCont. See ISTE.ContCont.

Outcome

The outcome for which a histogram has to be produced. When Outcome="ISTE", a histogram of the ISTE is produced. When Outcome="MSE", a histogram of the MSE values (of regression models in which \(\Delta T\) is regressed on \(\Delta S\)) is given. When Outcome="gamma0", a histogram of \(\gamma[0]\) values (of regression models in which \(\Delta T\) is regressed on \(\Delta S\)) is given. When Outcome="gamma1", a histogram of \(\gamma[1]\) values (of regression models in which \(\Delta T\) is regressed on \(\Delta S\)) is given. When Outcome="Exp.DeltaT", a histogram of the expected \(\Delta T\) values for a vector of \(\Delta S\) values (specified in the call of the ISTE.ContCont function) values is given. When Outcome="Exp.DeltaT.Low.PI", a histogram of the lower prediction intervals of the expected \(\Delta T\) values for a vector of \(\Delta S\) values (specified in the call of the ISTE.ContCont function) values is given. When Outcome="Exp.DeltaT.Up.PI", a histogram of the upper prediction intervals of the expected \(\Delta T\) values for a vector of \(\Delta S\) values (specified in the call of the ISTE.ContCont function) values is given. Dafault Outcome="ISTE". When Outcome="Delta_S_For_Which_Delta_T_equal_0", a histogram of \(omega\) is shown with \(E(Delta T | Delta S > omega)>0\).

breaks

The number of breaks used in the histogram(s). Default breaks=50.

…

Extra graphical parameters to be passed to hist().

References

Van der Elst, W., Alonso, A. A., and Molenberghs, G. (submitted). The individual-level surrogate threshold effect in a causal-inference setting.

See Also

ISTE.ContCont

Examples

# NOT RUN {
# Define input for analysis using the Schizo dataset, 
# with S=BPRS and T = PANSS. 
# For each of the identifiable quantities,
# uncertainty is accounted for by specifying a uniform
# distribution with min, max values corresponding to
# the 95% confidence interval of the quantity.
T0S0 <- runif(min = 0.9524, max = 0.9659, n = 1000)
T1S1 <- runif(min = 0.9608, max = 0.9677, n = 1000)

S0S0 <- runif(min=160.811, max=204.5009, n=1000)
S1S1 <- runif(min=168.989, max = 194.219, n=1000)
T0T0 <- runif(min=484.462, max = 616.082, n=1000)
T1T1 <- runif(min=514.279, max = 591.062, n=1000)

Mean_T0 <- runif(min=-13.455, max=-9.489, n=1000)
Mean_T1 <- runif(min=-17.17, max=-14.86, n=1000)
Mean_S0 <- runif(min=-7.789, max=-5.503, n=1000)
Mean_S1 <- runif(min=-9.600, max=-8.276, n=1000)

# Do the ISTE analysis
# }
# NOT RUN {
ISTE <- ISTE.ContCont(Mean_T1=Mean_T1, Mean_T0=Mean_T0, 
 Mean_S1=Mean_S1, Mean_S0=Mean_S0, N=2128, Delta_S=c(-50:50), 
 alpha.PI=0.05, PI.Bound=0, Show.Prediction.Plots=TRUE,
 Save.Plots="No", T0S0=T0S0, T1S1=T1S1, T0T0=T0T0, T1T1=T1T1, 
 S0S0=S0S0, S1S1=S1S1)

# Examine results:
summary(ISTE)

# Plots of results. 
  # Plot main ISTE results
plot(ISTE)
  # Other plots
plot(ISTE, Outcome="MSE")
plot(ISTE, Outcome="gamma0")
plot(ISTE, Outcome="gamma1")
plot(ISTE, Outcome="Exp.DeltaT")
plot(ISTE, Outcome="Exp.DeltaT.Low.PI")
plot(ISTE, Outcome="Exp.DeltaT.Up.PI")
# }