# Plots the individual-level surrogate threshold effect (STE) values and related metrics 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. ```r ## S3 method for class '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. ## Author(s) Wim Van der Elst, Ariel Alonso, & Geert Molenberghs ## See Also ISTE.ContCont ## Examples ```r # 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") ## End(Not run) ```

plot.ISTE.ContCont function