plot.ICA.ContCont.Mult: Plots the Individual Causal Association in the setting where...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

plot ICA.ContCont.MultS

R Documentation

Plots the Individual Causal Association in the setting where there are multiple continuous S and a continuous T

Description

This function provides a plot that displays the frequencies, percentages, or cumulative percentages of the multivariate individual causal association (R^2_{H}). These figures are useful to examine the sensitivity of the obtained results with respect to the assumptions regarding the correlations between the counterfactuals.

Usage

  ## S3 method for class 'ICA.ContCont.MultS'
plot(x, R2_H=FALSE, Corr.R2_H=TRUE, 
   Type="Percent", Labels=FALSE,  
   Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), col, 
   Prediction.Error.Reduction=FALSE, ...)

Arguments

`x`	An object of class `ICA.ContCont.MultS`. See `ICA.ContCont.MultS` or `ICA.ContCont.MultS_alt`.
`R2_H`	Should a plot of the `R^2_{H}` be provided? Default `FALSE`.
`Corr.R2_H`	Should a plot of the corrected `R^2_{H}` be provided? Default `TRUE`.
`Type`	The type of plot that is produced. When `Type=Freq` or `Type=Percent`, the Y-axis shows frequencies or percentages of `R^2_{H}`. When `Type=CumPerc`, the Y-axis shows cumulative percentages of `R^2_{H}`. Default "Percent".
`Labels`	Logical. When `Labels=TRUE`, the percentage of `R^2_{H}` values that are equal to or larger than the midpoint value of each of the bins are displayed (on top of each bin). Default `FALSE`.
`Par`	Graphical parameters for the plot. Default `par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1))`.
`col`	The color of the bins. Default `col <- c(8)`.
`Prediction.Error.Reduction`	Should a plot be shown that shows the prediction error (reisdual error) in predicting `Delta`T using an intercept only model, and that shows the prediction error (reisdual error) in predicting `Delta`T using `Delta S_1`, `Delta S_2`, ...? Default `Prediction.Error.Reduction=FALSE`.
`...`	Extra graphical parameters to be passed to `hist()`.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Van der Elst, W., Alonso, A. A., & Molenberghs, G. (2017). Univariate versus multivariate surrogate endpoints.

Examples

## Not run:  #time-consuming code parts
# Specify matrix Sigma (var-cavar matrix T_0, T_1, S1_0, S1_1, ...)
# here for 1 true endpoint and 3 surrogates

s<-matrix(rep(NA, times=64),8)
s[1,1] <- 450; s[2,2] <- 413.5; s[3,3] <- 174.2; s[4,4] <- 157.5; 
s[5,5] <- 244.0; s[6,6] <- 229.99; s[7,7] <- 294.2; s[8,8] <- 302.5
s[3,1] <- 160.8; s[5,1] <- 208.5; s[7,1] <- 268.4 
s[4,2] <- 124.6; s[6,2] <- 212.3; s[8,2] <- 287.1
s[5,3] <- 160.3; s[7,3] <- 142.8 
s[6,4] <- 134.3; s[8,4] <- 130.4 
s[7,5] <- 209.3; 
s[8,6] <- 214.7 
s[upper.tri(s)] = t(s)[upper.tri(s)]

# Marix looks like:
#            T_0    T_1  S1_0  S1_1  S2_0   S2_1  S2_0  S2_1
#            [,1]  [,2]  [,3]  [,4]  [,5]   [,6]  [,7]  [,8]
# T_0  [1,] 450.0    NA 160.8    NA 208.5     NA 268.4    NA
# T_1  [2,]    NA 413.5    NA 124.6    NA 212.30    NA 287.1
# S1_0 [3,] 160.8    NA 174.2    NA 160.3     NA 142.8    NA
# S1_1 [4,]    NA 124.6    NA 157.5    NA 134.30    NA 130.4
# S2_0 [5,] 208.5    NA 160.3    NA 244.0     NA 209.3    NA
# S2_1 [6,]    NA 212.3    NA 134.3    NA 229.99    NA 214.7
# S3_0 [7,] 268.4    NA 142.8    NA 209.3     NA 294.2    NA
# S3_1 [8,]    NA 287.1    NA 130.4    NA 214.70    NA 302.5

# Conduct analysis
ICA <- ICA.ContCont.MultS(M=100, N=200, Show.Progress = TRUE,
  Sigma=s, G = seq(from=-1, to=1, by = .00001), Seed=c(123), 
  Model = "Delta_T ~ Delta_S1 + Delta_S2 + Delta_S3")

# Explore results
summary(ICA)
plot(ICA)

## End(Not run)

Surrogate documentation built on Sept. 25, 2023, 5:07 p.m.