# Plots the (Meta-Analytic) Individual Causal Association and related metrics when S and T are binary outcomes

### Description

This function provides a plot that displays the frequencies, percentages, cumulative percentages or densities of the individual causal association (ICA; *R^2_{H}* or *R_{H}*), and/or the odds ratios for *S* and *T* (*θ_{S}* and *θ_{T}*).

### Usage

1 2 3 4 5 6 | ```
## S3 method for class 'ICA.BinBin'
plot(x, R2_H=TRUE, R_H=FALSE, Theta_T=FALSE,
Theta_S=FALSE, Type="Density", Labels=FALSE, Xlab.R2_H,
Main.R2_H, Xlab.R_H, Main.R_H, Xlab.Theta_S, Main.Theta_S, Xlab.Theta_T,
Main.Theta_T, Cex.Legend=1, Cex.Position="topright",
col, Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), ylim, ...)
``` |

### Arguments

`x` |
An object of class |

`R2_H` |
Logical. When |

`R_H` |
Logical. When |

`Theta_T` |
Logical. When |

`Theta_S` |
Logical. When |

`Type` |
The type of plot that is produced. When |

`Labels` |
Logical. When |

`Xlab.R2_H` |
The legend of the X-axis of the |

`Main.R2_H` |
The title of the |

`Xlab.R_H` |
The legend of the X-axis of the |

`Main.R_H` |
The title of the |

`Xlab.Theta_S` |
The legend of the X-axis of the |

`Main.Theta_S` |
The title of the |

`Xlab.Theta_T` |
The legend of the X-axis of the |

`Main.Theta_T` |
The title of the |

`Cex.Legend` |
The size of the legend when |

`Cex.Position` |
The position of the legend, |

`col` |
The color of the bins. Default |

`Par` |
Graphical parameters for the plot. Default |

`ylim` |
The (min, max) values for the Y-axis |

.

`...` |
Extra graphical parameters to be passed to |

### Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

### References

Alonso, A., Van der Elst, W., Molenberghs, G., Buyse, M., & Burzykowski, T. (submitted). A causal-inference approach for the validation of surrogate endpoints based on information theory and sensitivity analysis.

### See Also

ICA.BinBin

### Examples

1 2 3 4 5 6 7 8 9 10 11 | ```
# Compute R2_H given the marginals,
# assuming monotonicity for S and T and grids
# pi_0111=seq(0, 1, by=.001) and
# pi_1100=seq(0, 1, by=.001)
ICA <- ICA.BinBin.Grid.Sample(pi1_1_=0.261, pi1_0_=0.285,
pi_1_1=0.637, pi_1_0=0.078, pi0_1_=0.134, pi_0_1=0.127,
Monotonicity=c("General"), M=2500, Seed=1)
# Plot the results (density of R2_H):
plot(ICA, Type="Density", R2_H=TRUE, R_H=FALSE,
Theta_T=FALSE, Theta_S=FALSE)
``` |

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