plot.aalen | R Documentation |

This function plots the non-parametric cumulative estimates for the additive risk model or the test-processes for the hypothesis of time-varying effects with re-sampled processes under the null.

## S3 method for class 'aalen' plot( x, pointwise.ci = 1, hw.ci = 0, sim.ci = 0, robust.ci = 0, col = NULL, specific.comps = FALSE, level = 0.05, start.time = 0, stop.time = 0, add.to.plot = FALSE, mains = TRUE, xlab = "Time", ylab = "Cumulative coefficients", score = FALSE, ... )

`x` |
the output from the "aalen" function. |

`pointwise.ci` |
if >1 pointwise confidence intervals are plotted with lty=pointwise.ci |

`hw.ci` |
if >1 Hall-Wellner confidence bands are plotted with lty=hw.ci. Only 0.95 % bands can be constructed. |

`sim.ci` |
if >1 simulation based confidence bands are plotted with lty=sim.ci. These confidence bands are robust to non-martingale behaviour. |

`robust.ci` |
robust standard errors are used to estimate standard error of estimate, otherwise martingale based standard errors are used. |

`col` |
specifice colors of different components of plot, in order: c(estimate,pointwise.ci,robust.ci,hw.ci,sim.ci) so for example, when we ask to get pointwise.ci, hw.ci and sim.ci we would say c(1,2,3,4) to use colors as specified. |

`specific.comps` |
all components of the model is plotted by default, but a list of components may be specified, for example first and third "c(1,3)". |

`level` |
gives the significance level. |

`start.time` |
start of observation period where estimates are plotted. |

`stop.time` |
end of period where estimates are plotted. Estimates thus plotted from [start.time, max.time]. |

`add.to.plot` |
to add to an already existing plot. |

`mains` |
add names of covariates as titles to plots. |

`xlab` |
label for x-axis. |

`ylab` |
label for y-axis. |

`score` |
to plot test processes for test of time-varying effects along with 50 random realization under the null-hypothesis. |

`...` |
unused arguments - for S3 compatibility |

Thomas Scheike

Martinussen and Scheike, Dynamic Regression models for Survival Data, Springer (2006).

# see help(aalen) data(sTRACE) out<-aalen(Surv(time,status==9)~chf+vf,sTRACE,max.time=7,n.sim=100) par(mfrow=c(2,2)) plot(out,pointwise.ci=1,hw.ci=1,sim.ci=1,col=c(1,2,3,4)) par(mfrow=c(2,2)) plot(out,pointwise.ci=0,robust.ci=1,hw.ci=1,sim.ci=1,col=c(1,2,3,4))

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