Description Usage Arguments Details Value Author(s) References See Also Examples

This function constructs a diagnostic plot of the adequacy of the Weibull distribution for survival data with respect to one categorical covariate. If the Weibull distribution fits the data well, then the lines produced should be linear and parallel.

1 | ```
WeibullDiag(formula, data = parent.frame(), labels = names(m$strata))
``` |

`formula` |
A formula containing a |

`data` |
Data set. |

`labels` |
A vector containing labels for the plotted lines. |

As discussed in Klein and Moeschberger (2003), one method for checking the adequacy of the Weibull model with a
categorical covariate is to produce stratified Kaplan-Meier estimates (KM), which can be transformed to estimate
the log cumulative hazard for each stratum. Then in a plot of *log(t)* versus *log(-log(KM))*, the
lines should be linear and parallel. This can be seen as the log cumulative hazard for the Weibull distribution
is

*log H(t) = log λ + α log t.*

Produces a plot of log Time vs. log Estimated Cumulative Hazard for each level of the predictor
(similarly to what can be obtained using `plot.survfit`

and the `fun = "cloglog"`

option),
as well as a data set containing that information.

Sarah R. Haile, [email protected]

Klein, J. and Moeschberger, M. (2003).
*Survival analysis: techniques for censored and truncated data*.
2nd edition, Springer.

Requires survival. A similar plot can be produced using `plot.survfit`

and the option `fun = "cloglog"`

.

1 2 |

```
Loading required package: survival
```

SurvRegCensCov documentation built on May 30, 2017, 3:32 a.m.

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