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

This function performs the backward joinpoint selection algorithm with K maximum possible number of joinpoints based on the likelihood ratio test statistic. The p-value is determined by a Monte Carlo method.

1 | ```
ljrb(K,y,n,tm,X,ofst,R=1000,alpha=.05)
``` |

`K` |
the pre-specified maximum possible number of joinpoints |

`y` |
the vector of Binomial responses. |

`n` |
the vector of sizes for the Binomial random variables. |

`tm` |
the vector of ordered observation times. |

`X` |
a design matrix containing other covariates. |

`ofst` |
a vector of known offsets for the logit of the response. |

`R` |
number of Monte Carlo simulations. |

`alpha` |
significance level of the test. |

The re-weighted log-likelihood is the log-likelihood divided by the largest component of n.

`pvals` |
The estimates of the p-values via simulation. |

`Coef` |
A table of coefficient estimates. |

`Joinpoints` |
The estimates of the joinpoint, if it is significant. |

`wlik` |
The maximum value of the re-weighted log-likelihood. |

The authors are Michal Czajkowski, Ryan Gill, and Greg Rempala. The software is maintained by Ryan Gill [email protected].

Czajkowski, M., Gill, R. and Rempala, G. (2008). Model selection in logistic joinpoint regression with applications to analyzing cohort mortality patterns. *Statistics in Medicine* 27, 1508-1526.

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