Estimation of relative risk

`summary`

provides summary statistics for a `threshpt`

object produced by `threshpt()`

1 2 |

`object` |
a fitted |

`...` |
Not used |

`summary.threshpt`

produces a list of summary informations for a fitted `threshpt`

object with components

`Formula ` |
the formula which is used in the threshpt function |

`Best fit ` |
estimated parameter coefficients of model with the minimum deviance |

`Deviance ` |
deviance of a fitted |

`Threshold ` |
threshold value of the model with the minimum deviance |

Youn-Hee Lim, Il-Sang Ohn, and Ho Kim

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# read the Seoul data set and create lag variables
data(mort)
seoul = read6city(mort, 11)
seoul_lag = lagdata(seoul, c("meantemp", "mintemp", "meanpm10", "meanhumi"), 5)
# find a optimal threshold and conduct piecewise linear regression
mythresh = threshpt(nonacc ~ meantemp_m3 + meanpm10_m2 + meanhumi + ns(sn, 4*10) + factor(dow),
expvar = "meantemp_m3", family = "poisson", data = seoul_lag,
startrng = 23, endrng = 33, searchunit = 0.2)
# provide summary informations
summary(mythresh)
``` |

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