Plots distributed lags curves from the `modTempEff`

fit.

1 2 3 4 |

`x` |
object of class |

`which` |
Which DL curve should be plotted? for cold, heat or both of them (default). |

`add` |
logical; if |

`new` |
logical indicating if a new device should be opened. If |

`var.bayes` |
logical indicating if the 'Bayesian' rather than the frequentist standard errors should be employed to compute the pointwise confidence intervals to be plotted |

`delta.rr` |
logical indicating if the DL curves should be plotted on the log scale or as per cent change in relative risk, i.e. 100*(exp(.)-1). |

`level` |
the selected confidence level of the pointwise confidence intervals to be plotted |

`updown` |
logical; if |

`col.shade` |
the color of the shaded area representing the pointwise confidence intervals. If |

`leg` |
the possible legends to be set on the |

`...` |
additional arguments, such as |

Takes a fitted `"modTempEff"`

object produced by `tempeff()`

and plots the
DL curves for cold and heat effect with relevant pointwise confidence intervals.
`plot.modTempEff`

also works with objects with fixed (not estimated) breakpoint, namely
fits returned by

`tempeff(.., fcontrol=fit.control(it.max=0))`

.

Note `add=TRUE`

makes sense (and works) only for a single (cold *or* heat) DL curve to be superimposed to an existing plot.

The function simply plots the required estimated DL curve. If the fitted model includes only a smooth term for
the long term trend, `plot.modTempEff`

draws it.

Vito Muggeo

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## Not run:
#obj is an object returned by tempeff()
#plots DL curves for cold and heat with 95% pointwise CI
# using frequentist standard errors
plot(obj)
#plots the estimated DL curve only for heat with 90% pointwise CI
# using bayesian standard errors
plot(obj, "heat", var.bayes=TRUE, level=.90)
## End(Not run)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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