`lca`

and `lca.rh`

type regression objects.
Plot of forecasted Lee-Carter models based on a series of fitted model objects
Comparison plots of the forecasted period effect and life expectancy of a series of fitted Lee-Carter models

1 | ```
matflc.plot(lca.obj, lca.base, at = 65, label = NULL, ...)
``` |

`lca.obj` |
a list of fitted model objects of class |

`lca.base` |
base fitted model object of class |

`at` |
target age at which to calculate life expectancy |

`label` |
a data label |

`...` |
additional arguments to |

The function makes use of a univariate ARIMA process (i.e. random walk with drift) in order to extrapolate the period effects *k_t* of the model objects in `lca.obj`

, which is illustrated by the calendar years together with the corresponding forecasted life expectancy for a given age.

Plot

Z. Butt and S. Haberman and H. L. Shang

`matfle.plot`

, `flc.plot`

, `elca.rh`

1 2 3 4 5 6 7 | ```
rfp.cmi <- dd.rfp(dd.cmi.pens, c(0.5, 1.2, -0.7, 2.5))
mod6e <- elca.rh(rfp.cmi, age=50:70, interpolate=TRUE, dec=3)
# plot with original (fitted) base values
matflc.plot(mod6e$lca, label='RFP CMI')
# use a standard LC model fitting as base values
mod6 <- lca.rh(dd.cmi.pens, mod='lc', error='gauss', max.age = 70, interpolate=TRUE)
matflc.plot(mod6e$lca, mod6, label='RFP CMI')
``` |

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