# matflc.plot: Miscellaneous plotting functions for 'lca' and 'lca.rh' type... In ilc: Lee-Carter Mortality Models using Iterative Fitting Algorithms

## Description

Comparison plots of the forecasted period effect and life expectancy of a series of fitted Lee-Carter models

## Usage

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

## Arguments

 `lca.obj` a list of fitted model objects of class `lca` (such as returned by `elca.rh` function) `lca.base` base fitted model object of class `lca` to be used in comparison `at` target age at which to calculate life expectancy `label` a data label `...` additional arguments to `forecast` function

## Details

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

## Author(s)

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') ```