plot.DLM {BayesMortalityPlus} | R Documentation |

## DLM: Plot the life table

### Description

Function that returns a log-scale ggplot of the `DLM`

and `ClosedDLM`

objects returned by dlm() and dlm_close() functions.

### Usage

```
## S3 method for class 'DLM'
plot(
x,
plotIC = TRUE,
plotData = TRUE,
labels = NULL,
colors = NULL,
linetype = NULL,
prob = 0.95,
age = NULL,
...
)
```

### Arguments

`x` |
Object of the class |

`plotIC` |
Logical. If 'TRUE' (default), shows the predictive intervals. |

`plotData` |
Logical. If 'TRUE' (default), shows crude rate (black dots). |

`labels` |
Vector with the name of the curve label. (Optional). |

`colors` |
Vector with the color of the curve. (Optional). |

`linetype` |
Vector with the line type of the curve. (Optional). |

`prob` |
Coverage probability of the predictive intervals. Default is '0.95'. |

`age` |
Vector with the ages to plot the life table. |

`...` |
Other arguments. |

### Value

A 'ggplot' object with fitted life table.

### See Also

`plot.HP()`

, `plot.BLC()`

and `plot.PredBLC()`

for `HP`

, `BLC`

or `PredBLC`

methods.

`plot.list()`

to the `list`

method, adding multiple objects in one single plot.

`plot_chain()`

to plot the chains generated by the MCMC algorithms for the `HP`

and `DLM`

objects.

### Examples

```
## Selecting the log mortality rate of the 1990 male population ranging from 0 to 100 years old
USA1990 = USA[USA$Year == 1990,]
x = 0:100
Ex = USA1990$Ex.Male[x+1]
Dx = USA1990$Dx.Male[x+1]
y = log(Dx/Ex)
## Fitting DLM
fit = dlm(y, ages = 0:100, M = 100)
## Plotting the life tables:
plot(fit)
## Now we are starting from 20 years
fit2 = dlm(y[21:101], Ft = 1, Gt = 1, ages = 20:100, M = 100)
plot(fit2, plotIC = FALSE)
## To plot multiples life tables see ?plot.list
plot(list(fit, fit2), age = 20:100,
plotData = FALSE,
colors = c("red", "blue"),
labels = c("1", "2"))
```

*BayesMortalityPlus*version 0.2.4 Index]