Description Usage Arguments Warning Author(s) References See Also Examples

View source: R/apc_plot_data.R

`apc.plot.data.within`

produces plot showing time series of matrix
within age, period or cohort against one of the other two indices.
`apc.plot.data.within.all.six`

produces all six plots in one panel plot.

These plots are sometimes used to gauge how many of the age, period, cohort factors are needed: If lines are parallel when dropping one index the corresponding factor may not be needed. In practice these plots should possibly be used with care, see Italian bladder cancer example below.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
apc.plot.data.within(apc.data.list,
data.type="r",plot.type="awc",
average=FALSE,
thin=NULL,apc.index=NULL,
ylab=NULL,type="o",log="",legend=TRUE,
lty=1:5,col=1:6,bty="n",main=NULL,
x="topleft",return=FALSE)
apc.plot.data.within.all.six(apc.data.list,
data.type="r",
average=FALSE,
thin=NULL,apc.index=NULL,
ylab=NULL,type="o",log="",legend=TRUE,
lty=1:5,col=1:6,bty="n",main.outer=NULL,
x="topleft")
``` |

`apc.data.list` |
List. See |

`data.type` |
Optional. Character. "r"="response" / "d"="dose" / "m"="mortality"="rates" if sums are computed for responses/dose/rates, where rates are found through division response/dose. "r" is default. |

`plot.type` |
Optional. "awp", "pwa" "awc", "cwa, "cwp", "pwc": for example: "awp" gives time series in age within each period level: for an AP data-array these are the column sums. |

`average` |
Optional. Logical. If TRUE/FALSE reports averages/sums. Default is FALSE. |

`thin` |
Optional. Numerical. age/periods/cohorts are grouped in groups of size thin. Default is computed from dimensions of data. A warning is produced if dimension is not divisible by thin, so that one group is smaller than other groups. |

`apc.index` |
Optional. List. See |

`ylab` |
Optional |

`type` |
Optional |

`log` |
Optional |

`legend` |
Optional |

`lty` |
Optional |

`col` |
Optional |

`bty` |
Optional |

`main` |
Optional. Character. Main title for single plot. Default is NULL, in which case a title is generated internally. |

`main.outer` |
Optional. Character. Main title for panel of six plots, to be shown in outer margin. Default is NULL, in which case a title is generated internally. |

`x` |
Optional |

`return` |
Optional. If TRUE return matrix that is plotted. Default is FALSE |

A warning is produced if dimension is not divisible by thin, so that one group is smaller than other groups.

Bent Nielsen <bent.nielsen@nuffield.ox.ac.uk> 17 Nov 2016 (25 Apr 2015)

Clayton, D. and Schifflers, E. (1987a)
Models for temperoral variation in cancer rates. I: age-period and age-cohort models.
*Statistics in Medicine* 6, 449-467.

Clayton, D. and Schifflers, E. (1987b) Models for temperoral variation in cancer rates. II: age-period-cohort models. *Statistics in Medicine* 6, 469-481.

Martinez Miranda, M.D., Nielsen, B. and Nielsen, J.P. (2015) Inference and forecasting in the age-period-cohort model with unknown exposure with an application to mesothelioma mortality. *Journal of the Royal Statistical Society* A 178, 29-55. *Download*: Article, Nuffield DP.

`data.Japanese.breast.cancer`

,
`data.Italian.bladder.cancer`

and
`data.asbestos`

for information on the data used in the example.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ```
#####################
# EXAMPLE with artificial data
# Generate a 3x4 matrix in "AP" data.format with the numbers 1..12
# Then make a data list
# Then plot data.
# Note: this deterministic matrix has neither age, period, or cohort factors,
# only linear trends. Thus all 6 plots have parallel lines.
m.data <- matrix(data=seq(length.out=12),nrow=3,ncol=4)
m.data
data.list <- apc.data.list(m.data,"AP")
apc.plot.data.within(data.list,log="")
# It also works with a single argument, but then a default log scale is used.
apc.plot.data.within(data.list)
#####################
# EXAMPLE with Japanese breast cancer data
# Clayton and Shifflers (1987b) use APC design
# Make a data list
# Then plot data.
# Note: No plot appears to have approximately parallel lines.
data.list <- data.Japanese.breast.cancer()
apc.plot.data.within(data.list,"m",1,log="y")
# It also works with a single argument, but then a default log scale is used.
# Note that warnings are given in relation to the data chosen thinning
apc.plot.data.within(data.list)
#####################
# EXAMPLE with Italian bladder cancer data
# Clayton and Shifflers (1987a) use AC design
# Note: plot of within cohort against age appears to have approximately parallel lines.
# This is Figure 2 in Clayton and Shifflers (1987a)
# Note: plot of within age against cohort appears to have approximately parallel lines.
# Indicates that interpretation should be done carefully.
data.list <- data.Italian.bladder.cancer()
apc.plot.data.within(data.list,"m",1,log="y")
#####################
# EXAMPLE with asbestos data
# Miranda Martinex, Nielsen and Nielsen (2014).
# This is Figure 1d
data.list <- data.asbestos()
apc.plot.data.within(data.list,type="l",lty=1)
``` |

```
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
apc.plot.data.within error: plot.type not recognised
[1] "apc.plot.data.within warning: maximal index not divisible by thin, so last group smaller than other groups"
apc.plot.data.within error: plot.type not recognised
[1] "apc.plot.data.within warning: maximal index not divisible by thin, so last group smaller than other groups"
```

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