Functions for age-period-cohort analysis.
Aggregate data can be organised in matrices indexed by age-cohort, age-period or cohort-period.
The data can include dose and response or just doses.
The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors.
Individual-level data should have a row for each individual and columns for each of age, period, and cohort.
The statistical model for repeated cross-section is a generalized linear model.
The statistical model for panel data is ordinary least squares.
The canonical parametrisation of
Kuang, Nielsen and Nielsen (2008) is used.
Thus, the analysis does not rely on ad hoc identification.
| ReadData |
|
Illustrates how to read data from files and construct apc.data.list.
Uses the Belgian lung cancer data from Clayton and Schiffler (1987).
Considers data store in
csv format:
response.csv,
rates.csv
and
xlsx format:
both.xlsx.
|
1.3 |
|
|
R code |
| Identification |
|
Illustrate and check identification used in plot fit function |
1.1 |
PDF |
source |
R code |
| NewDesign |
|
Generating new models from design matrix function |
1.1 |
PDF |
source |
R code |
| ReproducingMMNN2015 |
|
Reproducing Martinez Miranda, Nielsen and Nielsen (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 series A 178, 29-55.
Download:
Published version.
Nuffield DP.
Estimation, forecasting using data.asbestos() |
1.2.2 |
PDF |
source |
R code |
| ReproducingMMNN2016 |
|
Reproducing Martinez-Miranda, Nielsen and Nielsen (2016).
A simple benchmark for mesothelioma projection for Great Britain.
Occupational and Environmental Medicine 73, 561-563.
Download:
Published version.
Nuffield DP.
Estimation, forecasting using data.asbestos.2013() |
1.2.1 |
PDF |
source |
R code |
| ReproducingHN2016 |
|
Reproducing Harnau and Nielsen (2018).
Asymptotic theory for over-dispersed age-period-cohort and extended chain ladder models.
Journal of the American Statistical Association 113, 1722-1732.
Download:
Published version.
Nuffield DP.
Estimation, forecasting using data.loss.TA() |
1.3.2 |
PDF |
source |
R code |
| ReproducingKN2018 |
|
Reproducing Kuang and Nielsen (2020).
Generalized log normal Chain-Ladder models.
Scandinavian Actuarial Journal 2020, 553--576.
Download:
Open access.
Nuffield DP.
Estimation, forecasting using data.loss.XL() |
1.3.5 |
PDF |
source |
R code |