apc: Age-Period-Cohort Analysis

Functions for age-period-cohort analysis. The 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. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) <DOI:10.1093/biomet/asn026> is used. Thus, the analysis does not rely on ad hoc identification.

AuthorBent Nielsen
Date of publication2016-12-01 08:28:22
MaintainerBent Nielsen <bent.nielsen@nuffield.ox.ac.uk>
LicenseGPL-3
Version1.3

View on CRAN

Man pages

apc_1.3-package: Age-period-cohort analysis

apc.data.list: Arrange data as an apc.data.list

apc.data.list.subset: Cut age, period and cohort groups from data set.

apc.data.sums: Computes age, period and cohort sums of a matrix

apc.fit.model: Fits an age period cohort model

apc.forecast: Forecasts from age-period-cohort models.

apc.forecast.ac: Forecast for Poisson response model with AC structure.

apc.forecast.ap: Forecast for Poisson response model with AP structure.

apc.forecast.apc: Forecast models with APC structure.

apc.get.design: Create design matrices

apc.get.index: Get indices for mapping data into trapezoid formation

apc.identify: Identification of time effects

apc.plot.data.all: Make all descriptive plots.

apc.plot.data.level: Level plot of data matrix.

apc.plot.data.sparsity: This plot shows heat map of the sparsity of a data matrix.

apc.plot.data.sums: This plot shows sums of data matrix by age, period or cohort.

apc.plot.data.within: This plot shows time series of matrix within age, period or...

apc.plot.fit: Plots of apc estimates

apc.plot.fit.all: Make all fit plots.

apc.plot.fit.pt: Plot probability transform of responses given fitted values

apc.plot.fit.residuals: Level plots of residuals / fitted values / linear predictors

apc.polygon: Add connected line and standard deviation polygons to a plot

data.aids: UK aids data

data.asbestos: Asbestos data

data.Belgian.lung.cancer: Belgian lung cancer data

data.Italian.bladder.cancer: Italian bladder cancer data

data.Japanese.breast.cancer: Japanese breast cancer data

data.loss.BZ: Motor data

data.loss.TA: Motor data

data.loss.VNJ: Motor data

data.RH.mortality: 2-sample mortality data.

data.US.prostate.cancer: Japanese breast cancer data

internal: Internal apc Functions

vector.2.triangle: Organise vector as matrix with triangle structure

Functions

apc Man page
apc.data.list Man page
apc.data.list.subset Man page
apc.data.sums Man page
apc.fit.model Man page
apc.fit.table Man page
apc.forecast Man page
apc.forecast.ac Man page
apc.forecast.ap Man page
apc.forecast.apc Man page
apc.get.design Man page
apc.get.design.collinear Man page
apc.get.index Man page
apc.identify Man page
apc.internal.function.date.2.character Man page
apc-package Man page
apc.plot.data.all Man page
apc.plot.data.level Man page
apc.plot.data.sparsity Man page
apc.plot.data.sums Man page
apc.plot.data.within Man page
apc.plot.data.within.all.six Man page
apc.plot.fit Man page
apc.plot.fit.all Man page
apc.plot.fit.fitted.values Man page
apc.plot.fit.linear.predictors Man page
apc.plot.fit.pt Man page
apc.plot.fit.residuals Man page
apc.polygon Man page
data.aids Man page
data.asbestos Man page
data.asbestos.2013 Man page
data.asbestos.2013.men Man page
data.asbestos.2013.women Man page
data.Belgian.lung.cancer Man page
data.Italian.bladder.cancer Man page
data.Japanese.breast.cancer Man page
data.loss.BZ Man page
data.loss.TA Man page
data.loss.VNJ Man page
data.RH.mortality Man page
data.RH.mortality.dk Man page
data.RH.mortality.no Man page
data.US.prostate.cancer Man page
foo2 Man page
foo3 Man page
foo4 Man page
vector.2.triangle Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.