apc.LCa: Fit Age-Period-Cohort models and Lee-Carter models with effects modeled by natural splines.

Description

apc.LCa fits an Age-Period-Cohort model and sub-models (using apc.fit) as well as Lee-Carter models (using LCa.fit). show.apc.LCa plots the models in little boxes with their residual deviance with arrows showing their relationships.

Usage

apc.LCa( data,
  keep.models = FALSE,
          ... )
show.apc.LCa( x,
       dev.scale = TRUE,
             top = "Ad", ... )

Arguments

data

A data frame that must have columns A, P, D and Y, see e.g. apc.fit

keep.models

Logical. Should the apc object and the 5 LCa objects be returned too?

...

Further parameters passed on to LCa.fit or boxes.matrix.

The result from a call to apc.LCa.

dev.scale

Should the vertical position of the boxes with the models be scales relative to the deviance between the Age-drift model and the extended Lee-Carter model?

top

The model presented at the top of the plot of boxes (together with any other model with larger deviance) when vertical position is scaled by deviances. Only "Ad", "AP", "AC", "APa" or "ACa" will make sense.

Value

A 9 by 2 matrix classified by model and deviance/df; optionally (if models=TRUE) a list with the matrix as dev, apc, an apc object (from apc.fit), and LCa, a list with 5 LCa objects (from LCa.fit).

Details

The function apc.LCa fits all 9 models (well, 10) available as extension and sub-models of the APC-model and compares them by returning deviance and residual df.

Examples

Run this code

# NOT RUN {
library( Epi )

# Danish lung cancer incidence in 5x5x5 Lexis triangles
data( lungDK )
lc <- subset( lungDK, Ax>40 )[,c("Ax","Px","D","Y")]
names( lc )[1:2] <- c("A","P")
head( lc )

al <- apc.LCa( lc, npar=c(9,6,6,6,10), keep.models=TRUE, maxit=500, eps=10e-3 )
show.apc.LCa( al, dev=FALSE )
show.apc.LCa( al, top="AP" )
show.apc.LCa( al, top="APa" )
show.apc.LCa( al, top="ACa" )

# Danish mortality data
# }
# NOT RUN {
data( M.dk )
mdk <- subset( M.dk, sex==1 )[,c("A","P","D","Y")]
head( mdk )

al <- apc.LCa( mdk, npar=c(15,15,20,6,6), maxit=50, eps=10e-3,
               quiet=FALSE, VC=FALSE )
show.apc.LCa( al, dev=FALSE )
show.apc.LCa( al, dev=TRUE )
show.apc.LCa( al, top="AP" )

# Fit a reasonable model to Danish mortality data and plot results
mAPa <- LCa.fit( mdk, model="APa", npar=c(15,15,20,6,6), c.ref=1930,
                 a.ref=70, quiet=FALSE, maxit=250 )
par( mfrow=c(1,3) )
plot( mAPa ) 
# }