The function uses termplot
to extract terms from a model
with, say, spline, terms, including the standard errors, computes
confidence intervals and transform these to the rate / rateratio
scale. Thus the default use is for models on the logscale such as
Poissonregression models. The function produces a plot with panels
sidebyside, one panel per term, and returns the
1 2 3 4 5 6 7 8 9 
obj 
An object with a 
plot 
Should a plot be produced? 
xlab 
Labels for the 
ylab 
Labels for the 
xeq 
Should the units all all plots have the same physical scale
for the 
yshr 
Shrinking of 
alpha 
1 minus the confidence level for computing confidence intervals 
terms 
Which terms should be reported. Passed on to

max.pt 
The maximal number of points in which to report the
terms. If 
A list with one component per term in the model object obj
,
each component is a 4column matrix with $x$ as the first column, and
3 columns with estimae and lower and upper confidence limit.
Bendix Cartensen
Ns
, termplot
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  # Get the diabetes data and set up as Lexis object
data(DMlate)
DMlate < DMlate[sample(1:nrow(DMlate),500),]
dml < Lexis( entry = list(Per=dodm, Age=dodmdobth, DMdur=0 ),
exit = list(Per=dox),
exit.status = factor(!is.na(dodth),labels=c("DM","Dead")),
data = DMlate )
# Split in 1year age intervals
dms < splitLexis( dml, time.scale="Age", breaks=0:100 )
# Model with 6 knots for both age and period
n.kn < 6
# Model agespecific rates with period referenced to 2004
( a.kn < with( subset(dms,lex.Xst=="Dead"),
quantile( Age+lex.dur, probs=(1:n.kn0.5)/n.kn ) ) )
( p.kn < with( subset(dms,lex.Xst=="Dead"),
quantile( Per+lex.dur, probs=(1:n.kn0.5)/n.kn ) ) )
m2 < glm( lex.Xst=="Dead" ~ 1 +
Ns( Age, kn=a.kn, intercept=TRUE ) +
Ns( Per, kn=p.kn, ref=2004 ),
offset = log( lex.dur ), family=poisson, data=dms )
# Finally we can plot the two effects:
Termplot( m2, yshr=0.9 )

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