Description Usage Arguments Value Note Author(s) Examples
This function is partly for convenient specification of natural splines in practical modeling. The convention used is to take the smallest and the largest of the supplied knots as boundary knots. It also has the option of centering the effects provided at a chosen reference point as well as projecting the columns on the orthogonal space to that spanned by the intercept and the linear effect of the variable, and finally fixing slopes beyond boundary knots (clamping).
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x 
A variable. 
ref 
Scalar. Reference point on the 
df 
degrees of freedom. 
knots 
knots to be used both as boundary and internal knots. If

intercept 
Should the intercept be included in the resulting
basis? Ignored if any of 
Boundary.knots 
The boundary knots beyond which the spline is
linear. Defaults to the minimum and maximum of 
fixsl 
Specification of whether slopes beyond outer knots should
be fixed to 0. 
detrend 
If 
A matrix of dimension c(length(x),df) where either df
was
supplied or if knots
were supplied, df = length(knots) 
intercept
. Ns
returns a spline basis which is centered at
ref
. Ns
with the argument detrend=TRUE
returns a
spline basis which is orthogonal to cbind(1,x)
with respect to
the inner product defined by the positive definite matrix
diag(weight)
(an assumption which is checked).
The need for this function is primarily from analysis of rates in epidemiology and demography, where the dataset are timesplit records of followup, and the range of data therefore rarely is of any interest (let alone meaningful).
In Poisson modeling of rates based on timesplit records one should aim at having the same number of events between knots, rather than the same number of observations.
Bendix Carstensen b@bxc.dk, Lars Jorge D\'iaz, Steno Diabetes Center.
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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74  require(splines)
require(stats)
require(graphics)
ns( women$height, df = 3)
Ns( women$height, knots=c(63,59,71,67) )
# Gives the same results as ns:
summary( lm(weight ~ ns(height, df = 3), data = women) )
summary( lm(weight ~ Ns(height, df = 3), data = women) )
# 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 followup in 1year age intervals
dms < splitLexis( dml, time.scale="Age", breaks=0:100 )
summary( dms )
# Model agespecific rates using Ns with 6 knots
# and periodspecific RRs around 2000 with 4 knots
# with the same number of deaths between each pair of knots
n.kn < 6
( a.kn < with( subset(dms,lex.Xst=="Dead"),
quantile( Age+lex.dur, probs=(1:n.kn0.5)/n.kn ) ) )
n.kn < 4
( p.kn < with( subset( dms, lex.Xst=="Dead" ),
quantile( Per+lex.dur, probs=(1:n.kn0.5)/n.kn ) ) )
m1 < glm( lex.Xst=="Dead" ~ Ns( Age, kn=a.kn ) +
Ns( Per, kn=p.kn, ref=2000 ),
offset = log( lex.dur ),
family = poisson,
data = dms )
# Plot estimated agemortality curve for the year 2005 and knots chosen:
nd < data.frame( Age=seq(40,100,0.1), Per=2005, lex.dur=1000 )
par( mfrow=c(1,2) )
matplot( nd$Age, ci.pred( m1, newdata=nd ),
type="l", lwd=c(3,1,1), lty=1, col="black", log="y",
ylab="Mortality rates per 1000 PY", xlab="Age (years)", las=1, ylim=c(1,1000) )
rug( a.kn, lwd=2 )
# Clamped Age effect to the right of rightmost knot.
m1.c < glm( lex.Xst=="Dead" ~ Ns( Age, kn=a.kn, fixsl=c(FALSE,TRUE) ) +
Ns( Per, kn=p.kn, ref=2000 ),
offset = log( lex.dur ),
family = poisson,
data = dms )
# Plot estimated agemortality curve for the year 2005 and knots chosen.
matplot( nd$Age, ci.pred( m1.c, newdata=nd ),
type="l", lwd=c(3,1,1), lty=1, col="black", log="y",
ylab="Mortality rates per 1000 PY", xlab="Age (years)", las=1, ylim=c(1,1000) )
rug( a.kn, lwd=2 )
par( mfrow=c(1,1) )
# Including a linear Age effect of 0.05 to the right of rightmost knot.
m1.l < glm( lex.Xst=="Dead" ~ Ns( Age, kn=a.kn, fixsl=c(FALSE,TRUE) ) +
Ns( Per, kn=p.kn, ref=2000 ),
offset = log( lex.dur ) + pmax( Age, max( a.kn ) ) * 0.05,
family = poisson,
data = dms )
# Plot estimated agemortality curve for the year 2005 and knots chosen.
nd < data.frame(Age=40:100,Per=2005,lex.dur=1000)
matplot( nd$Age, ci.pred( m1.l, newdata=nd ),
type="l", lwd=c(3,1,1), lty=1, col="black", log="y",
ylab="Mortality rates per 1000 PY", xlab="Age (years)", las=1, ylim=c(1,1000) )
rug( a.kn, lwd=2 )

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