Epi (version 2.0)

Ns: Natural splines - (cubic splines linear beyond outermost knots) with convenient specification of knots and possibility of centering and detrending.

Description

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.

Usage

Ns( x, ref = NULL, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots = NULL, detrend = FALSE )

Arguments

x

A variable.

ref

Scalar. Reference point on the x-scale, where the resulting effect will be 0.

df

degrees of freedom.

knots

knots to be used both as boundary and internal knots. If Boundary.knots are given, this will be taken as the set of internal knots.

intercept

Should the intercept be included in the resulting basis? Ignored if any of ref or detrend is given.

Boundary.knots

The boundary knots beyond which the spline is linear.

detrend

If TRUE, the columns of the spline basis will be projected to the orthogonal of cbind(1,x). Optionally detrend can be given as a vector of non-negative numbers used to define an inner product as diag(detrend) for projection on the orthogonal to cbind(1,x). The default is projection w.r.t. the inner product defined by the identity matrix.

Value

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).

Examples

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=dodm-dobth, DMdur=0 ),
               exit = list(Per=dox),
        exit.status = factor(!is.na(dodth),labels=c("DM","Dead")),
               data = DMlate )

# Split follow-up in 1-year age intervals
dms <- splitLexis( dml, time.scale="Age", breaks=0:100 )
summary( dms )

# Model  age-specific rates using Ns with 6 knots
# and period-specific 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.kn-0.5)/n.kn ) ) )
n.kn <- 4
( p.kn <- with( subset(dms,lex.Xst=="Dead"),
                quantile( Per+lex.dur, probs=(1:n.kn-0.5)/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 age-mortality curve for the year 2005 and knots chosen:
nd <- data.frame(Age=40:90,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 )
rug( a.kn, lwd=2 )