| ns {splines} | R Documentation |
Generate the B-spline basis matrix for a natural cubic spline.
ns(x, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots = range(x))
x |
the predictor variable. Missing values are allowed. |
df |
degrees of freedom. One can supply df rather than
knots; ns() then chooses df - 1 - intercept knots at
suitably chosen quantiles of x (which will ignore missing values). |
knots |
breakpoints that define the spline. The default is no
knots; together with the natural boundary conditions this results in
a basis for linear regression on x. Typical values are the
mean or median for one knot, quantiles for more knots. See also
Boundary.knots. |
intercept |
if TRUE, an intercept is included in the
basis; default is FALSE. |
Boundary.knots |
boundary points at which to impose the natural
boundary conditions and anchor the B-spline basis (default the range
of the data). If both knots and Boundary.knots are
supplied, the basis parameters do not depend on x. Data can
extend beyond Boundary.knots |
A matrix of dimension length(x) * df where either df was
supplied or if knots were supplied,
df = length(knots) + 1 + intercept.
Attributes are returned that correspond to the arguments to ns,
and explicitly give the knots, Boundary.knots etc for
use by predict.ns().
ns() is based on the function spline.des. It
generates a basis matrix for representing the family of
piecewise-cubic splines with the specified sequence of
interior knots, and the natural boundary conditions. These enforce
the constraint that the function is linear beyond the boundary knots,
which can either be supplied, else default to the extremes of the
data. A primary use is in modeling formula to directly specify a
natural spline term in a model.
Hastie, T. J. (1992) Generalized additive models. Chapter 7 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
bs, predict.ns, SafePrediction
ns(women$height, df = 5) summary(fm1 <- lm(weight ~ ns(height, df = 5), data = women)) ## example of safe prediction plot(women, xlab = "Height (in)", ylab = "Weight (lb)") ht <- seq(57, 73, len = 200) lines(ht, predict(fm1, data.frame(height=ht)))