Generate the B-spline basis matrix for a natural cubic spline.
ns(x, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots = range(x))
- the predictor variable. Missing values are allowed.
- degrees of freedom. One can supply
dfrather than knots;
df - 1 - interceptknots at suitably chosen quantiles of
x(which will ignore missing values). The default,
df = 1, corresponds to no 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
TRUE, an intercept is included in the basis; default is
- boundary points at which to impose the natural boundary conditions and anchor the B-spline basis (default the range of the data). If both
Boundary.knotsare supplied, the basis parameters do not depend on
x. Data can extend beyond
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
Boundary.knots etc for use by
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.
Documentation reproduced from R 2.15.3. License: GPL-2.