Generate the B-spline basis matrix for a polynomial spline.
bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, Boundary.knots = range(x))
- the predictor variable. Missing values are allowed.
- degrees of freedom; one can specify
df-degree(minus one if there is an intercept) knots at suitable quantiles of
x(which will ignore missing values). The default,
NULL, corresponds to no inner knots, i.e.,
degree - intercept.
- the internal breakpoints that define the spline. The default is
NULL, which results in a basis for ordinary polynomial regression. Typical values are the mean or median for one knot, quantiles for more knots. See also
- degree of the piecewise polynomial---default is
3for cubic splines.
TRUE, an intercept is included in the basis; default is
- boundary points at which to 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
c(length(x), df), where either
df was supplied or if
knots were supplied,
df = length(knots) + degree plus one if there is an intercept. Attributes are returned that correspond to the arguments to
bs, and explicitly give the
Boundary.knots etc for use by
bs() is based on the function
spline.des(). It generates a basis matrix for representing the family of piecewise polynomials with the specified interior knots and degree, evaluated at the values of
x. A primary use is in modeling formulas to directly specify a piecewise polynomial 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.