| bs {splines} | R Documentation |
B-Spline Basis for Polynomial Splines
Description
Generate the B-spline basis matrix for a polynomial spline.
Usage
bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE,
Boundary.knots = range(x))
Arguments
x |
the predictor variable. Missing values are allowed. |
df |
degrees of freedom; one can specify |
knots |
the internal breakpoints that define the
spline. The default is |
degree |
degree of the piecewise polynomial—default is 3 for cubic splines. |
intercept |
if |
Boundary.knots |
boundary points at which to anchor the B-spline
basis (default the range of the data). If both |
Value
A matrix of dimension length(x) * df, where either df
was supplied or if knots were supplied,
df = length(knots) + 3 + intercept. Attributes are returned
that correspond to the arguments to bs, and explicitly give
the knots, Boundary.knots etc for use by
predict.bs().
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.
References
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.
See Also
ns, poly, smooth.spline,
predict.bs, SafePrediction
Examples
require(stats); require(graphics)
bs(women$height, df = 5)
summary(fm1 <- lm(weight ~ bs(height, df = 5), data = women))
## example of safe prediction
plot(women, xlab = "Height (in)", ylab = "Weight (lb)")
ht <- seq(57, 73, length.out = 200)
lines(ht, predict(fm1, data.frame(height=ht)))
## Not run:
## Consistency:
x <- c(1:3,5:6)
stopifnot(identical(bs(x), bs(x, df = 3)),
!is.null(kk <- attr(bs(x), "knots")),# not true till 1.5.1
length(kk) == 0)
## End(Not run)