B-Spline Basis for Polynomial Splines

This function just calls bs() from the splines package.

Usage

bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE,
  Boundary.knots = range(x), warn.outside = TRUE)

x: the predictor variable. Missing values are allowed.
df: degrees of freedom; one can specify df rather than knots; bs() then chooses df-degree (minus one if there is an intercept) knots at suitable quantiles of x (which will ignore missing values). The default, NULL, takes the number of inner knots as length(knots). If that is zero as per default, that corresponds to df = degree - intercept.
knots: 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 Boundary.knots.
degree: degree of the piecewise polynomial—default is 3 for cubic splines.
intercept: if TRUE, an intercept is included in the basis; default is FALSE.
Boundary.knots: boundary points at which to anchor the B-spline basis (default the range of the non-NA data). If both knots and Boundary.knots are supplied, the basis parameters do not depend on x. Data can extend beyond Boundary.knots.
warn.outside: logical indicating if a warning should be signalled in case some x values are outside the boundary knots.