Generate a Basis Matrix for Natural Cubic Splines

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

Usage

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). The default, df = NULL, sets the number of inner knots as length(knots).
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