This function just calls ns() from the
splines
package.
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 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).
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.
if TRUE, an intercept is included in the
basis; default is FALSE.
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