This function just calls `ns()`

from the
**splines**
package.

`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`