# ns

0th

Percentile

##### Generate a Basis Matrix for Natural Cubic Splines

Generate the B-spline basis matrix for a natural cubic spline.

Keywords
smooth
##### Usage
ns(x, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots = range(x))
##### Arguments
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 = 1, corresponds to no 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
##### Details

ns is based on the function spline.des. It generates a basis matrix for representing the family of piecewise-cubic splines with the specified sequence of interior knots, and the natural boundary conditions. These enforce the constraint that the function is linear beyond the boundary knots, which can either be supplied or default to the extremes of the data.

A primary use is in modeling formula to directly specify a natural spline term in a model: see the examples.

##### Value

A matrix of dimension length(x) * df where either df was supplied or if knots were supplied, df = length(knots) + 1 + intercept. Attributes are returned that correspond to the arguments to ns, and explicitly give the knots, Boundary.knots etc for use by predict.ns().

##### 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.

bs, predict.ns, SafePrediction
library(splines) require(stats); require(graphics) ns(women\$height, df = 5) summary(fm1 <- lm(weight ~ ns(height, df = 5), data = women)) ## To see what knots were selected attr(terms(fm1), "predvars") ## 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)))