rstpm2 (version 1.2.2)

nsx: Generate a Basis Matrix for Natural Cubic Splines (with eXtensions)

Description

Generate the B-spline basis matrix for a natural cubic spline (with eXtensions).

Usage

nsx(x, df = NULL, knots = NULL, intercept = FALSE,
    Boundary.knots = range(x), derivs = if (cure) c(2, 1) else c(2, 2),
    log = FALSE, centre = FALSE, 
    cure = FALSE, stata.stpm2.compatible = FALSE)

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 + 4 - sum(derivs) knots at suitably chosen quantiles of x (which will ignore missing values).

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 mor

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 <

derivs

an integer vector of length 2 with values between 0 and 2 giving the derivative constraint order at the left and right boundary knots; an order of 2 constrains the second derivative to zero (f''(x)=0); an order of 1 constrains the first an

log

a Boolean indicating whether the underlying values have been log transformed; (deprecated: only used to calculate derivatives in rstpm2:::stpm2Old

centre

if specified, then centre the splines at this value (i.e. f(centre)=0) (default=FALSE)

cure

a Boolean indicated whether to estimate cure; changes the default derivs argument, such that the right boundary has the first and second derivatives constrained to zero; defaults to FALSE

stata.stpm2.compatible

a Boolean to determine whether to use Stata stpm's default knot placement; defaults to FALSE

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.nsx().

  nsx() is based on the functions ns and 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, else default to the extremes of the data. A primary use is in modeling formula to directly specify a natural spline term in a model.

  The extensions from ns are: specification of the derivative constraints at the boundary knots; whether to centre the knots; incorporation of cure using derivatives; compatible knots with Stata's stpm2; and an indicator for a log-transformation of x for calculating derivatives.

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.

See Also

ns, bs, predict.nsx, SafePrediction

Examples

require(stats); require(graphics); require(splines)
nsx(women$height, df = 5)
summary(fm1 <- lm(weight ~ ns(height, df = 5), data = women))

## 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)))