nsk: Natural splines with knot heights as the basis.

Description

Create the design matrix for a natural spline, such that the coefficient of the resulting fit are the values of the function at the knots.

Usage

nsk(x, df = NULL, knots = NULL, intercept = FALSE, b = 0.05, 
    Boundary.knots = quantile(x, c(b, 1 - b), na.rm = TRUE))

Arguments

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).

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

default placement of the boundary knots. A value of bs=0 will replicate the default behavior of ns.

Boundary.knots

boundary points at which to impose the natural boundary conditions and anchor the B-spline basis. Beyond these points the function is assumed to be linear. If both knots and Boundary.knots are supplied, the basis parameters do not depend on x. Data can extend beyond Boundary.knots

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 kns, and explicitly give the knots, Boundary.knots etc for use by predict.kns().

Details

The nsk function behaves identically to the ns function, with two exceptions. The primary one is that the returned basis is such that coefficients correspond to the value of the fitted function at the knot points. If intercept = FALSE, there will be k-1 coefficients corresponding to the k knots, and they will be the difference in predicted value between knots 2-k and knot 1. The primary advantage to the basis is that the coefficients are directly interpretable. A second is that tests for the linear and non-linear components are simple contrasts.

The second differnce with ns is one of opinion with respect to the default position for the boundary knots. The default here is closer to that found in the rms::rcs function.

Examples

Run this code

# NOT RUN {
# make some dummy data
tdata <- data.frame(x= lung$age, y = 10*log(lung$age-35) + rnorm(228, 0, 2))
fit1 <- lm(y ~ -1 + nsk(x, df=4, intercept=TRUE) , data=tdata)
fit2 <- lm(y ~ nsk(x, df=3), data=tdata)

# the knots (same for both fits)
knots <- unlist(attributes(fit1$model[[2]])[c('Boundary.knots', 'knots')])
knots
unname(coef(fit1))  # predictions at the knot points

unname(coef(fit1)[-1] - coef(fit1)[1])  # differences: yhat[2:4] - yhat[1]
unname(coef(fit2))

# }
# NOT RUN {
plot(y ~ x, data=tdata)
points(sort(knots), coef(fit1), col=2, pch=19)
coef(fit)[1] + c(0, coef(fit)[-1])
# }