Generate the B-spline basis matrix for a polynomial spline with derivative restrictions at the boundary knots.
bsx(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE,
Boundary.knots = range(x), deriv = NULL)the predictor variable. Missing values are allowed.
degrees of freedom; one can specify df rather than knots; bs() then chooses
df-degree (minus one if there is an intercept) knots at suitable quantiles of x
(which will ignore missing values). The default, NULL, corresponds to no inner knots,
i.e., degree-intercept.
the internal breakpoints that define the spline. The default is NULL, which results
in a basis for ordinary polynomial regression. Typical values are the mean or median for one knot,
quantiles for more knots. See also Boundary.knots.
degree of the piecewise polynomial<U+2014>default is 3 for cubic splines.
if TRUE, an intercept is included in the basis; default is FALSE.
boundary points at which to anchor the B-spline basis (default the range of the non-NA data).
If both knots and Boundary.knots are supplied, the basis parameters do not depend on x.
Data can extend beyond Boundary.knots.
an integer vector of length 2 with values between 0 and degree + 1 giving the
derivative constraint order at the left and right boundary knots;
an order of 2 constrains the second derivative to zero (f<U+201D>(x)=0);
an order of 1 constrains the first and second derivatives to zero (f'(x)=f<U+201D>(x)=0);
an order of 0 constrains the zero, first and second derivatives to zero (f(x)=f'(x)=f<U+201D>(x)=0)
An order of degree + 1 computes the basis matrix similarly to bs.
A matrix with containing the basis functions evaluated in x.