This function generates the B-spline basis matrix for a polynomial spline.

```
bSpline(x, df = NULL, knots = NULL, degree = 3L, intercept = FALSE,
Boundary.knots = range(x, na.rm = TRUE), ...)
```

x

The predictor variable. Missing values are allowed and will be returned as they were.

df

Degrees of freedom. One can specify `df`

rather than
`knots`

, then the function 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". If `knots`

was
specified, `df`

specified will be ignored.

knots

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

Non-negative integer degree of the piecewise polynomial. The
default value is 3 for cubic splines. Zero degree is allowed for this
function, which is the only difference compared with
`bs`

in package `splines`

.

intercept

If `TRUE`

, an intercept is included in the basis;
Default is `FALSE`

.

Boundary.knots

Boundary points at which to anchor the B-spline basis.
By default, they are 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`

.

...

Optional arguments for future usage.

A matrix of dimension `length(x)`

by
`df = degree + length(knots)`

(plus one if intercept is included).
Attributes that correspond to the arguments specified are returned
for usage of other functions in this package.

It is an augmented function of `bs`

in package
`splines`

for B-spline basis that allows piecewise constant (close on
the left, open on the right) spline basis with zero degree. When the
argument `degree`

is greater than zero, it internally calls
`bs`

and generates a basis matrix for representing the
family of piecewise polynomials with the specified interior knots and
degree, evaluated at the values of `x`

. The function has the same
arguments with `bs`

for ease usage.

`predict.bSpline2`

for evaluation at given (new) values;
`dbs`

, `deriv.bSpline2`

for derivatives;
`ibs`

for integral of B-splines;
`mSpline`

for M-splines;
`iSpline`

for I-splines;
`cSpline`

for C-splines.

# NOT RUN { library(splines2) x <- seq.int(0, 1, 0.01) knots <- c(0.3, 0.5, 0.6) bsMat <- bSpline(x, knots = knots, degree = 0, intercept = TRUE) library(graphics) matplot(x, bsMat, type = "l", ylab = "Piecewise constant B-spline bases") abline(v = knots, lty = 2, col = "gray") # }