# mSpline

##### M-Spline Basis for Polynomial Splines and its Derivatives

This function generates the monotone regression spline (or simply called M-spline) basis matrix for a polynomial spline or its derivatives of given order.

##### Usage

```
mSpline(x, df = NULL, knots = NULL, degree = 3L, intercept = FALSE,
Boundary.knots = range(x, na.rm = TRUE), derivs = 0L, ...)
```

##### Arguments

- 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".- 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 piecewise constant basis.

- intercept
If

`TRUE`

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

.- Boundary.knots
Boundary points at which to anchor the M-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`

.- derivs
A non-negative integer specifying the order of derivatives of M-splines. The default value is

`0L`

for M-spline bases.- ...
Optional arguments for future usage.

##### Details

It is an implementation of the close form M-spline basis based on
relationship between M-spline basis and B-spline basis. In fact, M-spline
basis is a rescaled version of B-spline basis. Internally, it calls function
`bSpline`

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`

.

##### Value

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.

##### References

Ramsay, J. O. (1988). Monotone regression splines in action.
*Statistical science*, 3(4), 425--441.

##### See Also

`predict.mSpline`

for evaluation at given (new) values;
`deriv.mSpline`

for derivative method;
`bSpline`

for B-splines;
`iSpline`

for I-splines;
`cSpline`

for C-splines.

##### Examples

```
# NOT RUN {
## Example given in the reference paper by Ramsay (1988)
library(splines2)
x <- seq.int(0, 1, 0.01)
knots <- c(0.3, 0.5, 0.6)
msMat <- mSpline(x, knots = knots, degree = 2, intercept = TRUE)
library(graphics)
matplot(x, msMat, type = "l", ylab = "M-spline basis")
abline(v = knots, lty = 2, col = "gray")
## derivatives of M-splines
dmsMat <- mSpline(x, knots = knots, degree = 2,
intercept = TRUE, derivs = 1)
## or using the 'deriv' method
dmsMat1 <- deriv(msMat)
stopifnot(all.equal(dmsMat, dmsMat1, check.attributes = FALSE))
# }
```

*Documentation reproduced from package splines2, version 0.2.5, License: GPL (>= 3)*