---
title: "A Short Introduction to splines2"
author: Wenjie Wang
date: "`r Sys.Date()`"
bibliography:
  - ../inst/bib/splines2.bib
vignette: >
  %\VignetteIndexEntry{A Short Introduction to splines2}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output:
  rmarkdown::html_vignette:
    number_sections: yes
    toc: yes
---


```{r setup, echo = FALSE}
knitr::opts_knit$set(global.par = TRUE)
knitr::opts_chunk$set(fig.width = 7, fig.height = 4)
```

```{r set-par, echo = FALSE}
library(graphics)
par(mar = c(2.5, 2.5, 0.5, 0.1), mgp = c(1.5, 0.5, 0))
```


# Introduction

The R package **splines2** is intended to be a user-friendly supplementary
package to the base package **splines**.
It provides functions to construct a variety of regression spline basis
functions that are not available from **splines**.
Most functions have a very similar user interface with the function
`splines::bs()`.
More specifically, **splines2** allows users to construct the basis functions of

- B-splines
- M-splines
- I-splines
- C-splines
- periodic splines
- natural cubic splines
- generalized Bernstein polynomials

along with their integrals (except C-splines) and derivatives of given order by
closed-form recursive formulas.


Compared to **splines**, the package **splines2** provides convenient interfaces
for spline derivatives with consistent handling on `NA`'s.
Most of the implementations are in *C++* with the help of **Rcpp** and
**RcppArmadillo** since v0.3.0, which boosted the computational performance.


In the remainder of this vignette, we illustrate the basic usage of most
functions in the package through examples.
We refer readers to [Wang and Yan (2021)](https://dx.doi.org/10.6339/21-JDS1020)
for a more formal introduction to the package with applications to
shape-restricted regression.
See the package manual for more details about function usage.

```{r load-lib}
library(splines2)
packageVersion("splines2")
```


# B-splines {#bSpline}

## B-spline Basis Functions

The `bSpline()` function generates the basis matrix for B-splines and extends
the function `bs()` of the package **splines** by providing 1) the piece-wise
constant basis functions when `degree = 0`, 2) the derivatives of basis
functions for a positive `derivs`, 3) the integrals of basis functions if
`integral = TRUE`, 4) periodic basis functions based on B-splines if `periodic =
TRUE`.


One example of linear B-splines with three internal knots is as follows:

```{r bSpline, fig.cap="B-splines of degree one with three internal knots placed at 0.3, 0.5, and 0.6."}
knots <- c(0.3, 0.5, 0.6)
x <- seq(0, 1, 0.01)
bsMat <- bSpline(x, knots = knots, degree = 1, intercept = TRUE)
plot(bsMat, mark_knots = "all")
```

## Integrals and Derivatives of B-splines

For convenience, the package also provides functions `ibs()` and `dbs()` for
constructing the B-spline integrals and derivatives, respectively.
Two toy examples are as follows:

```{r ibs, fig.cap="Piecewise linear B-splines (left) and their integrals (right)."}
ibsMat <- ibs(x, knots = knots, degree = 1, intercept = TRUE)
op <- par(mfrow = c(1, 2))
plot(bsMat, mark_knots = "internal")
plot(ibsMat, mark_knots = "internal")
abline(h = c(0.15, 0.2, 0.25), lty = 2, col = "gray")
```

```{r dbs, fig.cap="Cubic B-spline (left) and their first derivative (right)."}
bsMat <- bSpline(x, knots = knots, intercept = TRUE)
dbsMat <- dbs(x, knots = knots, intercept = TRUE)
plot(bsMat, mark_knots = "internal")
plot(dbsMat, mark_knots = "internal")
```

We may also obtain the derivatives easily by the `deriv()` method as follows:

```{r dbsMat}
is_equivalent <- function(a, b) {
    all.equal(a, b, check.attributes = FALSE)
}
stopifnot(is_equivalent(dbsMat, deriv(bsMat)))
```

## Periodic B-splines

The function `bSpline()` produces periodic spline basis functions following
@piegl1997nurbs [chapter 12] when `periodic = TRUE` is specified.
Different from the regular basis functions, the `x` is allowed to be placed
outside the boundary and the `Boundary.knots` defines the cyclic interval.
For instance, one may obtain the periodic cubic B-spline basis functions with
cyclic interval (0, 1) as follows:

```{r pbs}
px <- seq(0, 3, 0.01)
pbsMat <- bSpline(px, knots = knots, Boundary.knots = c(0, 1),
                  intercept = TRUE, periodic = TRUE)
ipMat <- ibs(px, knots = knots, Boundary.knots = c(0, 1),
             intercept = TRUE, periodic = TRUE)
dp1Mat <- deriv(pbsMat)
dp2Mat <- deriv(pbsMat, derivs = 2)
par(mfrow = c(1, 2))
plot(pbsMat, ylab = "Periodic B-splines", mark_knots = "boundary")
plot(ipMat, ylab = "The integrals", mark_knots = "boundary")
plot(dp1Mat, ylab = "The 1st derivatives", mark_knots = "boundary")
plot(dp2Mat, ylab = "The 2nd derivatives", mark_knots = "boundary")
```

For reference, the corresponding integrals and derivatives are also visualized.

# M-Splines {#mSpline}

## M-spline Basis Functions

M-splines [@ramsay1988monotone] can be considered the normalized version of
B-splines with unit integral within boundary knots.
An example given by @ramsay1988monotone was a quadratic M-splines with three
internal knots placed at 0.3, 0.5, and 0.6.
The default boundary knots are the range of `x`, and thus 0 and 1 in this
example.

```{r mSpline, fig.cap = "Quadratic M-spline with three internal knots placed at 0.3, 0.5, and 0.6."}
msMat <- mSpline(x, knots = knots, degree = 2, intercept = TRUE)
par(op)
plot(msMat, mark_knots = "all")
```

The derivative of the given order of M-splines can be obtained by specifying a
positive integer to argument `dervis` of `mSpline()`.
Similarly, for an existing `mSpline` object generated by `mSpline()`, one can
use the `deriv()` method for derivaitives.
For example, the first derivative of the M-splines given in the previous example
can be obtained equivalently as follows:

```{r mSpline-derivs}
dmsMat1 <- mSpline(x, knots = knots, degree = 2, intercept = TRUE, derivs = 1)
dmsMat2 <- deriv(msMat)
stopifnot(is_equivalent(dmsMat1, dmsMat2))
```

## Periodic M-Splines

The `mSpline()` function produces periodic splines based on M-spline basis
functions when `periodic = TRUE` is specified.
The `Boundary.knots` defines the cyclic interval, which is the same with the
periodic B-splines.

```{r pms-basis, fig.cap = "Cubic periodic M-splines."}
pmsMat <- mSpline(px, knots = knots, intercept = TRUE,
                  periodic = TRUE, Boundary.knots = c(0, 1))
plot(pmsMat, ylab = "Periodic Basis", mark_knots = "boundary")
```

We may still specify the argument `derivs` in `mSpline()` or use the
corresponding `deriv()` method to obtain the derivatives when `periodic = TRUE`.

```{r pms-deriv, fig.cap = "The first derivatives of the periodic M-splines."}
dpmsMat <- deriv(pmsMat)
plot(dpmsMat, ylab = "The 1st derivatives", mark_knots = "boundary")
```

Furthermore, we can obtain the integrals of the periodic M-splines by specifying
`integral = TRUE`.
The integral is integrated from the left boundary knot.

```{r pms-integral, fig.cap = "The integrals of the periodic M-splines."}
ipmsMat <- mSpline(px, knots = knots, intercept = TRUE,
                   periodic = TRUE, Boundary.knots = c(0, 1), integral = TRUE)
plot(ipmsMat, ylab = "Integrals", mark_knots = "boundary")
abline(h = seq.int(0, 3), lty = 2, col = "gray")
```


# I-Splines {#iSpline}

I-splines [@ramsay1988monotone] are simply the integral of M-splines and thus
monotonically nondecreasing with unit maximum value.
A monotonically nondecreasing (nonincreasing) function can be fitted by a linear
combination of I-spline basis functions with nonnegative (nonpositive)
coefficients *plus a constant*, where the coefficient of the constant is
unconstrained.

The example given by @ramsay1988monotone was the I-splines corresponding to the
quadratic M-splines with three internal knots placed at 0.3, 0.5, and 0.6.
Notice that the degree of I-splines is defined from the associated M-splines
instead of their polynomial degree.

```{r iSpline, fig.cap = "I-splines of degree two with three internal knots placed at 0.3, 0.5, and 0.6."}
isMat <- iSpline(x, knots = knots, degree = 2, intercept = TRUE)
plot(isMat, mark_knots = "internal")
```

The corresponding M-spline basis matrix can be obtained easily as the first
derivatives of the I-splines by the `deriv()` method.

```{r msMat}
stopifnot(is_equivalent(msMat, deriv(isMat)))
```

We may specify `derivs = 2` in the `deriv()` method for the second derivatives
of the I-splines, which are equivalent to the first derivatives of the
corresponding M-splines.

```{r dmsMat}
dmsMat3 <- deriv(isMat, 2)
stopifnot(is_equivalent(dmsMat1, dmsMat3))
```


# C-Splines {#cSpline}

Convex splines [@meyer2008inference] called C-splines are scaled integrals of
I-splines with unit maximum value at the right boundary knot.
@meyer2008inference applied C-splines to shape-restricted regression analysis.
The monotone (nondecreasing) property of I-spines ensures the convexity of
C-splines.
A convex regression function can be estimated using linear combinations of the
C-spline basis functions with nonnegative coefficients, plus an unconstrained
linear combination of a constant and an identity function $g(x)=x$.
If the underlying regression function is both increasing and convex, the
coefficient on the identity function is restricted to be nonnegative as well.

We may specify the argument `scale = FALSE` in the function `cSpline()` to
disable the scaling of the integrals of I-splines.
Then the actual integrals of the corresponding I-splines will be returned.
If `scale = TRUE` (by default), each C-spline basis is scaled to have unit
height at the right boundary knot.


```{r cSpline-scaled, fig.cap = "C-splines of degree two with three internal knots placed at 0.3, 0.5, and 0.6."}
csMat1 <- cSpline(x, knots = knots, degree = 2, intercept = TRUE)
plot(csMat1)
abline(h = 1, v = knots, lty = 2, col = "gray")
```

Similarly, the `deriv()` method can be used to obtain the derivatives.
A nested call of `deriv()` is supported for derivatives of a higher order.
However, the argument `derivs` of the `deriv()` method can be specified directly
for better computational performance.
For example, the first and second derivatives can be obtained by the following
equivalent approaches, respectively.

```{r cSpline-not-scaled}
csMat2 <- cSpline(x, knots = knots, degree = 2, intercept = TRUE, scale = FALSE)
stopifnot(is_equivalent(isMat, deriv(csMat2)))
stopifnot(is_equivalent(msMat, deriv(csMat2, 2)))
stopifnot(is_equivalent(msMat, deriv(deriv(csMat2))))
```

# Generalized Bernstein Polynomials

The Bernstein polynomials are equivalent to B-splines without internal knots and
have also been applied to shape-constrained regression analysis [e.g.,
@wang2012csda].
The $i$-th basis of the generalized Bernstein polynomials of degree $n$ over
$[a, b]$ is defined as follows: $$ B_i^n(x)=\frac{1}{(b-a)^n}{n\choose i}(x-a)^i
(b-x)^{n-i},~i\in\{0,\ldots,n\}, $$ where $a\le x\le b$.
It reduces to regular Bernstein polynomials defined over $[0, 1]$ when $a = 0$
and $b = 1$.


We may obtain the basis matrix of the generalized using the function
`bernsteinPoly()`.
For example, the Bernstein polynomials of degree 4 over $[0, 1]$ and is
generated as follows:

```{r bp-1, fig.cap = "Bernstein polynomials of degree 4 over [0, 1] (left) and the generalized version over [- 1, 1] (right)."}
x1 <- seq.int(0, 1, 0.01)
x2 <- seq.int(- 1, 1, 0.01)
bpMat1 <- bernsteinPoly(x1, degree = 4, intercept = TRUE)
bpMat2 <- bernsteinPoly(x2, degree = 4, intercept = TRUE)
par(mfrow = c(1, 2))
plot(bpMat1)
plot(bpMat2)
```

In addition, we may specify `integral = TRUE` or `derivs = 1` in
`bernsteinPoly()` for their integrals or first derivatives, respectively.

```{r bp-2, fig.height=6, fig.cap = "The integrals (upper panel) and the first derivatives (lower panel) of Bernstein polynomials of degree 4."}
ibpMat1 <- bernsteinPoly(x1, degree = 4, intercept = TRUE, integral = TRUE)
ibpMat2 <- bernsteinPoly(x2, degree = 4, intercept = TRUE, integral = TRUE)
dbpMat1 <- bernsteinPoly(x1, degree = 4, intercept = TRUE, derivs = 1)
dbpMat2 <- bernsteinPoly(x2, degree = 4, intercept = TRUE, derivs = 1)
par(mfrow = c(2, 2))
plot(ibpMat1, ylab = "Integrals")
plot(ibpMat2, ylab = "Integrals")
plot(dbpMat1, ylab = "Derivatives")
plot(dbpMat2, ylab = "Derivatives")
```

Similarly, we may also use the `deriv()` method to get derivatives of an
existing `bernsteinPoly` object.

```{r bp-deriv}
stopifnot(is_equivalent(dbpMat1, deriv(bpMat1)))
stopifnot(is_equivalent(dbpMat2, deriv(bpMat2)))
stopifnot(is_equivalent(dbpMat1, deriv(ibpMat1, 2)))
stopifnot(is_equivalent(dbpMat2, deriv(ibpMat2, 2)))
```


# Natural Cubic Splines

## Nonnegative Natural Cubic Basis Functions

The package provides two variants of the natural cubic splines that can be
constructed by `naturalSpline()` and `nsk()`, respectively, both of which are
different from `splines::ns()`.


The `naturalSpline()` function returns nonnegative basis functions (within the
boundary) for natural cubic splines by utilizing a closed-form null space
derived from the second derivatives of cubic B-splines.
When `integral = TRUE`, the function `naturalSpline()` returns the integral of
each natural spline basis.

```{r ns-basis, fig.cap = "Nonnegative natural cubic splines (left) and corresponding integrals (right)."}
nsMat <- naturalSpline(x, knots = knots, intercept = TRUE)
insMat <- naturalSpline(x, knots = knots, intercept = TRUE, integral = TRUE)
par(mfrow = c(1, 2))
plot(nsMat, ylab = "Basis")
plot(insMat, ylab = "Integrals")
stopifnot(is_equivalent(nsMat, deriv(insMat)))
```

Similarly, one may directly specify the argument `derivs` in `naturalSpline()`
or use the corresponding `deriv()` method to obtain the derivatives of spline
basis functions.

```{r ns-deriv, fig.cap = "The derivatives of natural cubic splines."}
d1nsMat <- naturalSpline(x, knots = knots, intercept = TRUE, derivs = 1)
d2nsMat <- deriv(nsMat, 2)
matplot(x, d1nsMat, type = "l", ylab = "The 1st derivatives")
matplot(x, d2nsMat, type = "l", ylab = "The 2nd derivatives")
```

## Natural Cubic Basis Functions with Unit Heights at Knots

The function `nsk()` produces another variant of natural cubic splines, where
only one of the spline basis functions is nonzero with unit height at every
boundary and internal knot.
As a result, the coefficients of the basis functions are the values of the
spline function at the knots, which makes it more straightforward to interpret
the coefficient estimates.
This idea originated from the function `nsk()` of the **survival** package
(introduced in version 3.2-8).
The implementation of the `nsk()` of **splines2** essentially follows the
`survival::nsk()` function.
One noticeable argument for `nsk()` is `trim` (equivalent to the argument `b` of
`survival::nsk()`).
One may specify `trim = 0.05` to exclude 5% of the data from both sides when
setting the boundary knots, which can be a more sensible choice in practice due
to possible outliers.
The `trim` argument is also available for `naturalSpline()`, which however is
zero by default for backward compatibility.
An illustration of the basis functions generated by `nsk()` is as follows:

```{r nsk}
nskMat <- nsk(x, knots = knots, intercept = TRUE)
par(op)
plot(nskMat, ylab = "nsk()", mark_knots = "all")
abline(h = 1, col = "red", lty = 3)
```

We can visually verify that only one basis function takes a value of one at each
knot.

# Helper and Alias Functions

## Update Spline's Specification by `update()` {#update}

The `update()` function is an S3 method to generate spline basis functions with
new `x`, `degree`, `knots`, or `df` specifications.
The first argument is an existing `splines2` object and additional named
arguments will be passed to the corresponding functions to update the spline
basis functions.
Suppose we want to add two more knots to `nskMat` for natural cubic spline basis
functions and exclude 5% of the data from both sides in total when placing the
boundary knots.
We can utilize the `update()` method as follows:

```{r update-nsk}
nskMat2 <- update(nskMat, knots = c(knots, 0.2, 0.4), trim = 0.025)
knots(nskMat2)
stopifnot(all.equal(quantile(x, c(0.025, 0.975), names = FALSE),
                    knots(nskMat2, "boundary")))
```

## Evaluation by `predict()` {#predict}

The `predict()` method for `splines2` objects allows one to evaluate the spline
function if a coefficient vector is specified via the `coef` argument.
In addition, it internally calls the `update()` method to update the basis
functions before computing the spline function, which can be useful to get the
derivatives of the spline function.
If the `coef` argument is not specified, the `predict()` method will be
equivalent to the `update()` method.
For instance, we can compute the first derivative of the I-spline function from
the previous example with a coefficient vector `seq(0.1, by = 0.1, length.out =
ncol(isMat))` at $x = (0.275, 0.525, 0.8)$ as follows:

```{r predict}
new_x <- c(0.275, 0.525, 0.8)
names(new_x) <- paste0("x=", new_x)
(isMat2 <- predict(isMat, newx = new_x)) # the basis functions at new x
stopifnot(all.equal(predict(isMat, newx = new_x), update(isMat, x = new_x)))
## compute the first derivative of the I-spline function in different ways
beta <- seq(0.1, by = 0.1, length.out = ncol(isMat))
deriv_ispline1 <- predict(isMat, newx = new_x, coef = beta, derivs = 1)
deriv_ispline2 <- predict(update(isMat, x = new_x, derivs = 1), coef = beta)
deriv_ispline3 <- c(predict(deriv(isMat), newx = new_x) %*% beta)
stopifnot(all.equal(deriv_ispline1, deriv_ispline2))
stopifnot(all.equal(deriv_ispline2, deriv_ispline3))
```

## Visualization by `plot()` {#plot}

As one may notice in the previous examples, we may visualize the spline basis
functions easily with the `plot()` method.
By default, the spline basis functions are visualized at 101 equidistant grid
points within the range of `x`, which can be tweaked by arguments `from`, `to`,
and `n`.
In addition, we can visualize the placement of knots by vertical lines through
the argument `mark_knots`.
The available options are `"none"`, `"internal"`, `"boundary"`, and `"all"`.


## Including Spline Basis Functions in Model Formulas

It is common to directly include spline basis functions in a model formula.
To avoid a lengthy model formula, the package provides alias functions that are
summarized in the following table:

| Function          | Equivalent Alias |
|-------------------|------------------|
| `bSpline()`       | `bsp()`          |
| `mSpline()`       | `msp()`          |
| `iSpline()`       | `isp()`          |
| `cSpline()`       | `csp()`          |
| `bernsteinPoly()` | `bpoly()`        |
| `naturalSpline()` | `nsp()`          |

One may create new alias functions.
For example, we can create a new alias function simply named `b()` for B-splines
and obtain equivalent models as follows:

```{r formula-alias}
b <- bSpline # create an alias for B-splines
mod1 <- lm(weight ~ b(height, degree = 1, df = 3), data = women)
iknots <- with(women, knots(bSpline(height, degree = 1, df = 3)))
mod2 <- lm(weight ~ bSpline(height, degree = 1, knots = iknots), data = women)
pred1 <- predict(mod1, head(women, 10))
pred2 <- predict(mod2, head(women, 10))
stopifnot(all.equal(pred1, pred2))
```

Nevertheless, there is a possible pitfall when using a customized wrapper
function for spline basis functions along with a data-dependent placement of
knots.
When we make model predictions for a given new data, the placement of the
internal/boundary knots can be different from the original placement that
depends on the training set.
As a result, the spline basis functions generated for prediction may not be the
same as the counterparts used in the model fitting.
A simple example is as follows:

```{r formula-wrap-failed}
## generates quadratic spline basis functions based on log(x)
qbs <- function(x, ...) {
    splines2::bSpline(log(x), ..., degree = 2)
}
mod3 <- lm(weight ~ qbs(height, df = 5), data = women)
mod4 <- lm(weight ~ bsp(log(height), degree = 2, df = 5), data = women)
stopifnot(all.equal(unname(coef(mod3)), unname(coef(mod4)))) # the same coef
pred3 <- predict(mod3, head(women, 10))
pred4 <- predict(mod4, head(women, 10))
all.equal(pred3, pred4)
pred0 <- predict(qbs(women$height, df = 5),
                 newx = head(log(women$height), 10),
                 coef = coef(mod3)[- 1]) + coef(mod3)[1]
stopifnot(all.equal(pred0, pred4, check.names = FALSE))
```

Although the coefficient estimates are the same, the prediction results by using
the `predict.lm()` differ.
Using an alias function in the model formula produces correct results.


To resolve this issue, we can create an S3 method for `stats::makepredictcall()` as follows:

```{r predict-qbs}
## generates quadratic spline basis functions based on log(x) with a new class
qbs <- function(x, ...) {
    res <- splines2::bSpline(log(x), ..., degree = 2)
    class(res) <- c("qbs", class(res))
    return(res)
}
## a utility to help model.frame() create the right matrices
makepredictcall.qbs <- function(var, call) {
    if (as.character(call)[1L] == "qbs" ||
    (is.call(call) && identical(eval(call[[1L]]), qbs))) {
        at <- attributes(var)[c("knots", "Boundary.knots", "intercept",
                                "periodic", "derivs", "integral")]
        call <- call[1L:2L]
        call[names(at)] <- at
    }
    call
}
## the same example
mod3 <- lm(weight ~ qbs(height, df = 5), data = women)
mod4 <- lm(weight ~ bsp(log(height), degree = 2, df = 5), data = women)
stopifnot(all.equal(unname(coef(mod3)), unname(coef(mod4)))) # the same coef
pred3 <- predict(mod3, head(women, 10))
pred4 <- predict(mod4, head(women, 10))
all.equal(pred3, pred4) # should be TRUE this time
```

## Extract Specifications by `$`

The basis specifications are saved as attributes of the returned *splines2*
objects, which means that we can extract one of the specifications by `attr()`.
Alternatively, we can treat *splines2* objects as lists and use the
corresponding `$` method.
For example, it is straightforward to extract the specified `trim` of `nskMat2`
by `attr(nskMat2, "trim")` or simply `nskMat2$trim`.

```{r extract}
c(nskMat2$trim, attr(nskMat2, "trim"))
```


# Reference {-}
