Constructs the discrete derivative matrix of a given order, with respect to given design points.
Arguments
- k
Order for the discrete derivative matrix. Must be >= 0.
- xd
Design points. Must be sorted in increasing order, and have length at least
k+1.- tf_weighting
Should "trend filtering weighting" be used? This is a weighting of the discrete derivatives that is implicit in trend filtering; see details for more information. The default is
FALSE.- row_idx
Vector of indices, a subset of
1:(n-k)wheren = length(xd), that indicates which rows of the constructed matrix should be returned. The default isNULL, which is taken to mean1:(n-k).
Details
The discrete derivative matrix of order \(k\), with respect to design points \(x_1 < \ldots < x_n\), is denoted \(D^k_n\). It has dimension \((n-k) \times n\), and is banded with bandwidth \(k+1\). It can be constructed recursively, as follows. We first define the \((n-1) \times n\) first difference matrix \(\bar{D}_n\): $$ \bar{D}_n = \left[\begin{array}{rrrrrr} -1 & 1 & 0 & \ldots & 0 & 0 \\ 0 & -1 & 1 & \ldots & 0 & 0 \\ \vdots & & & & & \\ 0 & 0 & 0 & \ldots & -1 & 1 \end{array}\right], $$ and for \(k \geq 1\), define the \((n-k) \times (n-k)\) diagonal weight matrix \(W^k_n\) to have diagonal entries \((x_{i+k} - x_i) / k\), \(i = 1,\ldots,n-k\). The \(k\)th order discrete derivative matrix \(D^k_n\) is then given by the recursion: $$ \begin{aligned} D^1_n &= (W^1_n)^{-1} \bar{D}_n, \\ D^k_n &= (W^k_n)^{-1} \bar{D}_{n-k+1} \, D^{k-1}_n, \quad \text{for $k \geq 2$}. \end{aligned} $$ We note that \(\bar{D}_{n-k+1}\) above denotes the \((n-k) \times (n-k+1)\) version of the first difference matrix that is defined in the second-to-last display.
The option tf_weighting = TRUE returns \(W^k_n D^k_n\) where \(W^k_n\)
is the \((n-k) \times (n-k)\) diagonal matrix as described above. This
weighting is implicit in trend filtering, as explained in the help file for
d_mat_mult(). See also Section 6.1 of Tibshirani (2020) for further
discussion.
Note: For multiplication of a given vector by \(D^k_n\), instead of
forming \(D^k_n\) with the current function and then carrying out the
multiplication, one should instead use d_mat_mult(), as this will be more
efficient (both will be linear time, but the latter saves the cost of
forming any matrix in the first place).
References
Tibshirani (2020), "Divided differences, falling factorials, and discrete splines: Another look at trend filtering and related problems", Section 6.1.
See also
b_mat() for constructing the extended discrete derivative matrix,
and d_mat_mult() for multiplying by the discrete derivative matrix.
Examples
d_mat(2, 1:10)
#> 8 x 10 sparse Matrix of class "dgCMatrix"
#>
#> [1,] 1 -2 1 . . . . . . .
#> [2,] . 1 -2 1 . . . . . .
#> [3,] . . 1 -2 1 . . . . .
#> [4,] . . . 1 -2 1 . . . .
#> [5,] . . . . 1 -2 1 . . .
#> [6,] . . . . . 1 -2 1 . .
#> [7,] . . . . . . 1 -2 1 .
#> [8,] . . . . . . . 1 -2 1
d_mat(2, 1:10 / 10)
#> 8 x 10 sparse Matrix of class "dgCMatrix"
#>
#> [1,] 100 -200 100 . . . . . . .
#> [2,] . 100 -200 100 . . . . . .
#> [3,] . . 100 -200 100 . . . . .
#> [4,] . . . 100 -200 100 . . . .
#> [5,] . . . . 100 -200 100 . . .
#> [6,] . . . . . 100 -200 100 . .
#> [7,] . . . . . . 100 -200 100 .
#> [8,] . . . . . . . 100 -200 100
d_mat(2, 1:10, row_idx = 2:5)
#> 4 x 10 sparse Matrix of class "dgCMatrix"
#>
#> [1,] . 1 -2 1 . . . . . .
#> [2,] . . 1 -2 1 . . . . .
#> [3,] . . . 1 -2 1 . . . .
#> [4,] . . . . 1 -2 1 . . .