Fabian Scheipl — written Mar 21, 2014 — source

Currie, Durban and Eilers write:

Data with an array structure are common in statistics, and the design or regression matrix for analysis of such data can often be written as a Kronecker product. Factorial designs, contingency tables and smoothing of data on multidimensional grids are three such general classes of data and models. In such a setting, we develop an arithmetic of arrays which allows us to define the expectation of the data array as a sequence of nested matrix operations on a coefficient array. We show how this arithmetic leads to low storage, high speed computation in the scoring algorithm of the generalized linear model.

For example, they show that if a design matrix `X`

has the Kronecker
structure `X = kronecker(Xd, ..., X2, X1)`

with `X<i>`

a partial model matrix
with `n<i>`

rows and `c<i>`

columns, linear functions `X%*%theta`

of `X`

and
a coefficient vector `theta`

can be efficiently computed based only on the
partial model matrices where the entries in the vector `X%*%theta`

(with
`nd*...*n2*n1`

entries) are the same as the entries in the array with
dimension `c(n1, n2, ..., nd)`

returned by ```
RH(Xd, ... , RH(X2, RH(X1,
Theta))...)
```

.

`Theta`

is an array with dimensions `c(c1, c2, ..., cd)`

containing `theta`

and `RH(X, A)`

– the “rotated H-transform” – is an operation generalizing
transposed pre-multiplication `t(X %*% A)`

of a matrix `A`

by a matrix `X`

to
the case of higher dimensional array-valued `A`

.

The code below implements a simple array class for numeric arrays and the
rotated H-transform in `RcppArmadillo`

and compares the performance to both
the naive straight forward matrix multiplication based on the full model
matrix and an `R`

-implementation of `RH()`

:

**Set up test case:**

Let’s look at a 3-dimensional example where `X = X3 %x% X2 %x% X1`

and
each `X<i>`

is a B-spline basis over `seq(0, 1, len=n<i>)`

:

Note that `X`

is fairly large, with 6 × 10^{4} rows and 750 columns.

**Check correctness:**

[1] TRUE

[1] TRUE

**Check performance:**

test elapsed relative 1 array(X %*% theta_vec, dim = c(n1, n2, n3)) 27.252 198.920 3 Reduce(RH, list(X3, X2, X1), Theta, TRUE) 0.137 1.000 2 Reduce(RH_r, list(X3, X2, X1), Theta, TRUE) 0.325 2.372

*Note:* An alternative version with proper formula notation can be found here.

- Constructing a Sparse Matrix Class in Rcpp — Zach DeBruine and Dirk Eddelbuettel
- Using iconv via the R exported header — Dirk Eddelbuettel
- Using Scheme from R via Guile and Rcpp — Dirk Eddelbuettel