bigPLSR_stream_kstats

Compute the column means and grand mean of the kernel matrix \(K(X, X)\)
without materialising it in memory. The input design matrix must be stored as
a <code>bigmemory::big.matrix</code> (or descriptor), and the kernel is evaluated by
iterating over row/column chunks.

Fast partial least squares (PLS) for dense and out-of-core data.
Provides SIMPLS (straightforward implementation of a statistically inspired
modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers,
plus kernel-style PLS variants ('kernelpls' and 'widekernelpls') with parity to 'pls'. Optimized for
'bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms)
(XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic
testing helpers. Includes an auto-selection strategy that chooses between
XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable
memory budget. About the package, Bertrand and Maumy (2023) <https://hal.science/hal-05352069>,
and <https://hal.science/hal-05352061> highlighted fitting and cross-validating
PLS regression models to big data. For more details about some of the techniques
featured in the package, Dayal and MacGregor (1997)
<doi:10.1002/(SICI)1099-128X(199701)11:1%3C73::AID-CEM435%3E3.0.CO;2-%23>,
Rosipal & Trejo (2001) <https://www.jmlr.org/papers/v2/rosipal01a.html>,
Tenenhaus, Viennet, and Saporta (2007) <doi:10.1016/j.csda.2007.01.004>,
Rosipal (2004) <doi:10.1007/978-3-540-45167-9_17>,
Rosipal (2019) <https://ieeexplore.ieee.org/document/8616346>,
Song, Wang, and Bai (2024) <doi:10.1016/j.chemolab.2024.105238>.
Includes kernel logistic PLS with 'C++'-accelerated alternating iteratively
reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS)
solvers with reusable centering statistics, and bootstrap
diagnostics with graphical summaries for coefficients, scores, and
cross-validation workflows, alongside dedicated plotting utilities for
individuals, variables, ellipses, and biplots.
The streaming backend uses far less memory and keeps memory bounded across data sizes.
For PLS1, streaming is often fast enough while preserving a small memory footprint;
for PLS2 it remains competitive with a bounded footprint.
On small problems that fit comfortably in RAM (random-access memory), dense in-memory
solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates.

Frederic Bertrand

bigPLSR

Partial Least Squares Regression Models with Big Matrices

Myriam Maumy

bigPLSR_stream_kstats function

<dl><dt>Xbm</dt>
<dd>A <code>bigmemory::big.matrix</code> (or descriptor) containing the
training design matrix.</dd>
<dt>kernel</dt>
<dd>Kernel name passed to <code><a href="/link/stats%3A%3Akernel()?package=bigPLSR&version=0.7.2" data-mini-rdoc="bigPLSR::stats::kernel()">stats::kernel()</a></code> compatible helpers
(<code>"linear"</code>, <code>"rbf"</code>, <code>"poly"</code>, <code>"sigmoid"</code>).</dd>
<dt>gamma, degree, coef0</dt>
<dd>Kernel hyper-parameters.</dd>
<dt>chunk_rows, chunk_cols</dt>
<dd>Numbers of rows/columns to process per chunk.</dd></dl>

Arguments

Streamed centering statistics for RKHS kernels — bigPLSR_stream_kstats

<dl>

<dt>Xbm</dt>
<dd>A <code>bigmemory::big.matrix</code> (or descriptor) containing the
training design matrix.</dd>


<dt>kernel</dt>
<dd>Kernel name passed to <code><a href='https://rdrr.io/r/stats/kernel.html'>stats::kernel()</a></code> compatible helpers
(<code>"linear"</code>, <code>"rbf"</code>, <code>"poly"</code>, <code>"sigmoid"</code>).</dd>


<dt>gamma, degree, coef0</dt>
<dd>Kernel hyper-parameters.</dd>


<dt>chunk_rows, chunk_cols</dt>
<dd>Numbers of rows/columns to process per chunk.</dd>

</dl>

bigPLSR_stream_kstats: Streamed centering statistics for RKHS kernels

Description

Usage

Value

Arguments