# `ds` — Direct Standardisation
_Group_: **Calibration transfer** · _Registry tolerance_: `1e-08`
## Description
DS — Direct Standardization (§13)
From the `pls4all.sklearn.DSTransformer` docstring:
> Direct Standardization — full-band cross-instrument regression.
> **Registry note** — Base R per-band `lm.fit` with window_half_width=0 — Direct Standardization is just per-band linear regression.
_No tunable parameters declared at the binding level._
## Explanations
### Bibliographic source
Wang, Y., Veltkamp, D. J. & Kowalski, B. R. (1991). *Multivariate instrument standardisation*. Analytical Chemistry 63(23), 2750–2756.
### Mathematical principle
Direct Standardisation (DS) learns a **single global transfer matrix** $\mathbf{F}$ that maps secondary-instrument spectra back onto the primary instrument: $\hat{\mathbf{X}}_{\mathrm{primary}} = \mathbf{X}_{\mathrm{secondary}} \mathbf{F}$. The matrix is fit by least squares on a small set of transfer-standard samples measured on both instruments, typically with a Tikhonov ridge for stability.
DS is the simplest member of the calibration-transfer family and works well when the inter-instrument response is approximately linear and stationary — i.e. when instrument differences amount to a constant linear transformation that is invariant across the spectrum. When the transformation varies across wavelength bands (common in dispersive vs FT spectrometers) the global transfer matrix produces band-mixing artefacts and PDS should be used instead.
A canonical workflow: fit DS on $\le 30$ transfer standards, apply $\mathbf{F}$ to all subsequent secondary-instrument spectra, then use a single PLS model fit only on primary data.
### Implementation
`n4m_domain_adaptation_ds_fit` (TransformerMixin in tier 2). Reference: R `chemometrics::stdize`.
R roxygen note (`methods_extra.R::ds_fit`):
> Direct Standardization (calibration transfer).
> @param X_source Method-specific parameter. See the underlying `*_fit()` function for the exact semantics.
> @param X_target Method-specific parameter. See the underlying `*_fit()` function for the exact semantics.
> @export
MATLAB header (`bindings/matlab/+pls4all/ds.m`):
```text
pls4all.ds Direct Standardization (calibration transfer).
```
### Usage
Every pls4all binding tab dispatches into the same C kernel; the external libraries listed at the bottom of the page are the parity references registered in `benchmarks.parity_timing.registry`. Switch tabs to read the same fit in your language. The R package now ships drop-in-compatible facades for the CRAN `pls` package (`plsr`, `pcr`, `mvr`) and for the `mdatools::pls(x, y, ...)` matrix idiom — those tabs appear only on the methods that have a meaningful equivalence.
**pls4all bindings**
::::{tab-set}
:class: pls4all-bindings
:::{tab-item} C ABI · libn4m
:sync: c
:class-label: lang-c
```c
/* C ABI — libn4m */
n4m_context_t* ctx = n4m_context_create();
n4m_config_t* cfg = n4m_config_create();
n4m_method_result_t* res = NULL;
n4m_domain_adaptation_ds_fit(ctx, cfg, &x_view, &y_view, /* hyperparams */, &res);
/* … read coefficients / mask / scores via */
/* n4m_method_result_get_double_matrix / vector / scalar … */
n4m_method_result_destroy(res);
n4m_config_destroy(cfg);
n4m_context_destroy(ctx);
```
:::
:::{tab-item} Python · pls4all (raw)
:sync: python-raw
:class-label: lang-python
```python
import pls4all
from pls4all._methods import ds_fit
with pls4all.Context() as ctx, pls4all.Config() as cfg:
res = ds_fit(ctx, cfg, X, y, X_target=X_target)
# then: res.matrix("predictions"), res.matrix("coefficients"),
# res.vector("mask"), res.scalar("intercept"), …
```
:::
:::{tab-item} Python · pls4all.sklearn
:sync: python-sklearn
:class-label: lang-python
```python
from pls4all.sklearn import DSTransformer
mdl = DSTransformer(n_components=2)
mdl.fit(X, y, X_target=X_target)
y_hat = mdl.predict(X_test)
```
:::
:::{tab-item} R · pls4all_method()
:sync: r-dispatcher
:class-label: lang-r
```r
library(pls4all)
# Unified low-level dispatcher (May 2026 R cleanup):
res <- pls4all_method("ds", X, y,
n_components = 2L)
# res is a named list with MethodResult arrays/scalars.
# selected_indices / top_k_intervals are 1-based.
```
:::
:::{tab-item} R · pls4all (raw fn)
:sync: r-raw
:class-label: lang-r
```r
library(pls4all)
res <- ds_fit(X_source, X_target)
yhat <- pls4all_predict(res, X_test)
```
:::
:::{tab-item} MATLAB · pls4all (MEX)
:sync: matlab-mex
:class-label: lang-matlab
```matlab
res = pls4all.ds(X, y, 2);
% see header of bindings/matlab/+pls4all/ds.m for full
% parameter surface:
% res = ds(X_source, X_target)
yhat = predict(res, Xtest);
```
:::
:::{tab-item} MATLAB · pls4all (classdef)
:sync: matlab-classdef
:class-label: lang-matlab
_No idiomatic classdef wrapper — invoke `pls4all.fit("ds", X, y, …)` directly from the unified MEX factory._
:::
::::
**Registry parity references** 📐
:::{card}
:class-card: external-refs
- 📐 **`ref.r_base`** (R · r) — `base` R 4.3.3 · strict (rmse_rel ≤ 1e-08) — Base R `lm` per spectral band — closest installable analog to Wang 1991 Piecewise Direct Standardization. With window_half_width=0 this reduces to Direct Standardization.
:::
### Benchmarks
Adaptive wall-clock per cell measured against [`full_matrix.csv`](../benchmarks/overview.md). Only backends that implement this method are listed; libraries without the method are omitted.
**Verdict** · ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance · ✓ bind = pls4all binding agrees with the C++ baseline · ⇄ cross-check = documented by-design selector/RNG/model, noncanonical API/facade convention, or secondary oracle · ✗ divergent · ⚠ error · — not run. The fastest backend per column is marked 🏆.
**Reference gate**: strict — numeric equivalence (`rmse_rel_tol ≤ 1e-08`).
Rows tagged with **📐** are the canonical parity references for this method (declared in [`parity_timing.registry`](../benchmarks/methodology.md)). C++ and external rows show reference parity; pls4all language bindings show binding parity against the C++ backend. Hover the icon for role and tolerance band.
::::{tab-set}
:class: parity-tabs
:::{tab-item} 1 thread
:sync: threads-1
| Backend | Parity | 200×30 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas+omp | ✓ ref 6e-09 | 1.64 ms🏆 |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | 2.09 ms |
pls4all.sklearn | ✓ 9e-16 | 2.27 ms |
| R · pls4all |
|---|
pls4all.R | ✓ bind | 10.2 ms |
pls4all.R.formula | ✓ bind | 14.0 ms |
pls4all.R.mdatools | ✓ bind | 11.4 ms |
pls4all.R.pls | ✓ bind | 12.6 ms |
| R · external |
|---|
📐ref.r_base | source | 31.0 ms |
:::
:::{tab-item} 3 threads
:sync: threads-3
| Backend | Parity | 200×30 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas+omp | ✓ ref 6e-09 | 3.32 ms |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | 1.57 ms🏆 |
pls4all.sklearn | ✓ 9e-16 | 1.72 ms |
| R · pls4all |
|---|
pls4all.R | ✓ bind | 11.1 ms |
pls4all.R.formula | ✓ bind | 11.8 ms |
pls4all.R.mdatools | ✓ bind | 10.7 ms |
pls4all.R.pls | ✓ bind | 13.0 ms |
| R · external |
|---|
📐ref.r_base | source | 27.8 ms |
:::
:::{tab-item} 10 threads
:sync: threads-10
| Backend | Parity | 200×30 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas+omp | ✓ ref 6e-09 | 1.42 ms🏆 |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | 2.13 ms |
pls4all.sklearn | ✓ 9e-16 | 1.72 ms |
| R · pls4all |
|---|
pls4all.R | ✓ bind | 8.95 ms |
pls4all.R.formula | ✓ bind | 9.70 ms |
pls4all.R.mdatools | ✓ bind | 9.43 ms |
pls4all.R.pls | ✓ bind | 10.1 ms |
| R · external |
|---|
📐ref.r_base | source | 28.0 ms |
:::
::::
---
_See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)