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New lwpls_local() returns the local models that
LW-PLS builds for new samples, with their diagnostics: the reliability
of each prediction (effective number of neighbors, distance to the
nearest training sample, and the Hotelling T² and Q statistics of the
query in its local model, compared with empirical limits, theoretical
limits and as percentile ranks among the training samples of the local
model), the regression coefficients and VIP of each local model, the
predictors selected by sparse models, and the robust weights of the
training samples. It accepts lwpls_fit() objects, parsnip
fits and workflows.
New ggplot2 plots of the local models, also available with
autoplot(): plot_reliability() (applicability
domain of the predictions), plot_coefficients() (local
regression vectors or VIP along the spectrum, as lines or a heatmap),
plot_robust_weights() (training samples down-weighted by
robust models, such as wrong reference values) and
plot_selection() (wavelengths selected by sparse models,
per component or overall). The spectral axis is read from the predictor
names or given as wavelength.
New vignette, vignette("diagnostics").
The README and vignette("lwpls") show the
cross-validated RMSE as a 3D surface over num_comp and
localization (interactive on the package website, with
plotly as a new suggested package).
New vignette, vignette("similarity"), on choosing a
similarity index: the geometry of the Euclidean and covariance-based
distances, and simulations showing when each one works best.
Robust LW-PLS: with robust = TRUE (an engine
argument of lwpls() and an argument of
lwpls_fit()), each local model is fitted by partial robust
M-regression (PRM; Serneels et al., 2005), with the Fair (default) or
Hampel weight function. The medians and the L1-median that PRM uses are
weighted by the similarity weights, so the robust models stay local.
Robust models are available for regression with one or more outcomes and
for classification, and they standardize the data and scale the
distances with the median and the MAD.
Sparse LW-PLS: the new sparsity argument (a tunable
main argument of lwpls(), with the dials parameter
sparsity()) fits the local models by sparse NIPALS (SNIPLS;
Hoffmann et al., 2015), so that each local model selects its own
predictors. Combined with robust = TRUE, it gives local
sparse PRM (SPRM).
New yardstick metric trimmed_rmse() to tune robust
models on data with outliers.
New vignette, vignette("robust-sparse").
This is a rewrite of the package. LW-PLS is now implemented natively and no longer depends on the archived rnirs package.
lwpls() now has the arguments mode,
num_comp, localization,
neighbors, similarity and engine.
The shapefactor argument is replaced by
localization, the parameter of the similarity function of
Kim et al. (2011) (lambda in Kaneko’s implementation). The
engine is now "lwpls" (was "rnirs").
The magrittr pipe %>% and
generics::tidy() are no longer re-exported. Use the native
pipe |> (R >= 4.1.0).
Native implementation of LW-PLS, validated against Kaneko’s reference implementation. The prediction kernel is written in C++ (RcppArmadillo): it uses the kernel PLS algorithm of Dayal & MacGregor (1997) and processes queries in blocks so that most of the work runs as BLAS matrix-matrix products.
Regression with one or more numeric outcomes, and classification (LW-PLS-DA) with class and probability predictions.
New lwpls_fit(), a hardhat-based fitting function
with matrix, data frame, formula and recipe interfaces. Its
predict() method returns tibbles that follow the tidymodels
conventions and can use any number of components.
multi_predict() and min_grid() methods:
tune evaluates all values of num_comp from a single model
fit.
New dials tuning parameter localization().
Covariance-based LW-PLS (CbLW-PLS, Hazama and Kano, 2015): the
new similarity argument measures the similarity between
samples with the Euclidean distance ("euclidean", the
default) or along the covariance direction of the training data
("covariance"). It can be tuned with the new dials
parameter similarity().
The optional neighbors argument restricts each local
model to the nearest training samples.
The "lwpls" engine has a scale argument
to switch off the standardization of the data.
Added unit tests (with test coverage tracked on Codecov) and an
introductory vignette (vignette("lwpls")), including
regression and classification examples.
NEWS.md file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.