The pls Package: Principal Component and Partial Least Squares Regression in R

By Bjørn-Helge Mevik, Ron Wehrens. 2007

1,891 citations recorded by the source corpus.

The pls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R (R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network (CRAN), licensed under the GNU General Public License (GPL). The user interface is modelled after the traditional formula interface, as exemplified by lm. This was done so that people used to R would not have to learn yet another interface, and also because we believe the formula interface is a good way of working interactively with models. It thus has methods for generic functions like predict, update and coef. It also has more specialised functions like scores, loadings and RMSEP, and a exible crossvalidation system. Visual inspection and assessment is important in chemometrics, and the pls package has a number of plot functions for plotting scores, loadings, predictions, coefficients and RMSEP estimates. The package implements PCR and several algorithms for PLSR. The design is modular, so that it should be easy to use the underlying algorithms in other functions. It is our hope that the package will serve well both for interactive data analysis and as a building block for other functions or packages using PLSR or PCR. We will here describe the package and how it is used for data analysis, as well as how it can be used as a part of other packages. Also included is a section about formulas and data frames, for people not used to the R modelling idioms.

Subfields: Analytical Chemistry, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/391e9281618610426b9fb406a2d611cca0b5a200

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Citations in the corpus

Predicting effective microRNA target sites in mammalian mRNAs (2015, background): “…; Anders et al., 2012 ), PITA Catalog v6 (3/15 flank for either ‘All’ or ‘Top’ predictions, August 2008) ( Kertesz et al., 2007 ), RNA22 (May 2011) ( Miranda et al., 2006 ), SVMicrO (February 2011) ( Liu et al., 2010 ), TargetRank (all scores from web server) ( Nielsen et al., 2007 ), TargetSpy…”
CCA: An R Package to Extend Canonical Correlation Analysis (2008, methodology): “The principle and interpretation are similar as the “correlation loadings” plot provided by the pls package in the context of PLS regression (Mevik and Wehrens 2007).”
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data (2009, methodology): “Three different feature selection approaches were applied to the data in an explicit recursive feature elimination (Table 1), together with the three following classifiers: linear discriminant principal component (D-PCR) and partial least squares (D-PLS) classification (using [40]) and the nonlinear”
Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes (2012, methodology): “PLSR analyses was performed using the pls package (Mevik and Wehrens, 2007) and the variance components of actual and predicted grain yield were estimated using the ASREML package (Butler et al.”
Laser-assisted selection of field plots for an area-based forest inventory (2013, methodology): “The statistical software R (R core team 2012) and the external library pls (Mevik and Wehrens 2007) were used to fit the models.”
Building Predictive Models in R Using the caret Package (2008, methodology): “Also, predict.mvr from the package (Mevik and Wehrens 2007) will produce predictions for every candidate value of ncomp that was tested.”
Orthogonal decomposition of left ventricular remodeling in myocardial infarction (2017, neutral): “The R implementation requires the pls package [25].”
Applied Predictive Modeling (2013, methodology): “The pls package (Mevik and Wehrens 2007) has functions for PLS and PCR.”
Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non‐spatial techniques (2014, methodology): “All geostatistical and regressive analyses were carried out with the Gstat (Pebesma, 2004) and geoR (Ribeiro & Diggle, 2001) packages, and PLSR analysis was carried out with the pls package (Mevik & Wehrens, 2007) of R software (R Development Core Team, 2011).”
Likelihood-Free Inference of Population Structure and Local Adaptation in a Bayesian Hierarchical Model (2010, methodology): “However, we have chosen to use principal component analysis (PCA), also commonly used in calibration and typically producing similar results (Mevik and Wehrens 2007), which orders the axes by decreasing variance.”
Differential response of continental stock complexes of Atlantic salmon (Salmo salar) to the Atlantic Multidecadal Oscillation (2014, methodology): “We used a multivariate PLSR analysis (Mevik and Wehrens, 2007) to simultaneously examine the relationships between the four stock components (North American/Southern European; 1SW/2SW) and the above five ocean/atmosphere climate indicators (Mevik and Wehrens, 2007).”