# Partial least squares regression

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**Partial least squares regression (PLS regression)** is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of minimum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the *X* and *Y* data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares Discriminant Analysis (PLS-DA) is a variant used when the Y is categorical.

PLS is used to find the fundamental relations between two matrices (*X* and *Y*), i.e. a latent variable approach to modeling the covariance structures in these two spaces. A PLS model will try to find the multidimensional direction in the *X* space that explains the maximum multidimensional variance direction in the *Y* space. PLS regression is particularly suited when the matrix of predictors has more variables than observations, and when there is multicollinearity among *X* values. By contrast, standard regression will fail in these cases (unless it is regularized).

The PLS algorithm is employed in Partial least squares path modeling,^{[1]}^{[2]} a method of modeling a "causal" network of latent variables (causes cannot be determined without experimental or quasi-experimental methods, but one typically bases a latent variable model on the prior theoretical assumption that latent variables cause manifestations in their measured indicators). This technique is a form of structural equation modeling, distinguished from the classical method by being component-based rather than covariance-based.^{[3]}

Partial least squares was introduced by the Swedish statistician Herman Wold, who then developed it with his son, Svante Wold. An alternative term for PLS (and more correct according to Svante Wold^{[4]}) is *projection to latent structures*, but the term *partial least squares* is still dominant in many areas. Although the original applications were in the social sciences, PLS regression is today most widely used in chemometrics and related areas. It is also used in bioinformatics, sensometrics, neuroscience and anthropology. In contrast, PLS path modeling is most often used in social sciences, econometrics, marketing and strategic management.

## Underlying model

The general underlying model of multivariate PLS is

where is an matrix of predictors, is an matrix of responses; and are matrices that are, respectively, projections of (the *X score*, *component* or *factor* matrix) and projections of (the *Y scores*); and are, respectively, and orthogonal *loading* matrices; and matrices and are the error terms, assumed to be independent and identically distributed random normal variables. The decompositions of and are made so as to maximise the covariance between and .

## Algorithms

A number of variants of PLS exist for estimating the factor and loading matrices and . Most of them construct estimates of the linear regression between and as . Some PLS algorithms are only appropriate for the case where is a column vector, while others deal with the general case of a matrix . Algorithms also differ on whether they estimate the factor matrix as an orthogonal, an orthonormal matrix or not.^{[5]}^{[6]}^{[7]}^{[8]}^{[9]}^{[10]}
The final prediction will be the same for all these varieties of PLS, but the components will differ.

### PLS1

PLS1 is a widely used algorithm appropriate for the vector case. It estimates as an orthonormal matrix. In pseudocode it is expressed below (capital letters are matrices, lower case letters are vectors if they are superscripted and scalars if they are subscripted):

1functionPLS1() 2 3 , an initial estimate of . 4 5for= 0to6 (note this is a scalar) 7 8 9 (note this is a scalar) 10if= 0 11 ,breakthefor loop12if13 14 15 16 endfor17defineto be the matrix with columns . Do the same to form the matrix and vector. 18 19 20return

This form of the algorithm does not require centering of the input and , as this is performed implicitly by the algorithm. This algorithm features 'deflation' of the matrix (subtraction of ), but deflation of the vector is not performed, as it is not necessary (it can be proved that deflating yields the same results as not deflating.). The user-supplied variable is the limit on the number of latent factors in the regression; if it equals the rank of the matrix , the algorithm will yield the least squares regression estimates for and

## Extensions

In 2002 a new method was published called orthogonal projections to latent structures (OPLS). In OPLS, continuous variable data is separated into predictive and uncorrelated information. This leads to improved diagnostics, as well as more easily interpreted visualization. However, these changes only improve the interpretability, not the predictivity, of the PLS models.^{[11]} L-PLS extends PLS regression to 3 connected data blocks.^{[12]} Similarly, OPLS-DA (Discriminant Analysis) may be applied when working with discrete variables, as in classification and biomarker studies.

## Software implementation

Most major statistical software packages offer PLS regression.{{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=
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}} The 'pls' package in R provides a range of algorithms.^{[13]}

## See also

- Feature extraction
- Data mining
- Machine learning
- Regression analysis
- Canonical correlation
- Deming regression
- Multilinear subspace learning
- Principal component analysis
- Total sum of squares

## Further reading

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- Wan Mohamad Asyraf Bin Wan Afthanorhan. (2013). A Comparison Of Partial Least Square Structural Equation Modeling (PLS-SEM) and Covariance Based Structural EquationModeling (CB-SEM) for Confirmatory Factor Analysis International Journal of Engineering Science and Innovative Technology (IJESIT), 2(5), 9.

## References

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## External links

- imDEV free Excel add-in for PLS and PLS-DA
- PLS in Brain Imaging
- on-line PLS regression (PLSR) at Virtual Computational Chemistry Laboratory
- Uncertainty estimation for PLS
- A short introduction to PLS regression and its history