# Questions tagged [partial-least-squares]

A class of linear methods for modeling the relationship between two groups of variables, X and Y. Includes PLS regression.

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### Is it possible to predict on new data using PLS SEM?

Using the seminr package in R, I have fitted a model based on PLS SEM with several exogenous (latent) variables and one endogenous latent variable (ELV) measured by ...
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### Centering and scaling in Partial Least Squares

I am trying to understand, how data is centered and scaled in Partial Least Squares (PLS). I understand how it is done in Principal Component analysis (PCA). For example, in PCA test-data is centered ...
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### Explaination of Orthogonal Partial Least Squares (OPLS)

I am rather familiar with PLS and understand that it is composed of 3 iterated steps: finding directions $\vec{v}$, $\vec{w}$ such that the correlation E[($\vec{x}$$\vec{v})(\vec{y}$$\vec{w}$)] is ...
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### How to work with Proportion of variance explained in PLS Regression?

I have some very basic questions regarding PLS. I ran PLS using SPSS on small dataset. n=312, Dependent variable=1; Independent Variables=9. Here is the output of Proportion of Variance Explained. As ...
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### How can I do continuum regression in R?

I am looking for a R package that does continuum regression. More concrete I need a function that does continuum regression s.t. I can evaluate the values afterwards. At least extracting MSE or RMSE ...
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### Regression in data with one group, having just zeros as outcome

I have a data set, consisting of positive and negative patients (virus infection). If the patient is negative, it has 0 as outcome (y), if it is positive it has a positive value, up to 100. The input (...
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### Decomposition of oil price

For a project I want to recreate the graph "Cumulative Weekly Decomposition" from: https://www.newyorkfed.org/medialibrary/media/research/policy/oil_decomposition/oil-decomp_2022-0328.pdf?la=...
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### R-square vs. NFI?

I ran a path model (no latent variables) in smartPLS3. It's not a complicated model. But after the analysis was computed, I checked the model fit measures. R-squares are small (all of them < .3), ...
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### Nuisance covariates in partial least squares analysis

I have six correlated phenotypic variables (e.g. height, weight, waist circumference) and I wish to see how these relate to a single continuous genetic variable. The sample size is large (n>30,000) ...
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### Transformation of data

I have a question regarding transformation of data. I have handled some data with both negative and positive elements by using the transformation: log(Y+1-min(Y)) which is all good. The problem is ...
1 vote
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### How to convert SMART-PLS structural coefficients from standardized (correlation) to unstandardized?

I need use smartPLS but i also have prediction purpose (coefficients should also act like b-coefficients of regression). How to do this in smartPLS, since all coefficients are standardized?
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### Using Partial Least Squares for reduced-dimension machine learning

I want to perform dimensionality reduction using Partial Least Squares on a complex, large-dimension data set before training various regression models on the reduced-dimension data set. I understand ...
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### PLS: Find number of components for multiple dependent variables

I created a PLS model with three dependent variables using mdatools. Variable A gets the best results when using two components. However for variables B and C it would be better to use four components....
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### How to interpret null or nearly null coefficients with VIP > 1 in PLSR?

I try to interpret a PLSr model that I used to predict a response variable using full range spectroscopy (500 - 2400 nm). I followed the method from Serbin et al. 2014 (https://doi.org/10.1890/13-2110....
1 vote
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### PLS regression - VIP treshold to exclude variables

I have been developing PLS models in the software SIMCA. To optimize the model and decide which variables to exclude, I use the VIP (Variable Importance in Projection [1,2]) and in the software ...
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### PLS Regression - RMSEP minimum value

I use the plsr function in R with cross validation (10-fold). As a result, I get this output: From my limited understanding, I know that the ideal number of components is usually chosen by the ...
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### What does it mean to say that a regression method is (not) "scale invariant"?

I was just studying partial least squares regression, and I read that it is "not scale invariant". What does "scale invariant" mean, and why is partial least squares, and why would ...
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1 vote
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### What's behind PLS regression method?

I was wondering if anyone could provide me a source with a more or less simple explanation to the PLS regression process? I have been reading this paper to help me understand what's behind the PLS, ...
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### PLS-DA dependent variables

Is it possible to use more than one categorical dependent variable with partial least square discriminant analysis? Thanks.
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### PLS procedure in SAS software

experts, I have a question about PLS procedure in SAS. The manual said that the prediction on new data is by combining training data and new data (new data dont have response values). I did ...
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### Variable of importance and Q2Y in PLSR

In Partial Least Squares Regression, we can set a threshold to variable of importance scores to extract variables that have significant influence over the output. We can then reduce the model size to ...
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### Explained variation in PLS vs PCA

A lot of research articles outline that the number of extracted factors by PLS (partial least squares) is less than the number of extracted factors by PCA (principal component analysis). However, the ...
1 vote
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### Difference between sequential/simultaneous nonlinear partial least squares and NIPALS algorithm

I've been reading about nonlinear partial least squares, and according to the below study, there are two types of NLPLS: sequential NLPLS and simultaenous NLPLS. https://www.sciencedirect.com/science/...
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