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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some important question to understand partial least squares (PLS)

I am reading the PLS of Kevin Dunn's book Process Improvement Using Data section ...
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Multiple dim responding variable the relation between CCA and trivial linear regression (PLSR, CCA, PCA, PCR and Linear Regression)

Here is my summary of Multivariate Linear Regression between explain variable $\textbf{x}$ and responding variable $\textbf{y}.$ I have summaried the relation ...
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How does PLSR solve Multicollinearity

We know that PLSR is a very common way to solve Multicollinearity in the Multiple Linear Regression. But do you know how does it work in detail? And why Multicollinearity of $x$ will be related to the ...
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Meaning of components and coefficient matrix in pls

I want to model a relation of some variables with pls in R. The situation looks like: ...
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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....
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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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174 views

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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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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Combining PLS components

I am using Partial Least squares to investigate associations between two multidimensional datasets. I have 60 observations, and one of the datasets has 60 features, while the other has around 5,000. ...
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Can we do inverse prediction for data having multi-class response variable after fitting PLS-DA?

I know how to do inverse prediction (predicting one of the input variables when we know what is the output we want) for the case of regression. I know we can do the same for binary classification ...
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Clarification of p values following PLS-DA and obtaining Q2Y and R2Y metrics following permutation

I am using the ropls package in R to apply a PLS-DA model. My predictor is a matrix of gene expression values and my response is either disease or control. From the description it states that the p-...
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How does partial least squares algorithm return more than one factor?

My understanding of PLS regression is that we find an eigen vector such that it maximises the covariance between X(matrix of independent variables) and Y(vector/matrix of dependent variable) i.e. find ...
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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 ...
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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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Value for ncomp when making predictions for PLSR model

Using the seatpos dataset from the faraway package in R, I wanted to do PLS regression models with up to eight components, ...
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Can a trained Partial Least Squares (PLS) model be used for lossy compression/encoding?

Once I have carefully trained a PLS model, I know the optimal number N of components for a regressor model. Can those components and their coefficients be used to lossy compress the original data ...
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Combining Bootstrap and Cross-Validation

I am trying to think of ways of combining bootstrap and cross-validation (CV) to get out-of-sample prediction error and its confidence interval. I was initially thinking of applying this to partial ...
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363 views

Partial Least Squares Using Python - Understanding Predictions

I am having trouble constructing/applying a regression equation from PLS to make a prediction in a manner that can obtain the same predicted values that the model produces when calling the model....
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Non-negative partial-least-squares regression?

I am using partial least squares regression (PLSR) to analyze a chemometrics dataset. I'm interested in non-negative components. Is there an analogous technique to non-negative matrix factorization ...
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How to control the level of sparsity on sgPLS and spls packages?

I am trying to use a (group) sparse PLS algorithm on a regression problem with an univariate response variable $y$, and I found the packages sgPLS and ...
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Difference between LDA and PLS-DA?

Could someone please help by explaining the difference between LDA and PLS-DA? Or are we talking about the same?
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If I control for country effects and results are significant, can I conclude generalizability of results?

I am working on a mediated model where M mediates the relationship between X and Y and I have one control variable. The data I am using is from 3 different countries with different sample sizes (46, ...
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333 views

(Dis)advantages of a PLS regression over PCR

I've read a lot of sources about Partial Least Squares (PLS) Regression and, based on my readings, it seems that it has some advantages over a Principal Component Regression (PCR). Different sources ...
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Is it possible to use Partial Least Squares-SEM with only one latent variable

I want to compute a Confirmatory Factor Analysis using Partial Least Squares-Structural Equation Modeling. I have only one latent variable and 10 manifest variables....
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PLS regression assumptions [duplicate]

When we perform an usual multiple linear regression analysis, we must check some assumptions like residuals are normally distributed, no multicollinearity of predictors and homoscedasticity. For each ...
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190 views

Moderated moderation (3-way interaction) with latent variables

I would appreciate some help in deciding the analysis method for my research. I have 7 variables: one DV, one IV, two moderators, one moderator/moderating moderator plus 2 controls. My overall ...
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Multicollinearity and OPLSDA fail

Which one would be the alternative if I find this problem? I am performing OPLDS-DA to determine, among my 58 parameters (104 observations), which one(s) drive the separation between my disease ...
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PLS regression predictions

We have the following sample containing two predictors ($x_1, x_2$) and one dependent variable ($y$). $x_1=[-1.01, 3.23, 5.49, 0.23, -2.87, 3.67]$ $x_2=[-0.99, 3.25, 5.55, 0.21, -2.91, 3.76]$ $y=[-...
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How to interpret output from RMSEP in R

I have a dataset with 15 columns and 500 rows. I have developed a plsr model as "plsr_model" and I have a testing dataset as "train.data". I want to find the Root mean square error of prediction (RMSE)...
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Different PLS model when using plsr() function in R or Minitab PLS regression menu

I have a PLS model made in R with plsr() function (from package pls); I have chosen the right number of components with cross ...
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Partial Least Squares: adding unrelated variables improves fit

I am performing some simulations of partial least squares. In particular, I have 30 observations split into 20 which are for training and 10 which are for testing. I also have 23 independent variables ...
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Partial least-squares regression - Estimate future response residuals

I'm trying to apply Partial least-squares regression to forecast Y(t) using X(t,space), where space is my spatial points ( ~ 1000 points), using Matlab(function plsregress): [XL,YL,~,YS,BETA,~,~,stats]...
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PLSr: Generating predicted value using regression coefficient

I perform PLS with pls package in R using plsr function. Why am I unable to get the same predicted Y value as when I use the ...
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Minimum number of obs. for machine learning and training/test sets?

Are there a minimum number of observations for ML techniques (classification, regression) in psychology/cognitive neuroscience? In particular for training and test datasets? I found this article for ...
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Are there constraints on the ratio of dependent variables to independent variables for PLS regression?

I will begin with a disclaimer that while I understand the general underlying principles behind PLS, my linear algebra background is rather limited. I have trouble with the details of constraints on ...
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RMSE behaviour during cross-validation of a PLS model - What does it mean?

In PLS regression, one of the approaches in selecting the number of components (Latent Variables, LV) is to perform cross-validation over a range of LV, and select the one with lower Root Mean Squared ...
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PLS training by cross validation, selection of components and prediction

I know there are plenty of questions on this topic on the site, but I still can't work out how to choose the optimal number of components for the particularities of my PLS model. I have 1800 ...
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Standardizing dataset before comparing slopes in regression

I have 3 different yield parameters data which was obtained from four different locations. Here is the distribution of dataset at all locations. I want to determine which yield component is strongly ...
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Why is there an inconsistency between PLS projections and energy statistic output?

On R, I use the mixOmics package to run a repeated measures ("multilevel") Partial Least Squares analysis. I'm able to plot the 2-dimensional PLS projections such ...
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How should predicted probabilities be interpreted in a binary classification model?

I ran a PLS-DA model with 10-fold cross-validation to classify data in 2 groups (using the Caret package in R). The predicted probabilities are close to 0.5 (the highest propbability is just 0.7). The ...
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Problem with Principal component (PCA) and Partial least squares (PLS) using R

I'm trying to reduce highly dimensional data with factor methods. I'm using Principal component analysis and Partial least squares. From these methods I'm using the first component as a Common factor ...
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meaning of projection subspace in a PLSDA plot

I have a dataset with a handful of predictors and one output variable which is categorical and can only be C or N. I am working ...
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How is this standard error obtained?

I am working through the exercises in Kuhn and Johnson's "Applied Predictive Modelling" and cannot reproduce one of their results in the exercises. Looking at 4.3 we have ... find the number of ...
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333 views

How to interpret weights of a PLS SEM model

I made a PLS SEM model using smartPLS, consisting only of formative constructs. I managed to get weights out of the software, which all had excellent t values. The only thing is, I am not entirely ...

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