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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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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31 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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118 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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79 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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83 views

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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81 views

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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1answer
36 views

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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19 views

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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Partial least square

In the context of confirmatory factor analysis, structural equation modeling, & predictor space dimension reduction. PLS is a supervised dimension reduction procedure, since it summarize the ...
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Comparison/Visualisation of Regression Methods

This question follows this question, in particular @amoeba's clarifying answer and the plot from the SAS documentation included. I'm especially interested in knowing if $\mathbf{X}, \mathbf{Y}$ are ...
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130 views

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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22 views

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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Treating seasonality in Partial Least Squares forecast

I've been looking for answers on this question but couldn't find concrete solutions so wanted to ask y'all. I have been playing around trying to forecast an economic/financial-related indicator with ...
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heterotrait-monotrait analysis with PLSPM

I am using the PLSPM package in R and conducted discriminant analysis by applying the Fornell-Larcker criterion. I would also like to conduct heterotrait-monotrait (HTMT) analysis. Are there any tools ...
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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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70 views

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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37 views

How to validate PLS2 with R?

I have a PLS1 modell wich predicts single chemicals. Now I would like to run a PLS2 modell to predict two or more chemicals from a mixture. I programmed it in R and the modell runs, but i don´t find ...
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Interpretation of Nominal Scaling in PLSPM

I used nominal scaling in PLSPM in R and I am uncertain how to interpret the results. I made a minimal code example from the satisfaction data set and made up a nominal value for color (red, blue, ...
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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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291 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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66 views

When does partial least squares provide >1 component solutions?

I'm a beginner to using partial least squares analyses, so apologies if this question is a bit basic. I've been trying out PLS models on my datasets and it usually says that a single component can ...
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60 views

Is this an example of where I shouldn't scale before doing PCA / PLS?

I'm working with NMR spectra (it's a common chemical test). There are various peaks of the signal across a range of ppm values. I'm trying to relate the NMR spectra of various samples to a ...
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521 views

Partial Least Squares regression - coefficients vs loadings

In partial least squares regression, what is the difference between the regression coefficients and the loadings for each independent variable in each component? Specifically, I understand in evety ...
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161 views

What is the origin of the PLS1 algorithm given on the PLS Wikipedia page?

The Wikipedia page for Partial Least Squares (PLS) gives an algorithm for the method which is uncited and for which I cannot find the source material. It appears to be very much simpler than most if ...
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57 views

why does preprocessed test data change with change of calibration data in PLS-DA?

Why does preprocessed test data change when calibration dataset (and model based on that data) changes? I have spectral, normalized datasets, the preprocessing was 1. derivative + autoscale. For ...
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Is there a theoretical basis for using partial least squares with categorical responses

I am using what is called PLS-DA in JMP to find a model for predicting a categorical (Positive/Negative) response. The documentation says that the responses are simply coded as 0/1, thereby ...
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Partial Least Square vs Principle Component Regression

Is it the case where PLS, when compared to PCR with all things equal, generally gives lower bias but higher variance when regressed against a response Y, since PLS relates to/makes use of Y but PCR ...
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126 views

Partial Least Squares Regression : deflation of the Y matrix

I am digging deep into the PLSR algorithms and while I have found multiple flavours of if (different normalisations, SIMPLS,..), there is always something in the Y deflation that is throwing me off ...
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108 views

PLS (Partial Least Squares) deflation and graphics

I have been working with pls for a little while now. I have a question in terms of the deflation of both the $X$ and $Y$ matrices. In the literature I have found different methods over which deflation ...
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65 views

Formulating Partial Least Squares as minimizing squared error

The book chapter linked below (see section 4.3.1) lists a few formulations of partial least squares (PLS). The first two make sense to me and seem standard: $$\underset{\mathbf{u}, \mathbf{v}}{\text{...
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How to compute/plot the contribution of each original descriptor in a final PLA regression model?

New to scikit-learn. I am using v 20.2. I am developing PLS regression models.I would like to know how important each of the original predictors/descriptors are in predicting the response. The ...
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112 views

Choosing number of factors in PLSR

Im confused about how many factors I should choose for my prediction model. I am using Unscrambler X to do PLSR. Unscrambler is supposed to suggest the optimal number of factors. It suggests 4 factors ...
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Will PLSR work on nonlinear dataset?

Im new to this topic and a bit confused. When I read about PLSR on the web I only see examples where the original plot shows a somewhat linear relationship in the data set, but in my case I have a ...
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145 views

Screening data prior to PCA v. PLS

I have a very large time series matrix $X$, where the number of observations (rows) $n$ is much smaller than the number of input variables (columns) $p$. My aim is to use the information in $X$ to ...

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