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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32 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
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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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Applying PLSDA coefficients to a new dataset to classify [closed]

I have a trained model to classify leaves through its spectra by using a PLSDA (with caret package). My question is, is it possible to apply the coefficients of my model over a new spectra dataset?
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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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1answer
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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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1answer
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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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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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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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22 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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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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1answer
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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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What is an appropriate technique for large number of correlated predictor variables with interactions?

I want to allow the effects of a large number of continuous predictor variables to be different depending on which treatment group the individual is a part of. If I had only three continuous ...
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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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Partial Least squares regression - Variable Importance on Projection (VIP) method of selecting variables

I understand that partial least squares regression produces VIP scores for each predictor variable enabling variable selection (using a VIP threshold of >1). Does this method account for collinearity ...
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Replicating partial least squares (NIPALS) results using ordinary least squares regression in Tensorflow?

I have multivariate variables that I want to regress to a single target label. For some reason, using partial least squares regression (projected to a single component) gives much better prediction ...
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Partial Least Square - choosing number of factors

I'm trying to implement PLS in R (using the package "PLS") to a time series consisting on realized variance of the S&P 500 and macroeconomic variables, however, I've notice they use cross-...
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285 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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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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What's the difference between loadings from partial least squares (PLS) regression and beta coefficients from multiple linear regression?

I have a set of independent variables (X1, X2, ..., X10) and I have run a PLS to find a combination of the X1, X2, ..., X10 that best predicts an outcome Y (a single-variable outcome). As a result, I ...
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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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Calculating Variable Importance for Feature Selection - PLSR

I have used the plsr() function in R (from the pls package) to predict a Y variable using many X variables (spectral bands) - and am wanting to calculate variable importance (ViP) to begin to reduce ...
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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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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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Is it appropriate/possible for me to use PLS regression for my problem?

Im currently writing a dissertation on the effect of cultural dimensions on technology acceptance. In order to collect data I have used a questionnaire with questions relating to technology acceptance,...
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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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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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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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Partial least squares and its variants

What is the difference between PLS, PLS-DA, PLS-PM and PLS-SEM? I could not find a single article that describes the differences.
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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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Non-linear multivariate analysis / partial least squares

When dealing with multivariate analysis with variables that have a mix of linear and non-linear relationships (example: two variables are linearly related but one of these maybe non-linear with ...
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3% explained variance in PLSR

My dataset contains 3 inputs and 1 output with 36000 samples. With PCA analysis I can explain 100% of the variance with 2 components. But when I do PLSR I only get 3% explained variance. Ive only done ...
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Interpret reuslts of PLS regression coefficients

I have performed PLS regression using sklearn library (python 2.7) over three types of soil (PLS model per soil type) and I plotted the regression coefficients, but ...
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I'd like to do regression using canonical correlation analysis

I got two multidimensional datasets, X and Y. I thought I build the model, which explains the relationship between two datasets, using canonical correlation analysis (CCA). The first correlation ...
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What is the point of using PRESS instead of RMSECV?

What is the point of using predicted residual sum of squares (PRESS) instead of root-mean-squared-error-of-cross-validation(RMSECV)? In many books, especially in the area of chemometrics, the authors ...
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1answer
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Finding correlation between 2 predictors and a response

I've created a dataset, where the response, y, is related to the predictors X1, X2, by the formula: y = 2X1 + 5X2. If we look at correlating y with X1, and then y with X2, we get the following: And ...
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Partial least square different maximization programs

PLS regression is a regression method based based on latent variables in order to handle collinearity or violation of full rank assumption in linear regression. Latent variables called components are ...
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Confirmatory Factor Analysis - Variance Parameters - Error Variance Estimates

I am referring to a confirmatory factor analysis output. I am curious to know how do we interpret the "Variance Parameters" Output containing the "error variance estimates" if the predictors are ...
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Inference using model with low goodness of fit.

Assuming a model is correctly specified, would it be appropriate to draw inferences based on a model with a low Goodness of Fit (~0.15)? Of course, using such a model to make predictions is likely ...