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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Variation on partial least squares regression (PLSR) that does not assume linear relationships between variables
Partial least squares regression (PLSR) is a statistical technique that allows you to predict multiple response variables from multiple predictor variables. It works by essentially running separate ...
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3 views
PLS correlation and PLS DA
I have 400 variables that are continuous with 100 observations. I'm interested to explore the relationship of these variables with another set of variables that are in an ordinal scale (0-4). I looked ...
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28 views
PLS model performs significantly worse than chance according to permutation test
I'm using Partial Least Squares regression to relate two datasets: X has 40 variables, Y has 54 variables, both have 26 observations. Because I don't have a lot of observations, I'm using a ...
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7 views
How to settup a databas for PLS model different scenarios- same individuals
I am currently doing my Master Thesis on how the Willingness to Pay (WTP) and Purchase intention (PI) of customers is affected by the level of customization of a product; I am using PLS-SEM model.
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GLM on features selected by PLS-DA
First question: Can we use GLM on specific variables selected by PLS-DA latent components? To obtein p-value of response of prediction (e.g.for each selecetd variable of comp 1)?
Second question: What ...
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2answers
18 views
Multicollinearity and case:predictor ratio problems
I have 444 cases and 60ish predictors that suffer from collinearity. The predictors fall into three categories (vol, thickness and demographics). I would prefer to subdivide my cases into 4 (age) ...
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7 views
How can I summarize latent variables using PLSSEM
I am kinda new to PLS SEM, but I think I have the steps down and can interpret the results. However, I initially like to look at summary statistics of variables when a single variable measure a ...
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26 views
Do loadings in PCA describe variation around the mean?
I found this statement in one paper about statistical process control and monitoring:
The P loading matrices contain all the structural
information about how the variable measurements should deviate
...
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1answer
104 views
Noisy PLS-DA loading for a model with good performance
What does it mean to have a good PLS-DA model (in terms of both sensitivity and specificity on independent test set), but very noisy loadings (the data is IR spectrum, and it lost its spectral shape ...
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24 views
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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1answer
42 views
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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1answer
75 views
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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26 views
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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1answer
59 views
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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1answer
31 views
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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1answer
35 views
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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1answer
39 views
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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3answers
511 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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1answer
49 views
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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16 views
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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32 views
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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12 views
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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19 views
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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76 views
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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13 views
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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1answer
143 views
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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118 views
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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1answer
82 views
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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1answer
229 views
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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1answer
2k 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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39 views
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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2answers
665 views
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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1answer
849 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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1answer
532 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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48 views
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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112 views
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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1answer
47 views
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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2answers
211 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
264 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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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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1answer
432 views
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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1answer
384 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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1answer
26 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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35 views
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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63 views
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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1answer
123 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 ...
