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.

Filter by
Sorted by
Tagged with
1
vote
0answers
488 views

Quality of PLS Regression at different interaction levels

I am fairly new to multivariate statistics and have run into the following situation: I have a data set of 12 response sets based on a Likert scale (1-5), data which is commonly (in social research) ...
0
votes
0answers
689 views

Calculating influence for PLS with outer loadings and beta coefficient of latent variable

I'm calculating a penalized least squares regression (PLS). Two influence variables are connected to a latent variable. This latent variable has an influence (beta coefficient) of 0.6 on the response (...
11
votes
1answer
25k views

What is the difference between “loadings” and “correlation loadings” in PCA and PLS?

One common thing to do when doing Principal Component Analysis (PCA) is to plot two loadings against each other to investigate the relationships between the variables. In the paper accompanying the ...
13
votes
3answers
6k views

Model assumptions of partial least squares (PLS) regression

I am trying to find information regarding the assumptions of PLS regression (single $y$). I am especially interested in a comparison of the assumptions of PLS with regards to those of OLS regression. ...
3
votes
1answer
2k views

How many components to use when doing a PLS regression?

I'm doing a PLS regression with SAS. My man-a asked me to do find the numbers of principal components for the dataset I'm working with through SAS. As I've never done that before, I'm confused on how ...
2
votes
1answer
3k views

Top R package for PLS regression? [closed]

I'm very new to R and PLS-regression. I would like to know, based on your experience, which R packages for PLS-regression are most highly recommended. My area of application is chemistry.
1
vote
1answer
314 views

step by step analysis for GDP forecast with PLS and SAS

I'm currently trying to do a forecast of GDP , although I'm new to the econometric field , with SAS and the Partial Least Square method. My question is the following: Does anyone have any articles ...
1
vote
1answer
2k views

PLS Regression and collinearity

From what i know PLS regression is used when there is more variables than observations and when there exist multicollinearity between the independent variables. I have data for a regression model that ...
4
votes
0answers
100 views

Is it possible to combine bayesian SEM with PLS SEM?

I have already read some books about both two structural equation models. It seems both SEMs are suitable to the situation with small observations and large variables. I assume to use combine both ...
1
vote
0answers
102 views

Beta Vector from Scaled PLS Model not Reproducing Predicted Responses

I am running R 3.0.2 and using the PLS package to build partial least squares models. The problem I am having is that when I apply feature-scaling to my design matrix the resulting coefficient vector ...
5
votes
1answer
9k views

PCA and PLS: testing variables for significance

I'm trying to understand the process for statistical testing for principal component analysis or partial least squares. Step 1. PCA: I feel that I have a not-terrible understanding of PCA: You find ...
1
vote
0answers
415 views

Testing significant difference between two determination coefficients $R^{2}$ for two PLS-based structural equation models

Two structural equation models were tested (one was based on a sample with 199 individuals and the second one on a sample with 93 individuals). The aim was to test whether the results of the first ...
2
votes
2answers
8k views

Choosing number of components in PLS - without minimum in RMSEP

I use the plsr formula in R and the oscorespls algoritm for analysing my datasets. The datasets are characterized by relatively few number of observations (22), one ...
3
votes
2answers
251 views

What variable importance criterion?

A student of mine developed a heuristic supervised machine learning algorithm for highly multivariate data. It seems to work pretty well, and once the model has been derived from the training data set,...
1
vote
1answer
2k views

PLS-DA with binary predictors in R (package mixOmics)

I am trying to analyse a dataset with at minimum 50 explanatory variables coded as 0 and 1 for presence/absence and a binary response variable (case/control). The goal is to see how the variables can ...
1
vote
1answer
3k views

Why does increasing the number of bootstrapped cases make PLS coefficients significant?

I am running a PLS model with a low number of observations ($n=50$). While several pieces of academic work argue that this sample size is appropriate to run this type of model, I am quite confused ...
4
votes
1answer
3k views

What are guidelines for SmartPLS boostrapping case size?

In SmartPLS, bootstrapping is used to generate the t statistic from which statistical significance can be judged. The two main bootstrapping parameters are case and sample size. Increasing the sample ...
5
votes
1answer
389 views

What's the best way to choose data for Crossvalidation on linear regression settings (PCA, PLS)

We are extracting features from EEG, which is a time dependent signal. We have signals of 10,000 datapoints over 64 channels, and we extract 10 features per timestamp per channel, so at the end we ...
1
vote
1answer
111 views

How do you determine the effect of a simple predictor variable after a PLS analysis?

So, I am running PLS on a genetic dataset with phenotypic and genotypic information. I have about 1000 binary predictors (X), representing molecular markers, for each individual. My indicator ...
6
votes
2answers
3k views

How do you predict the value of new instance, when the training data were normalized?

I estimated a Partial Least Squares model where the X matrix had normalized columns. Now I want to predict the value for a new instance (which is a frequency vector summing to one.) I assume that if I ...
4
votes
1answer
247 views

Under what conditions can a PLS regression model be expressed by single linear equation?

I am confused by two, yet inconsistent for me, facts: Since the PLS regression is expressed by matrices of scores and loadings as $$X=TP^T+E\\Y=UQ^T+F$$ how it can be translated into linear equation ...
2
votes
1answer
1k views

How to fit data with nonlinear partial least squares in R?

I am looking for a way to do nonlinear partial least squares in R or matlab. I thought kernel pls was a way to do it but it is not directly related to nonlinear pls. Do I have to calculate my own ...
4
votes
1answer
2k views

Combining principal component analysis and partial least squares

I know PCA and PLS are considered as alternative method to each other. But I am thinking about a kind of combination of the two in case of lots of predictors with little variability. In that case, ...
0
votes
1answer
1k views

how to find a linear combination of predictors maximizing correlation between its score and dependent variable in R

Please correct me if I am wrong as I am not good at R. I think I can find a linear combination maximizing correlation between predictors and dependent variables by running partial least squares ...
13
votes
1answer
22k views

Partial least squares regression in R: why is PLS on standardized data not equivalent to maximizing correlation?

I am very new in partial least squares (PLS) and I try to understand the output of the R function plsr() in the pls package. Let ...
30
votes
1answer
7k views

PCA, LDA, CCA, and PLS

How are PCA, LDA, CCA, and PLS related? They all seem "spectral" and linear algebraic and very well understood (say 50+ years of theory built around them). They are used for very different things (PCA ...
6
votes
1answer
1k views

Measuring predictive accuracy for multiple dependent variables

In machine learning and in statistics there exist plenty of measures which estimate the performance of a predictive model. For example, classification accuracy, area under ROC curve ... for ...
6
votes
0answers
394 views

What, if any, dissimilarity is preserved in partial least squares (PLS)?

When we perform a principal components analysis (PCA) on a multivariate data set we are interested in finding orthogonal components that explain maximal variance in the data set. We can form a biplot ...
10
votes
2answers
4k views

How to compute the confidence intervals on regression coefficients in PLS?

The underlying model of PLS is that a given $n \times m$ matrix $X$ and $n$ vector $y$ are related by $$X = T P' + E,$$ $$y = T q' + f,$$ where $T$ is a latent $n \times k$ matrix, and $E, ...

1 2 3 4
5