Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
Response Surface Methodology01:16

Response Surface Methodology

258
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
258
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

293
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
293
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

2.1K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
2.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CeO<sub>2</sub>@CA@PEI@dextran improves drug sensitivity in chronic myelogenous Leukemia through scavenging free radical efficiently.

Journal of colloid and interface science·2026
Same author

A Non-Contact Electronic Nose System Based on Off-Gas Response for Real-Time NH<sub>4</sub><sup>+</sup> Monitoring in Fermentation.

Sensors (Basel, Switzerland)·2026
Same author

FDX1 Depletion Activates CD8<sup>+</sup> T Cell Antitumor Immunity by Promoting DMBT1 Secretion in Cuproptosis of Colorectal Cancer.

Cancer science·2026
Same author

Pore-Scale Mechanisms and Enhanced Oil Recovery Performance of Polymer-Assisted Winsor Microemulsion Systems: From Single Systems to Optimized Slug Design.

Polymers·2026
Same author

Smartphone-Operated FRET Platform with Aggregation-Induced Emission MOFs and Spiky Carbon Nanospheres for Rapid Quantification of CA125 and HE4.

ACS sensors·2026
Same author

Establishment and Validation of a Cell-Based Relative Potency Method for Respiratory Syncytial Virus mRNA Vaccine Drug Substance.

Vaccines·2026
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Sep 8, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.9K

Partial least squares regression with compositional response variables and covariates.

Jiajia Chen1, Xiaoqin Zhang1, Karel Hron2

  • 1School of Statistics, Shanxi University of Finance and Economics, Taiyuan, People's Republic of China.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

Partial least squares (PLS) regression offers a solution for analyzing compositional data when standard methods fail. This study details PLS regression for multiple compositional variables, demonstrating its invariance across coordinate systems.

Keywords:
62H1262H8662J05Compositional datacentered log-ratio coefficientscoordinateslinear regression modelpartial least squares

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

Related Experiment Videos

Last Updated: Sep 8, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

Area of Science:

  • Statistics
  • Chemometrics
  • Bioinformatics

Background:

  • Compositional data analysis commonly uses log-ratio transformations for regression.
  • Standard least squares regression fails with more covariates than observations.
  • Partial least squares (PLS) regression is a potential alternative for such scenarios.

Purpose of the Study:

  • To detail partial least squares (PLS) regression for compositional data analysis.
  • To investigate PLS regression for multiple compositional response and covariate variables.
  • To demonstrate the coordinate system invariance of the PLS model.

Main Methods:

  • Developed the PLS regression model using log-ratio coordinates for compositional variables.
  • Expressed the PLS model directly within the simplex.
  • Proved the invariance of the PLS model under different coordinate systems (e.g., ilr, clr).

Main Results:

  • Established a robust PLS regression framework for high-dimensional compositional data.
  • Demonstrated the model's invariance to the choice of log-ratio coordinates.
  • Provided methods for parameter estimation and inference within the PLS model.

Conclusions:

  • Partial least squares (PLS) regression effectively handles regression with multiple compositional variables.
  • The PLS model's invariance ensures consistent results regardless of the chosen log-ratio transformation.
  • Applied the PLS model with centered log-ratio (clr) coefficients to analyze metabolite relationships in Astragali Radix studies.