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

Correlation and Regression00:53

Correlation and Regression

2.7K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.7K
Multiple Regression01:25

Multiple Regression

3.4K
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...
3.4K
Coefficient of Correlation01:12

Coefficient of Correlation

7.3K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
7.3K
Correlation of Experimental Data01:23

Correlation of Experimental Data

379
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
379
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

399
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
399
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

368
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...
368

You might also read

Related Articles

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

Sort by
Same author

Beta bursts in SMA mediate anticipatory muscle inhibition.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same author

Menstrual health and Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms: A scoping review.

Women's health (London, England)·2026
Same author

Effects of non-invasive brain stimulation on gait and corticospinal plasticity in children and adolescents with cerebral palsy: A systematic review.

Neurophysiologie clinique = Clinical neurophysiology·2026
Same author

TRAM-01: A phase 2 study of trametinib for pediatric patients with neurofibromatosis type 1 and plexiform neurofibromas.

Neuro-oncology·2026
Same author

Copy number variants reveal divergent genetic and diagnostic cortical signatures across psychiatric disorders.

Research square·2026
Same author

Combinatorial effects of gene dosage, polygenic background and environment on complex traits.

medRxiv : the preprint server for health sciences·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Nov 14, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Conditional canonical correlation estimation based on covariates with random forests.

Cansu Alakuş1, Denis Larocque1, Sébastien Jacquemont2,3

  • 1Department of Decision Sciences, HEC Montréal, Montréal, QC H3T 2A7, Canada.

Bioinformatics (Oxford, England)
|March 11, 2021
PubMed
Summary
This summary is machine-generated.

We introduce Random Forest with Canonical Correlation Analysis (RFCCA), a novel method to analyze variable set relationships while accounting for covariates like age and gender. This approach accurately estimates conditional canonical correlations and tests covariate effects.

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

3.5K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.2K

Related Experiment Videos

Last Updated: Nov 14, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K
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

3.5K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.2K

Area of Science:

  • Multivariate statistics
  • Machine learning
  • Bioinformatics

Background:

  • Canonical Correlation Analysis (CCA) explores relationships between two variable sets.
  • Covariates (e.g., age, gender) can influence these relationships, making standard CCA suboptimal.
  • Conditional CCA methods are needed to account for such covariates.

Purpose of the Study:

  • To develop a novel method, Random Forest with Canonical Correlation Analysis (RFCCA), for estimating conditional canonical correlations.
  • To introduce a significance test for assessing the global effect of covariates on variable set relationships.
  • To evaluate the performance of RFCCA and the significance test via simulations and a real-world EEG data application.

Main Methods:

  • RFCCA utilizes individual trees built with a specialized splitting rule to maximize canonical correlation heterogeneity.
  • The method partitions data based on covariates to estimate conditional relationships.
  • A global significance test is proposed to detect covariate influence.

Main Results:

  • Simulation studies demonstrate RFCCA's accuracy in estimating conditional canonical correlations.
  • The proposed significance test shows well-controlled Type-1 error rates.
  • RFCCA was successfully applied to electroencephalography (EEG) data.

Conclusions:

  • RFCCA provides an effective approach for analyzing relationships between variable sets conditional on covariates.
  • The method and its significance test offer valuable tools for statistical analysis in various fields, including bioinformatics.
  • The RFCCA R package is available on CRAN for public use.