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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

120
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
120
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.1K
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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.1K
2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

253
Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
253
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.7K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
1.7K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
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...
1.4K
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

14.1K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
14.1K

You might also read

Related Articles

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

Sort by
Same author

Genetic variants of p21 and p27 and hepatocellular cancer risk in a Chinese Han population: a case-control study.

International journal of cancer·2012
Same author

Inhibition of TGF-β/Smad signaling by BAMBI blocks differentiation of human mesenchymal stem cells to carcinoma-associated fibroblasts and abolishes their protumor effects.

Stem cells (Dayton, Ohio)·2012
Same author

MAIGO2 is involved in abscisic acid-mediated response to abiotic stresses and Golgi-to-ER retrograde transport.

Physiologia plantarum·2012
Same author

The internal dynamics of mini c TAR DNA probed by electron paramagnetic resonance of nitroxide spin-labels at the lower stem, the loop, and the bulge.

Biochemistry·2012
Same author

Electrochemical depassivation of zero-valent iron for trichloroethene reduction.

Journal of hazardous materials·2012
Same author

Derivation of quantum work equalities using a quantum Feynman-Kac formula.

Physical review. E, Statistical, nonlinear, and soft matter physics·2012

Related Experiment Video

Updated: Jul 31, 2025

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

5.8K

Nonlinear canonical correspondence analysis and its application.

Leru Zhou1, Zhili Liu2, Fei Liu2

  • 1College of Resources and Environment, Xinjiang Agricultural University, Urumqi, 830052, China.

Scientific Reports
|May 10, 2023
PubMed
Summary

We introduce nonlinear canonical correspondence analysis (NCCA) to better model species-environment relationships. NCCA effectively captures nonlinear patterns, outperforming existing methods in explaining response variable variance.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.6K

Related Experiment Videos

Last Updated: Jul 31, 2025

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

5.8K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.6K

Area of Science:

  • Ecology
  • Environmental Science
  • Statistical Modeling

Background:

  • Canonical Correspondence Analysis (CCA) is widely used for gradient analysis.
  • CCA assumes linear relationships between species and environmental factors, limiting its accuracy for unimodal species responses.
  • Existing methods like polynomial CCA (PCCA) have limitations in explaining response variance.

Purpose of the Study:

  • To develop a nonlinear canonical correspondence analysis (NCCA) method.
  • To accurately model the nonlinear relationships between species distribution and environmental gradients.
  • To improve upon existing methods in explaining the variance of response variables.

Main Methods:

  • NCCA identifies nonlinear explanatory factors via correlation and LASSO regression.
  • It employs linear regression and an improved heuristic optimal quadratic approximation.
  • Chi-square transformed response variables are fitted to capture nonlinearities.

Main Results:

  • NCCA effectively reflects nonlinear species-environment interactions.
  • A biplot visualization aids in understanding environmental factor impacts on species distribution.
  • NCCA demonstrates superior performance in explaining response variable variance compared to PCCA.

Conclusions:

  • NCCA provides a more accurate approach for ecological gradient analysis.
  • The method enhances understanding of complex species-environment dynamics.
  • NCCA offers advantages over previous methods, particularly in variance explanation.