Factor and Correlation Analysis of Multivariate Environmental Data

1992 ◽  
pp. 139-180 ◽  
Author(s):  
Philip K. Hopke
Keyword(s):  
Correlation Analysis ◽  
Environmental Data

A new method for correlation analysis of compositional (environmental) data – a worked example

2017 ◽  
Vol 607-608 ◽  
pp. 965-971 ◽  
Author(s):  
C. Reimann ◽  
P. Filzmoser ◽  
K. Hron ◽  
P. Kynčlová ◽  
R.G. Garrett
Keyword(s):  
Correlation Analysis ◽  
Environmental Data ◽  
New Method ◽  
Worked Example

scholarly journals Hierarchical Canonical Correlation Analysis Reveals Phenotype, Genotype, and Geoclimate Associations in Plants

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Raphael Petegrosso ◽  
Tianci Song ◽  
Rui Kuang
Keyword(s):  
Arabidopsis Thaliana ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Geographical Origin ◽  
Local Environment ◽  
Environmental Data ◽  
Phenotype Prediction ◽  
Genomic Features ◽  
Cross Covariance

The local environment of the geographical origin of plants shaped their genetic variations through environmental adaptation. While the characteristics of the local environment correlate with the genotypes and other genomic features of the plants, they can also be indicative of genotype-phenotype associations providing additional information relevant to environmental dependence. In this study, we investigate how the geoclimatic features from the geographical origin of the Arabidopsis thaliana accessions can be integrated with genomic features for phenotype prediction and association analysis using advanced canonical correlation analysis (CCA). In particular, we propose a novel method called hierarchical canonical correlation analysis (HCCA) to combine mutations, gene expressions, and DNA methylations with geoclimatic features for informative coprojections of the features. HCCA uses a condition number of the cross-covariance between pairs of datasets to infer a hierarchical structure for applying CCA to combine the data. In the experiments on Arabidopsis thaliana data from 1001 Genomes and 1001 Epigenomes projects and climatic, atmospheric, and soil environmental variables combined by CLIMtools, HCCA provided a joint representation of the genomic data and geoclimate data for better prediction of the special flowering time at 10°C (FT10) of Arabidopsis thaliana. We also extended HCCA with information from a protein-protein interaction (PPI) network to guide the feature learning by imposing network modules onto the genomic features, which are shown to be useful for identifying genes with more coherent functions correlated with the geoclimatic features. The findings in this study suggest that environmental data comprise an important component in plant phenotype analysis. HCCA is a useful data integration technique for phenotype prediction, and a better understanding of the interactions between gene functions and environment as more useful functional information is introduced by coprojections of multiple genomic datasets.

Vegetation studies at Polar Bear Pass, Bathurst Island, N.W.T. II. Vegetation–environment relationships

1983 ◽  
Vol 61 (6) ◽  
pp. 1637-1646 ◽  
Author(s):  
J. W. Sheard ◽  
Dorothy W. Geale
Keyword(s):  
Correlation Analysis ◽  
Principal Components Analysis ◽  
Canonical Correlation Analysis ◽  
Principal Components ◽  
Canonical Correlation ◽  
Environmental Gradient ◽  
Environmental Influence ◽  
Environmental Data ◽  
Components Analysis ◽  
Soil Moisture Status

Vegetation–environment relationships are defined with the aid of principal-components analysis and canonical correlation analysis. In both the uplands and lowlands a moisture gradient, determined by measuring gravimetric moisture and indicated by organic carbon, is the most important environmental influence on the vegetation. In the uplands this gradient is also associated with snow depth (drifting) and in the lowlands with conductivity. The second environmental gradient in the uplands is associated with depth to permafrost and its soil textural correlates. Thus soil texture, independent of its effect on soil moisture status, influences the distribution of plant communities. In the lowlands the second environmental gradient is less clear but is also associated with depth to permafrost and, in addition, elevation and CaCO3 equivalent. Canonical correlation analysis shows that the components extracted by principal-components analysis of the vegetation data did not conform to the important trends of variation in the environmental data. Principal-components analysis is nevertheless an essential means of data reduction prior to the application of canonical correlation. The statistical model used in the study has potential advantages over the independent use of either principal-components analysis or canonical correlation.

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

The German Strengths and Difficulties Questionnaire (SDQ)

2010 ◽  
Vol 26 (4) ◽  
pp. 256-262 ◽  
Author(s):  
Ulrike Petermann ◽  
Franz Petermann ◽  
Ina Schreyer
Keyword(s):  
Correlation Analysis ◽  
Teacher Ratings ◽  
Screening Instrument ◽  
Preschool Age ◽  
Strengths And Difficulties Questionnaire ◽  
Preschool Age Children ◽  
Discriminant Analyses ◽  
Strengths And Difficulties ◽  
Behavioral Attributes ◽  
And Behavior

The Strengths and Difficulties Questionnaire (SDQ) is a screening instrument that addresses positive and negative behavioral attributes of children and adolescents. Although this questionnaire has been used in Germany to gather information from parents and teachers of preschoolers, few studies exist that verify the validity of the German SDQ for this age. In the present study, teacher ratings were collected for 282 children aged 36 to 60 months (boys = 156; girls = 126). Likewise, teacher ratings were collected with another German checklist for behavior problems and behavior disorders at preschool age (Verhaltensbeurteilungsbogen für Vorschulkinder, VBV 3–6). Moreover, children’s developmental status was assessed. Evaluation included correlation analysis as well as canonical correlation analysis to assess the multivariate relationship between the set of SDQ variables and the set of VBV variables. Discriminant analyses were used to clarify which SDQ variables are useful to differentiate between children with or without developmental delay in a multivariate model. The results of correlation and discriminant analyses underline the validity of the SDQ for preschoolers. According to these results, the German teacher SDQ is recommended as a convenient and valid screening instrument to assess positive and negative behavior of preschool age children.

Analysis of Maximum Expiratory Flow Volume Curves Using Canonical Correlation Analysis

1985 ◽  
Vol 24 (02) ◽  
pp. 91-100 ◽  
Author(s):  
W. van Pelt ◽  
Ph. H. Quanjer ◽  
M. E. Wise ◽  
E. van der Burg ◽  
R. van der Lende
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Flow Volume ◽  
Non Linear ◽  
Maximum Expiratory Flow ◽  
Expiratory Flow ◽  
Relationship Of ◽  
The Relationship ◽  
Age And Sex

SummaryAs part of a population study on chronic lung disease in the Netherlands, an investigation is made of the relationship of both age and sex with indices describing the maximum expiratory flow-volume (MEFV) curve. To determine the relationship, non-linear canonical correlation was used as realized in the computer program CANALS, a combination of ordinary canonical correlation analysis (CCA) and non-linear transformations of the variables. This method enhances the generality of the relationship to be found and has the advantage of showing the relative importance of categories or ranges within a variable with respect to that relationship. The above is exemplified by describing the relationship of age and sex with variables concerning respiratory symptoms and smoking habits. The analysis of age and sex with MEFV curve indices shows that non-linear canonical correlation analysis is an efficient tool in analysing size and shape of the MEFV curve and can be used to derive parameters concerning the whole curve.

Detection of the Structural Failing Point Using Correlation and Local Correlation Analysis

2020 ◽  
Author(s):  
Hyounkyun Oh ◽  
Younghan Jung ◽  
Junyong Ahn ◽  
Sujin Kim ◽  
M. Myung Jeong
Keyword(s):  
Correlation Analysis ◽  
Local Correlation
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