Phylogenetic Analysis of Phenotypic Covariance Structure. I. Contrasting Results from Matrix Correlation and Common Principal Component Analysis

Evolution ◽  
1997 ◽  
Vol 51 (2) ◽  
pp. 571 ◽  
Author(s):  
Scott J. Steppan
Keyword(s):  
Principal Component Analysis ◽  
Phylogenetic Analysis ◽  
Covariance Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Matrix Correlation

scholarly journals Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)

2020 ◽  
Author(s):  
Christiane Scherer ◽  
James Grover ◽  
Darby Kammeraad ◽  
Gabe Rudy ◽  
Andreas Scherer
Keyword(s):  
Principal Component Analysis ◽  
Population Genetics ◽  
Phylogenetic Analysis ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Comprehensive Analysis ◽  
Large Data Sets ◽  
Data Sets ◽  
Whole Genomes

AbstractSince the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year.SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.

scholarly journals Metode ROBPCA (Robust Principal Component Analysis) dan Clara (Clustering Large Area) pada Data dengan Outlier

2020 ◽  
Vol 13 (2) ◽  
pp. 11
Author(s):  
Bekti Endar Susilowati ◽  
Pardomuan Robinson Sihombing
Keyword(s):  
Principal Component Analysis ◽  
Covariance Structure ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Large Area ◽  
Robust Principal Component Analysis ◽  
Data Set ◽  
Silhouette Width ◽  
The World

Principal Component Analysis (PCA) merupakan salah satu analisis multivariat yang digunakan untuk mengganti variable dengan Principal Component yang sedikit jumlahnya namun tidak terlalu banyak informasi yang hilang. Atau dengan kata lain, it used to explain the underlying variance-covariance structure of the large data set of variables through a few linear combination of these variables. PCA sangat dipengaruhi oleh kehadiran outlier karena didasarkan pada matriks kovarian yang sensitive terhadap outlier. Oleh karena itu, pada analisis ini akan digunakan PCA yang robust terhadap outlier yaitu ROBPCA atau PCA Hubert. Selanjutnya, dari Principal Component yang terbentuk digunakan sebagai input (masukan) untuk cluster analysis dengan metode Clara (Clustering Large Area). Clustering Large Area merupakan salah satu metode k-medoids yang robust terhadap outlier dan baik digunakan pada data dalam jumlah besar. Dalam studi kasus terhadap variabel penyusun indeks kebahagiaan berdasarkan The World Happiness Report 2018 dengan metode Clara yang menggunakan jarak manhattan didapatkan nilai rata-rata Overall Average Silhouette Width yang terbaik pada 5 cluster. 

Wood Structure of the Rosaceae in Relation to Ecology, Habit and Phenology

IAWA Journal ◽  
1992 ◽  
Vol 13 (3) ◽  
pp. 307-349 ◽  
Author(s):  
Shu-Yin Zhang ◽  
Pieter Baas ◽  
Marinus Zandee
Keyword(s):  
Principal Component Analysis ◽  
Phylogenetic Analysis ◽  
Correlation Analysis ◽  
Path Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Distribution Area ◽  
Wood Structure ◽  
Simple Correlation ◽  
Entire Distribution

Twelve wood anatornical characters, together with broad parameters from ecology, habit and phenology were subjected to simple correlation analysis, path analysis and principal component analysis, in a total sampie of over 470 specimens belonging to 271 species of the Rosaceae from the entire distribution area of the farnily. The functional, developmental and systematic implications of the resulting relations are discussed. Based on the present analysis of ecological trends and previous phylogenetic analysis, a tentative scenario for the evolution of the Rosaceae is offered.

scholarly journals A Comparison of Principal Component Analysis and Maximum Likelihood Factor Analysis in Bank Health Ratio

2021 ◽  
Vol 22 (2) ◽  
pp. 147
Author(s):  
Firdaus Firdaus ◽  
Sigit Nugroho ◽  
Haryo Widodo
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Maximum Likelihood ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction Methods ◽  
Financial Ratios ◽  
Financial Ratio ◽  
Matrix Correlation ◽  
Health Ratio

The use of factor analysis methods to reduce variable dimensions is generally known and has been used in various disciplines. The two famous extraction methods of factor analysis are principal component analysis and maximum likelihood. This study aimed to compare both, principal component analysis and maximum likelihood. By their constructed matrix correlation, applied to bank financial ratios. The study is developed from an initial set of 22 ratios of healthy indexed banks. The use of bank financial data aims to identify the structure of the financial ratio of healthy indexed banks. There are 10 variables satisfying the criteria of factor analysis techniques to be considered in the analysis. Both principal component analysis and maximum likelihood suggest three factors that can be used to represent 10 variables.Keywords: factor analysis; principal component analysis; maximum likelihood; financial ratios; bank health.

Products of Gaussians and Probabilistic Minor Component Analysis

2002 ◽  
Vol 14 (5) ◽  
pp. 1169-1182 ◽  
Author(s):  
C. K. I. Williams ◽  
F. V. Agakov
Keyword(s):  
Principal Component Analysis ◽  
Covariance Structure ◽  
Minor Component ◽  
Principal Component ◽  
Component Analysis ◽  
Minor Component Analysis ◽  
A Minor ◽  
Analysis Models ◽  
Probabilistic Principal Component Analysis ◽  
Individual Expert

Recently, Hinton introduced the products of experts architecture for density estimation, where individual expert probabilities are multiplied and renormalized. We consider products of gaussian “pancakes” equally elongated in all directions except one and prove that the maximum likelihood solution for the model gives rise to a minor component analysis solution. We also discuss the covariance structure of sums and products of gaussian pancakes or one-factor probabilistic principal component analysis models.

Eutrema bulbiferum (Brassicaceae), a new species with bulbils from Hunan, China

Phytotaxa ◽  
2015 ◽  
Vol 219 (3) ◽  
pp. 233 ◽  
Author(s):  
Yan Xiao ◽  
Chun Li ◽  
Tung-Yu Hsieh ◽  
Dai-Ke Tian ◽  
Jian-Jun Zhou ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Phylogenetic Analysis ◽  
New Species ◽  
Internal Transcribed Spacer ◽  
Principal Component ◽  
Component Analysis ◽  
Leaf Axil ◽  
Morphometric Characters ◽  
Stem Base ◽  
A New Species

Eutrema bulbiferum, a unique new species of Brassicaceae found in the limestone areas of Longshan and Jishou, Hunan, China, is described and illustrated. This species is most similar to E. tenue and E. yunnanense, but can be easily distinguished by its rosulate fleshy bulbils in the leaf axil or near the stem base, fewer ovules per ovary, slightly 4-angled short wand-like silique, and bended silique apex with a beak. E. bulbiferum is categorized into Eutrema by phylogenetic analysis based on the nuclear internal transcribed spacer (ITS). It is also clearly separated from E. tenue and E. yunnanense by the results of both phylogenetic analysis and Principal Component Analysis (PCA) based on morphometric characters.

scholarly journals Differences between strawberry cultivars based on principal component analysis

2020 ◽  
Vol 8 (6) ◽  
pp. 136-145
Author(s):  
Darlei Michalski Lambrecht ◽  
Alessandro Dal'Col Lúcio ◽  
Maria Inês Diel ◽  
Denise Schmidt ◽  
Francieli De Lima Tartaglia ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Latent Variables ◽  
Covariance Structure ◽  
Principal Component ◽  
Titratable Acidity ◽  
Component Analysis ◽  
Soluble Solids ◽  
Family Farms ◽  
Multivariate Technique ◽  
Strawberry Cultivars

Strawberry culture is of extreme economic importance, especially for small producers, as it has the capacity to add value to small family farms, in addition to absorbing family labor. Principal component analysis (PCA) is a multivariate technique for modeling covariance structure, where a basic idea is to find latent variables that represent linear combinations of a group of variables under study, which in turn are related between itself. In this way, the objective of the work was estimated, through the analysis of main components (PCA), as relationships between development variables, products and fruit quality in different strawberry cultivars. The design used was a randomized block with 11 treatments, consisting of strawberry cultivars of Italian and American origins, with four replications. During the culture cycle, the following variables were evaluated: phyllochron, number of commercial (FC) and non-commercial (FNC) fruits, mass of commercial (MFC) and non-commercial (MFNC) fruits, total titratable acidity (AT), total soluble quantities (SST) and total soluble ratio, titratable acidity (SST / AT). The relationships between the variables were evaluated by the PCA analysis and the results were plotted on the Biplot graph. From the analysis, it was possible to identify the relationships between the variables that show how to cultivate the same photoperiod and the same characteristic origin. Growing short photoperiods are more productive, for example, as the neutral photoperiod has less phyllochron and less acidity. The increase in soluble solids can cause a reduction in acidity, which is one of the characteristics that add flavor to the fruit.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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