Morphological variation in the Pacific sand dollar Dendraster excentricus

1995 ◽  
Vol 73 (3) ◽  
pp. 576-583
Author(s):  
Raymond K. Nakamura

Correlations between latitude, habitat, and morphology in the Pacific sand dollar Dendraster excentricus were identified with principal component analysis. Twenty-two lengths were measured on the oral and aboral surfaces of 615 specimens from 31 sites. Samples were divided at latitude 34°30′N (Point Conception) and into bay and coastal habitats by relative wave exposure. Principal components (PC) were estimated from a correlation matrix of sample means of log-transformed measurements. PC1 accounted for 90% of the variance and was a measure of overall size. All 22 PC1 coefficients were positive and differed significantly from 0, according to a jackknifing test. PC1 differed significantly with latitude (ANOVA, p < 0.01) but not habitat. Southern populations tended to be smaller. PC2 accounted for 5% of the variance and described overall shape. Of the 22 variables, 13 had significant coefficients that varied in sign. PC2 varied significantly with habitat (ANOVA, p < 0.05) but not latitude. In coastal populations, the peristome and petaloids tended to be more posteriorly positioned and the food grooves were branched more peripherally. These features correspond to the greater tendency for coastal specimens to use their posterior end to suspension feed.

2012 ◽  
Vol 166-169 ◽  
pp. 2740-2743
Author(s):  
Hao Yao Zheng ◽  
De Li Zhuang ◽  
Luo Shi Xu ◽  
Zhi Fei Long ◽  
De Wei Yang

Many sluices were damaged seriously after several decades’ use. In sluice safety level’s classification standard, there are quantitative indexes and qualitative indexes. Principal component analysis was used to evaluate the sluice. First the data were standardized, correlation matrix was got, and then confirmed principal components and weight, through this step principal components’ value and total value were received, finally sluice risk grades were determined and they could be ordered. The result is objective and reasonable, so we assess sluice easily.


1992 ◽  
Vol 75 (3) ◽  
pp. 929-930 ◽  
Author(s):  
Oliver C. S. Tzeng

This note summarizes my remarks on the application of reliability of the principal component and the eigenvalue-greater-than-1 rule for determining the number of factors in principal component analysis of a correlation matrix. Due to the unpredictability and uselessness of the reliability approach and the Kaiser-Guttman rule, research workers are encouraged to use other methods such as the scree test.


2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.


2017 ◽  
Vol 921 (3) ◽  
pp. 24-29 ◽  
Author(s):  
S.I. Lesnykh ◽  
A.K. Cherkashin

The proposed procedure of integral mapping is based on calculation of evaluation functions on the integral indicators (II) taking into account the feature of the local geographical environment, when geosystems in the same states in the different environs have various estimates. Calculation of II is realized with application of a Principal Component Analysis for processing of the forest database, allowing to consider in II the weight of each indicator (attribute). The final value of II is equal to a difference of the first (condition of geosystem) and the second (condition of environmental background) principal components. The evaluation functions are calculated on this value for various problems of integral mapping. The environmental factors of variability is excluded from final value of II, therefore there is an opportunity to find the invariant evaluation function and to determine coefficients of this function. Concepts and functions of the theory of reliability for making the evaluation maps of the hazard of functioning and stability of geosystems are used.


2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.


2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.


2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.


2015 ◽  
Vol 36 (6) ◽  
pp. 3909
Author(s):  
Michelle Santos da Silva ◽  
Luciana Shiotsuki ◽  
Raimundo Nonato Braga Lôbo ◽  
Olivardo Facó

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.


2020 ◽  
Author(s):  
Gabriel Gonçalves da Costa ◽  
Bruno Francisco Teixeira Simões ◽  
Alessandra da Silva Pereira

Objective: To identify dietary patterns in food availability data at the global level using multivariate statistical methodology, to associate the identified dietary patterns with socioeconomic data and to analyze the adequacy of the applied statistical methods in data of food availability. Methods: Principal Component Analysis was applied to food availability data of 172 UN registered countries available at FAOSTAT database in Food Balance Sheets section. The Principal Components were correlated with socioeconomic data available from the World Bank database. Results: Five principal components were identified, each characterizing a dietary pattern. The first one, a westernized dietary pattern, was composed of energy-dense and processed foods, foods of animal origin, alcoholic beverages, but also, albeit less, by vegetables, fruits and nuts, being correlated with income, urbanization and trade liberalization. This westernized pattern was characterized more by western, animal origin and processed foods, yet preserving unprocessed and regional foods. The other dietary patterns were three agricultural patterns characterized more by regional foods, especially starchy staples, and one coastal dietary pattern composed of fish and seafoods, being associated with GINI index, poverty, and female labor force. Conclusions: Principal Component Analysis was adequate to identify dietary patterns in food availability data. A westernized dietary pattern was identified, being associated with income, urbanization and trade liberalization. This association did not occur for the remain of the dietary patterns identified, these being less driven by economic development.


2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.


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