scholarly journals Estudo Pluvial no Nordeste do Brasil Utilizando Análise Multivariada (Rain Study in Northeast Brazil Using Multivariate Analysis)

2012 ◽  
Vol 5 (3) ◽  
pp. 448
Author(s):  
Winicius Santos Araújo ◽  
Francisco Assis Saviano Souza ◽  
José Ivaldo Barbosa de Brito ◽  
Lourivaldo Mota Lima
Keyword(s):  
Multivariate Analysis ◽  
Principal Components ◽  
Climatic Variability ◽  
Principal Component ◽  
Original Data ◽  
Climate Indices ◽  
Multivariate Techniques ◽  
Northeast Brazil ◽  
Homogeneous Groups ◽  
Rain Study

O objetivo deste trabalho foi estudar a dinâmica de variabilidade climática espacial e temporal da pluviosidade nos nove estados do Nordeste Brasileiro, utilizando as técnicas multivariadas de Análise de Componentes Principais (ACP) e Análise de Agrupamento (AA). Foram utilizadas médias mensais da precipitação pluvial e de mais 11 índices climáticos pluviais definidos pela OMM (Organização Meteorológica Mundial) obtidas a partir de dados diários de 258 estações meteorológicas e/ou postos pluviométricos, fornecidos pela antiga rede de postos da SUDENE/DCA, referentes a um período de 47 anos (1960-2006). Com base nesses dados, foram aplicadas as técnicas de ACP e AA à média pluvial e aos 11 índices pluviais. Na ACP, nove índices climáticos e a média pluvial foram representados por três componentes principais e estas explicaram mais de 90% da variância original dos dados. Na AA, nove índices apresentaram quatro grupos homogêneos de atuação. Palavras - chave: Componentes principais, agrupamento, índices pluviais.  Rain Study in Northeast Brazil Using Multivariate Analysis  ABSTRACTThe aim of this work was to study the dynamics of spatial and temporal climatic variability in rainfall in the nine states of Northeast Brazil, using the multivariate techniques of Principal Component Analysis (PCA) and Cluster Analysis (CA). We used monthly averages of rainfall and 11 climate indices over rain defined by WMO (World Meteorological Organization) obtained from daily data from 258 meteorological stations and/or climatic stations, supplied by the former service station network SUDENE/DCA, referring a period of 47 years (1960-2006). Based on these data, we applied the techniques the average PCA and CA rain and 11 rain indices. In ACP, nine climate indices and average rainfall were represented by three principal components and these accounted for more than 90% of the variance of the original data. In AA, nine indices showed four homogeneous groups of activity.Keywords: Principal components; cluster; rain indices.

scholarly journals Multivariate techniques in the analysis of carcass traits of Morada Nova breed sheep

2018 ◽  
Vol 48 (9) ◽  
Author(s):  
Déborah Galvão Peixôto Guedes ◽  
Maria Norma Ribeiro ◽  
Francisco Fernando Ramos de Carvalho
Keyword(s):  
Total Variation ◽  
Principal Components ◽  
Strong Association ◽  
Principal Component ◽  
Original Data ◽  
Discriminatory Power ◽  
Multivariate Techniques ◽  
Data Set ◽  
Canonical Variables ◽  
And Performance

ABSTRACT: This study aimed to use multivariate techniques of principal component analysis and canonical discriminant analysis in a data set from Morada Nova sheep carcass to reduce the dimensions of the original data set, identify variables with the best discriminatory power among the treatments, and quantify the association between biometric and performance traits. The principal components obtained were efficient in reducing the total variation accumulated in 19 original variables correlated to five linear combinations, which explained 80% of the total variation present in the original variables. The first two principal components together accounted for 56.12% of the total variation of the evaluated variables. Eight variables were selected using the stepwise method. The first three canonical variables were significant, explaining 92.25% of the total variation. The first canonical variable showed a canonical correlation coefficient of 0.94, indicating a strong association between biometric traits and animal performance. Slaughter weight and hind width were selected because these variables presented the highest discriminatory power among all treatments, based on standard canonical coefficients.

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

scholarly journals Spatial and Temporal Analysis of Drought in Nepal using Standardized Precipitation Index and its Relationship with Climate Indices

1970 ◽  
Vol 7 (1) ◽  
pp. 59-74 ◽  
Author(s):  
M Sigdel ◽  
M Ikeda
Keyword(s):  
Principal Component Analysis ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Precipitation Index ◽  
Climate Indices ◽  
Indian Ocean Dipole Mode ◽  
Time Space

Drought over Nepal is studied on the basis of precipitation as a key parameter. Using monthly mean precipitation data for a period of 33 years, Standardized Precipitation Index (SPI) is produced for the drought analysis with the time scale of 3 months (SPI-3) and 12 months (SPI-12) as they are applicable for agriculture and hydrological aspects, respectively. Time-space variability is explored based on Principal Component Analysis (PCA) along with Rotated PCA (RPCA). Four rotated components were explored for both SPI-3 and SPI-12 representing climatic variability with cores over eastern, central and western Nepal separately. Droughts associated with SPI-3 occurred almost evenly over these regions. Droughts associated with SPI-12 were consistent with SPI-3 for summer, since summer precipitation dominates annual precipitation. Connection between SPI and the climate indices such as Southern Oscillation Index (SOI) and Indian Ocean Dipole Mode Index (DMI) was studied, suggesting that one of the causes for summer droughts is El Nino, while the winter droughts could be related with positive DMI. Keywords: Standardized Precipitation Index; Nepal; Principal component analysis; Drought DOI: http://dx.doi.org/10.3126/jhm.v7i1.5617 JHM 2010; 7(1): 59-74

Cluster Analysis of the Chrysalis Silk Production Provinces in China

2013 ◽  
Vol 796 ◽  
pp. 323-326
Author(s):  
Huan Wang ◽  
Ting Ting Tao ◽  
Wan Chun Fei
Keyword(s):  
Principal Components ◽  
Clustering Analysis ◽  
Structural Adjustment ◽  
Price Index ◽  
Consumer Price Index ◽  
Principal Component ◽  
Original Data ◽  
Basic Reference ◽  
Per Capita ◽  
Production Areas

In this article, the yield of mulberry cocoon, the output of raw silk, the output of silk fabric, the consumer price index, the GDP per capita and the per capita income from 1999 to 2011 were analyzed for their principal components on the major production areas of cocoon and silk in China. The principal component analysis can ensure the smallest loss of the original data, to replace the multi-variables with a few synthetic variables, to simplify the data structure, and objectively determine the weights. The distances and similarities between provincial principal components, which were regarded as multivariable time series, were analyzed and computed, and clustering analysis were carried out. The result can be used as a basic reference for the industrial configuration and structural adjustment of silk in China.

scholarly journals Climate indices for Baltic States from principal component analysis

2017 ◽  
Author(s):  
Liga Bethere ◽  
Juris Sennikovs ◽  
Uldis Bethers
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Climate Models ◽  
Regional Climate ◽  
Principal Component ◽  
Regional Climate Models ◽  
Component Analysis ◽  
Climate Indices ◽  
Baltic States ◽  
The Baltic

Abstract. We used principal component analysis (PCA) to derive climate indices that describe the main spatial features of the climate in the Baltic States (Estonia, Latvia and Lithuania). Monthly mean temperature and total precipitation values derived from the ensemble of bias-corrected regional climate models (RCM) were used. Principal components were derived for years 1961–1990. The first three components describe 92 % of the variance of the initial data and were chosen as climate indices in further analysis. Spatial patterns of these indices and their correlation with the initial variables were analyzed and it was observed that higher values of each index corresponded to: (1) less distinct seasonality, (2) warmer and (3) wetter climate. The loadings from the chosen principal components were then further used to calculate values of the climate indices for years 2071–2100. Overall increase was found for all three indices with minimal changes in their spatial pattern.

scholarly journals PCR, PLS, or OPLS Evaluation of different regression techniques for hypothesis generation

2021 ◽  
Author(s):  
Avani Ahuja
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Principal Components ◽  
Selection Process ◽  
Principal Component ◽  
Hypothesis Generation ◽  
Multivariate Techniques ◽  
Response Variable ◽  
Number Of Principal Components ◽  
Latent Structures

In the current era of ‘big data’, scientists are able to quickly amass enormous amount of data in a limited number of experiments. The investigators then try to hypothesize about the root cause based on the observed trends for the predictors and the response variable. This involves identifying the discriminatory predictors that are most responsible for explaining variation in the response variable. In the current work, we investigated three related multivariate techniques: Principal Component Regression (PCR), Partial Least Squares or Projections to Latent Structures (PLS), and Orthogonal Partial Least Squares (OPLS). To perform a comparative analysis, we used a publicly available dataset for Parkinson’ disease patien ts. We first performed the analysis using a cross-validated number of principal components for the aforementioned techniques. Our results demonstrated that PLS and OPLS were better suited than PCR for identifying the discriminatory predictors. Since the X data did not exhibit a strong correlation, we also performed Multiple Linear Regression (MLR) on the dataset. A comparison of the top five discriminatory predictors identified by the four techniques showed a substantial overlap between the results obtained by PLS, OPLS, and MLR, and the three techniques exhibited a significant divergence from the variables identified by PCR. A further investigation of the data revealed that PCR could be used to identify the discriminatory variables successfully if the number of principal components in the regression model were increased. In summary, we recommend using PLS or OPLS for hypothesis generation and systemizing the selection process for principal components when using PCR.rewordexplain later why MLR can be used on a dataset with no correlation

scholarly journals Ranking of energy consumption objects using the principal components method

2020 ◽  
Vol 1 (57) ◽  
pp. 39-44
Author(s):  
A. Perekrest ◽  
V. Ogar ◽  
О. Vovna ◽  
M. Kushch-Zhyrko
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Correlation Analysis ◽  
Principal Components ◽  
Initial Conditions ◽  
Principal Component ◽  
Component Analysis ◽  
Energy Resources ◽  
Technical Documentation ◽  
Homogeneous Groups

Ensuring comfortable conditions in civil buildings requires the implementation of tasks of monitoring and forecasting the cost of energy resources, as well as energy-efficient management of heating engineering systems and its equipment. The implementation of appropriate automation and monitoring solutions allows the accumulation of a significant amount of data. To increase the informativeness of the analysis of energy efficiency in the operation of civil buildings a model of their information ranking was developed using correlation analysis and the principal component analysis. Based on the interdisciplinary methodology of data analysis (CRISP-DM), the basic indicators were determined for the accepted initial conditions on electricity and heat consumption of the university buildings and the matrix of correlation coefficients of their interrelation was estimated. Certain data (external volume and area of the building and average temperature values for this region according to the norm) are obtained from the technical documentation of buildings and available from open sources, others (amount of consumed heat and electricity, indoor temperature) are determined during operation and characterize the efficiency of energy resources in the building. At the initial stage, a correlation analysis of the relationship between the main parameters that characterize buildings and their consumption of energy resources. The principal component analysis was used to reduce the dimensionality of the feature set of data and to identify homogeneous groups of energy consumption objects. The obtained four components explain about 90% of the variance of the initial data and characterize the efficiency of energy use in terms of temperature, volume and coefficient of heating degree days of the heating season. The obtained results are recommended for implementation in modern systems of energy monitoring and municipal energy management as applied models for diagnosing abnormal situations and sound management decisions. Keywords – buildings; energy consumption; principal components; machine learning; data segmentation.

Estimation of Diversity of Living Standards in Polish Voivodships in 2003–2013 Using Principal Components for Multidimensional Functional Data and Cluster Analysis

2016 ◽  
Vol 63 (1) ◽  
pp. 81-97
Author(s):  
Mirosław Krzyśko ◽  
Agnieszka Majka ◽  
Waldemar Wołyński
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Components ◽  
Functional Data ◽  
Principal Component ◽  
Component Analysis ◽  
Living Standards ◽  
Homogeneous Groups ◽  
And Cluster Analysis

The paper presents an estimation of life standard diversity for residents of Polish voivodships in 2003–2013. The principal component analysis was applied for multidimensional functional data and the dendrite method was used for cluster analysis. These methods made it possible to isolate relatively homogeneous groups of voivodships that had similar values of characteristics under consideration, for the whole period at issue.

Evaluation of Groundwater Quality at an Industrial Park Site Zone Using Statistical Analyses: A Case Study in Taiwan

2013 ◽  
Vol 457-458 ◽  
pp. 1581-1584
Author(s):  
Bo Ming Yang ◽  
Zong Han Yang ◽  
Jong Kang Liu ◽  
Hui Yu Lee ◽  
Chih Ming Kao
Keyword(s):  
Statistical Analysis ◽  
Groundwater Quality ◽  
Quality Indicators ◽  
Principal Components ◽  
Multivariate Statistical Analysis ◽  
Organic Pollutant ◽  
Principal Component ◽  
Original Data ◽  
Industrial Park ◽  
Multivariate Statistical

Multivariate statistical analysis explains the huge and complicated current situation of the original data efficiently, concisely, and explicitly. It simplifies the original data into representative factors, or bases on the similarity between data to cluster and identify clustering outcome. In this study, the statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate characteristics of groundwater quality at an industrial park site located in Kaohsiung, Taiwan. Results from the principal component analysis (PCA) and factor analyses (FA) show that seven principal components could be compiled from 20 groundwater quality indicators obtained from groundwater analyses, which included background factor, salt residua factor, hardness factor, ethylene chloride factor, alkalinity factor, organic pollutant factor, and chloroform factor. Among the seven principal components, the major influencing components were salinization factor and acid-base factor. Results show that the seven principal component factors were able to represent 89.6% of the total variability for 20 different groundwater quality indicators. Groundwater monitoring wells were classified into seven groups according to the partition of homogeneity and similarity using the two-phase cluster analysis (CA). The clustering results indicate that chlorides such as 1,1-dichloroethylene, 1,1-dichloroethane, and cis-1,2-dichloroethylene had the highest concentrations among the clusters. This indicates that groundwater at nearby areas may be polluted by chlorinated organic compounds. Results from the correlation analysis by Fisher coefficient formula show that the cluster results of seven groups of groundwater wells had 100 and 80% accuracies using discriminant and cross-validation analyses, respectively. This implies that high accuracy can be obtained when discriminant and cluster analyses are applied for data evaluation.

scholarly journals Principal component analysis: The path ahead for aircrew-aircraft compatibility at the Institute of Aerospace Medicine

2019 ◽  
Vol 63 ◽  
pp. 8-15
Author(s):  
P Biswal ◽  
YS Dahiya
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Principal Components ◽  
Computer Aided Design ◽  
Principal Component ◽  
Component Analysis ◽  
Critical Parameters ◽  
Design Stage ◽  
Aviation Industry ◽  
Aerospace Medicine

Introduction: The Institute of Aerospace Medicine provides design consultancy on aircraft-aircrew compatibility on a number of fixed-wing as well as rotary-wing aircrafts during various stages of development. Till date, the cockpit compatibility of aircrew has been determined based on the percentile concept. Percentiles, though useful when dealing with a single parameter, pose major design and fitment problems when considering multiple parameters simultaneously as in aircraft cockpit design. The concept of multivariate analysis has been the solution which the aviation industry the world over has accepted in overcoming this problem. This paper presents the Institute of Aerospace Medicine initial foray into the field of multivariate analysis, specifically principal component analysis (PCA) to achieve desired aircrew fitment in the aircraft cockpit right from the design stage. Materials and Methods: The fighter aircraft of the near future is being designed using the anthropometric parameters available in the IAF aircrew anthropometry survey 2013. Of the 57 parameters available, six parameters critical to the design of the cockpit were subject to PCA to derive three principal components. About 96% confidence ellipse was drawn on the plot of the principal components. From this, along the different axes, 21 boundary individuals were identified defining the extreme individuals in various combinations of the six parameters. Discussion: The use of more than 2 parameters is not amenable to sequential use of percentiles. As the number of parameters considered increases, it leads to reduced fitment percentage. The use of PCA allows consideration of critical parameters together at one go. The design aim is changed from the 3rd to 97th percentile to an overall aim of fitting 96% of the target population in the cockpit. The boundary individual’s entire anthropometry data are used to create boundary manikins for use in computer-aided design models. The fitment of these boundary individuals ensures that if these individuals fit, all others would fit in the cockpit. This concept brings about a paradigm shift in the aircrew-aircraft compatibility in the aviation industry in India.

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