scholarly journals Metodologias de identificação de padrões alimentares a posteriori em crianças brasileiras: revisão sistemática

2016 ◽  
Vol 21 (1) ◽  
pp. 143-154 ◽  
Author(s):  
Carolina Abreu de Carvalho ◽  
Poliana Cristina de Almeida Fonsêca ◽  
Luciana Neri Nobre ◽  
Silvia Eloiza Priore ◽  
Sylvia do Carmo Castro Franceschini
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
A Posteriori ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank

Resumo O objetivo deste estudo é fornecer orientações para identificação de padrões alimentares por abordagem a posteriori, bem como analisar os aspectos metodológicos dos estudos realizados no Brasil que os identificaram em crianças. Os artigos foram selecionados nas bases de dados da Literatura Latino-Americana e do Caribe em Ciências da Saúde, Scientific pattern; Principal component analysis; Factor analysis; Cluster analysis; Reduced rank regression. Incluíram-se pesquisas que identificaram padrões alimentares de crianças por meio da abordagem a posteriori. Selecionou-se 7 estudos, sendo 6 transversais e 1 de coorte, publicados entre 2007 e 2014. Cinco usaram como inquérito o questionário de frequência alimentar, um o recordatório de 24h e outro uma lista de alimentos. O método de abordagem exploratória mais utilizado nas publicações foi a análise fatorial por componentes principais, seguida da análise de agrupamento. O tamanho amostral dos estudos variou de 232 a 4231, os valores do teste Kaiser-Meyer-Olkin de 0,524 a 0,873, e o alfa de Cronbach de 0,51 a 0,69. Poucos estudos brasileiros identificaram padrões alimentares de crianças utilizando abordagem a posteriori, e a análise fatorial por componentes principais foi a técnica mais usada.

scholarly journals Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults

2015 ◽  
Vol 19 (2) ◽  
pp. 195-203 ◽  
Author(s):  
Carolina Batis ◽  
Michelle A Mendez ◽  
Penny Gordon-Larsen ◽  
Daniela Sotres-Alvarez ◽  
Linda Adair ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Dietary Patterns ◽  
Dietary Pattern ◽  
Principal Component ◽  
Component Analysis ◽  
Model Assessment ◽  
Reduced Rank Regression ◽  
Nutrition Survey ◽  
Rank Regression ◽  
Reduced Rank

AbstractObjectiveWe examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in glycated Hb (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose.DesignWe measured diet over a 3 d period with 24 h recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009.SettingAdults (n 4316) from the China Health and Nutrition Survey.ResultsThe adjusted odds ratio for diabetes prevalence (HbA1c≥6·5 %), comparing the highest dietary pattern score quartile with the lowest, was 1·26 (95 % CI 0·76, 2·08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0·76 (95 % CI 0·49, 1·17) for a traditional southern pattern (PCA; rice, meat, poultry and fish) and 2·37 (95 % CI 1·56, 3·60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviourally meaningful. It combined the deleterious effects of the modern high-wheat pattern (high intakes of wheat buns and breads, deep-fried wheat and soya milk) with the deleterious effects of consuming the opposite of the traditional southern pattern (low intakes of rice, poultry and game, fish and seafood).ConclusionsOur findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

scholarly journals Economic Causes of Extremism Intellectual (Thoughts) Views of Graduate Students Using Multivariate Statistical Techniques

2016 ◽  
Vol 9 (7) ◽  
pp. 160
Author(s):  
Hasan Abdullah Al-Dajah
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Graduate Students ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
The Impact

The present study investigated the impact of the economic reasons on the intellectual (thoughts) extremism, and the statement of the most important indicators in the economic factor that lead to extremism from the views of graduate students. The study problem based on the following question: What are economic factors leading to the extremism of the intellectual(Thoughts)? Correlation coefficient, Principal component analysis (PCA), varimax (F) rotated factor analysis, and dendrogram cluster analysis (DCA) were assessed for the economic impacts that leads to extremism(Thoughts). Multivariate statistical analysis of the dataset and correlation analysis suggested that the strong positive correlations are commonly associated in the poverty and lack of interest in remote areas for major cities Center. Multivariate statistical analysis such as principal component analysis, varimax rotated factor analysis, and dendrogram cluster analysis allowed the identification of three main factors controlling that lead to extremism from the views of graduate students. The extracted factors are as follows: low living expenses, poverty and substantial deprivation, and unequal opportunities and unemployment associations related to prevalence of corruption phase.

scholarly journals Ανάλυση περιβαλλοντικού κινδύνου από βαρέα μέταλλα γεωγενούς και ανθρωπογενούς προέλευσης σε δύο αντιπροσωπευτικές περιοχές του Ελλαδικού χώρου

2016 ◽  
Author(s):  
Δήμητρα Μεγαδούκα
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Η Factor

Σκοπός της παρούσας μελέτης είναι η έρευνα έξι βαρέων μετάλλων σε δύο περιοχές του Ελλαδικού χώρου. Οι κεντρικοί άξονες της έρευνας αυτής αποτελούνται από τους εξής στόχους: i) να προσδιοριστούν οι συγκεντρώσεις των μετάλλων Pb, Cd, Zn, Co, Ni και Cr που εντοπίζονται σε αδιατάρακτα εδάφη, ii) να προσδιοριστούν οι πηγές προέλευσης των μετάλλων (ανθρωπογενής – γεωγενής) με εργαστηριακές και στατιστικές μεθόδους, iii) να προσδιοριστούν οι γεωχημικές μορφές των μετάλλων που κατανέμονται στα εδάφη, iv) να παρουσιαστούν γεωγραφικοί χάρτες κατανομής των μετάλλων με την χρήση του προγράμματος Γεωγραφικών Πληροφοριακών Συστημάτων, v) να προσδιοριστούν οι υποβόσκουσες σχέσεις μεταξύ των μετάλλων vi) να γίνει εκτίμηση περιβαλλοντικού κινδύνου που σχετίζεται με την ρύπανση εδαφών από βαρέα μέταλλα σε περιοχές επιβαρυμένες από ανθρωπογενή και γεωγενή αίτια.Επιλέχθηκαν δύο περιοχές μελέτης. α) Η Βόρεια Εύβοια επιλέχθηκε ως περίπτωση επιβάρυνσης από βαρέα μέταλλα γεωγενούς προέλευσης λόγω μητρικού υλικού - των βασικών και υπερβασικών πετρωμάτων που χαρακτηρίζουν γεωλογικά την περιοχή. β) Η Λαυρεωτική χερσόνησος επιλέχθηκε ως περίπτωση επιβάρυνσης από βαρέα μέταλλα ανθρωπογενούς προέλευσης από τις παλαιότερες (π.Χ.) αλλά και πρόσφατες μεταλλευτικές και μεταλλουργικές δραστηριότητες.Στην περιοχή της Βόρειας Εύβοιας επιλέχθηκαν 21 θέσεις δειγματοληψίας και στην Λαυρεωτική χερσόνησο επιλέχθηκαν 45 θέσεις δειγματοληψίας. Από κάθε θέση δειγματοληψίας, συλλέχθηκαν δείγματα από δύο διαφορετικά βάθη, 0-10 cm (Α) και 10-30 cm (Β). Σε όλα τα εδαφικά δείγματα προσδιορίστηκαν οι εξής φυσικοχημικές ιδιότητες των εδαφών : η κοκκομετρική σύσταση, το pH, το Eh, η οργανική ουσία και το ισοδύναμο CaCO3. Επίσης προσδιοριστήκαν οι μορφές των μετάλλων με την μέθοδο διαδοχικών εκχυλίσεων τροποποιημένη BCR. Η μέθοδος των διαδοχικών εκχυλίσεων στοχεύει επιλεκτικά και απελευθερώνει τα μέταλλα που είναι συνδεδεμένα σε : α) υδατοδιαλυτές & συνδεδεμένες με ανθρακικά άλατα, β) οξείδια Fe και Mn, γ) οργανική ουσία και δ) πυριτικές μορφές. Επίσης, για να γίνει μία σωστή και αντικειμενική σύγκριση των αποτελεσμάτων και να προσδιοριστούν οι σχέσεις μεταξύ των μετάλλων χρησιμοποιήθηκε το πρόγραμμα Factor Analysis (ανάλυση παραγόντων) και το πρόγραμμα Hierarchical Cluster Analysis. Η Factor Analysis με την μέθοδο της Principal Component Analysis, προσδιορίζει έναν μικρό αριθμό παραγόντων που εξηγούν το μεγαλύτερο μέρος της διακύμανσης που παρατηρείται, σε έναν πολύ μεγαλύτερο όγκο δεδομένων, ενώ η Hierarchical Cluster Analysis προσδιορίζει τις μεταβλητές ή τους παράγοντες, οι οποίοι εξηγούν τον τρόπο που συσχετίζονται τα μέταλλα απεικονιζόμενες σε ένα δενδρόγραμμα.

Study on Nitrogen, Phosphor and Chemical Oxygen Demand of Differnt Categories of Aquaculture Lakes by Means of Principal Component Analysis, Factor Analysis and Cluster Analysis

2011 ◽  
Vol 340 ◽  
pp. 369-377 ◽  
Author(s):  
Luo Jun Gong ◽  
Xue Fen Yang ◽  
Bang Xi Xiong ◽  
Gui Ping Li ◽  
Xian Li Chen
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Oxygen Demand ◽  
Score Function ◽  
Principal Component ◽  
Component Analysis ◽  
Cluster Result ◽  
Function Expression ◽  
And Cluster Analysis

The author measured TN, NH4+-N, NO2--N, NO3--N, TP, PO43--P and CODMn of the five aquaculture lakes that is, Liangzi Lake, Futou Lake, Chaipo Lake, Nanhu Lake and Yezhi Lake, in Wuhan in 2007, and had analyzed the results by means of Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA). The PCA result showed that the two principal components were nutrient factor (including TN, NO2--N, NO3--N, TP, PO43--P and CODMn) and ammonium absorption factor (NH4+-N), with their function expression integrated. The results of FA and PCA were in conformity, with their factor score function expression integrated. R-mode cluster result indicated that the seven hydrochemical indexes could be divided into four categories, i.e. NO3 - -N, TP, TN and NO2--N make up of one category, and another three categories were PO43--P, NH4+-N and CODMn, respectively. Q-mode cluster result showed that the five lakes can be divided into two categories, Futou Lake and Liangzi Lake is one category, and Chaipo Lake, Yezhi Lake and Nanhu Lake is the other, which has been caused by urban waste water, domestic sewage and aquaculture production.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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