Academic literature on the topic 'Biplot analysis'

The extraction of essential features of any real-valued time series is crucial for exploring, modeling and producing, for example, forecasts. Taking advantage of the representation of a time series data by its trajectory matrix of Hankel constructed using Singular Spectrum Analysis, as well as of its decomposition through Principal Component Analysis via Partial Least Squares, we implement a graphical display employing the biplot methodology. A diversity of types of biplots can be constructed depending on the two matrices considered in the factorization of the trajectory matrix. In this work, we discuss the called HJ-biplot which yields a simultaneous representation of both rows and columns of the matrix with maximum quality. Interpretation of this type of biplot on Hankel related trajectory matrices is discussed from a real-world data set.

Biplot analysis has evolved into an important statistical tool in plant breeding and agricultural research. Here we review the basic principles of biplot analysis and recent developments in its application in analyzing multi-environment trail (MET) data, with the aim of providing a working guide for breeders, agronomists, and other agricultural scientists on biplot analysis and interpretation. The review is divided into four sections. The first section is a complete but succinct description of the principles of biplot analysis. The second section is a detailed treatment of biplot analysis of genotype by environment data. It addresses environment and genotype evaluation from all perspectives. The third section deals with biplot analysis of various two-way tables that can be generated from a three-way MET dataset, which is an integral and essential part to a fuller understanding and exploration of MET data. The final section discusses questions that are frequently asked about biplot analysis. Methods described in this review are available in a user-friendly, interactive software package called “GGEbiplot”. Key words: biplot analysis; genotype by environment interaction; mega-environment; multi-environment trials

SummaryThe presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET). Despite their different approaches, both models complement each other in order to strengthen decision making. However, both models are based on biplots, consequently, biplot-based interpretation doesn’t scale well beyond two-dimensional plots, which happens whenever the first two components don’t capture enough variation. This paper proposes an approach to such cases based on cluster analysis combined with the concept of medoids. It also applies AMMI and GGE Biplot to the adjusted data in order to compare both models. The data is provided by the International Maize and Wheat Improvement Center (CIMMYT) and comes from the 14th Semi-Arid Wheat Yield Trial (SAWYT), an experiment concerning 50 genotypes of spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall. It was performed in 36 environments across 14 countries. The analysis provided 25 genotypes clusters and 6 environments clusters. Both models were equivalent for the data’s evaluation, permitting increased reliability in the selection of superior cultivars and test environments.

Effective breeding for disease resistance relies on a thorough understanding of host-by-pathogen relations. Achieving such understanding can be difficult and challenging, particularly for large data sets with complex host genotype-by-pathogen strain interactions. This paper presents a biplot approach that facilitates visual analysis of host-by-pathogen data. A biplot displays both host genotypes and pathogen isolates in a single scatter plot; each genotype or isolate is displayed as a point defined by its scores on the first two principal components derived from subjecting genotype- or strain-centered data to singular value decomposition. From a biplot, clusters of host genotypes and clusters of pathogen strains can be simultaneously visualized. Moreover, the basis for genotype and strain classifications, i.e., interactions between individual genotypes and strains, can be visualized at the same time. A biplot based on genotype-centered data and that based on strain-centered data are appropriate for visual evaluation of susceptibility/resistance of genotypes and virulence/avirulence of strains, respectively. Biplot analysis of genotype-by-strain is illustrated with published response scores of 13 barley line groups to 8 net blotch isolate groups.

One of functions of multivariate analysis is to group data. Multivariate analysis often used in grouping data are cluster analysis and biplot analysis. In this paper, a comparative analysis will be made between clusters analysis and biplot analysis for grouping the data. Technique used in the cluster analysis is k-mean method and biplot analysis used two-dimensional display. The results ware that biplot analysis produces are better in grouping accuracy than clusters analysis. But in general, biplot analysis can not be said to be better than clusters analysis in grouping the data and vice versa.

Genetic markers, from morphological to molecular, in function with early Heterosis is a prerequisite for the successful commercial maize production. It does not appear in any cross of two inbred lines, and therefore, the determination of combining abilities of parental lines is essential. The most commonly used method for determining combining abilities is diallel analysis. Besides conventional methods for diallel analysis, a new biplot approach has been sugested. In this paper, we studied the combining ability for grain yield in a set of genotypes obtained by diallel crossing system of six inbred lines. Both, the Griffing?s conventional method and the biplot approach have been used for diallel analysis. Comparing the GCA values from biplot analysis and Griffing?s method, similar results can be observed, with the exception of NS L 1051 and NS L 1000 whose ranks are interchanged. Biplot analysis enables the SCA estimation of parent inbred, and the highest SCA has inbred B73D. Biplot analysis also allows the estimation of the best crosses. Inbred B73D shows the best results when crossed with testers Mo17Ht, NS L 1051 and N152, inbred N152 combines best with testers NS L 1001 and NS L 1000, whereas the cross of inbred NS L 1051 with tester B73D results with the highest grain yield per plant in comparison with other testers.

Cowpea is an African legume that was introduced to Brazil by Portuguese settlers in the mid-16th century. The productive potential of this crop may fluctuate depending on its environment. The objective of the present study was to select cowpea lines with high grain yield coupled with other traits of agronomic interest, such as good adaptability and stability, by the GYT biplot methodology. Twelve lines were evaluated in the years 2016 and 2017 in the municipality of Bom Jesus de Itabapoana, Brazil, in a randomized-block design with four replicates and two cultivars, which were used as controls. The following variables were evaluated: number of days to flowering, final stand, crop value, lodging, pod weight, pod length, seed number per pod, seed weight per pod, 100-seed weight (100SW), and grain yield. Analysis of variance was performed and GYT biplots were constructed using R software and the ggplot2 package. The GYT biplot graph analysis allowed for the selection of superior cowpea genotypes. In the combinations of traits observed, lines L1, L3, L5, L6, L8, and L9 were superior and cultivar Imponente stood out as one of the controls. The yield combinations GY*CV, GY*NDF, GY*LDG, GY*CV, GY*PW, GY*SNP and GY*P100G were positively correlated with each other but showed negative to highly negative correlations with GY*SWP and GY*TS.

One of the challenges facing statisticians is to provide tools to enable researchers to interpret and present their data and conclusions in ways easily understood by the scientific community. One of the tools available for this purpose is a multivariate graphical representation called reduced rank regression biplot. This biplot describes how to construct a graphical representation in nonsymmetric contexts such as approximations by least squares in multivariate linear regression models of reduced rank. However multicollinearity invalidates the interpretation of a regression coefficient as the conditional effect of a regressor, given the values of the other regressors, and hence makes biplots of regression coefficients useless. So it was, in the search to overcome this problem, Alvarez and Griffin presented a procedure for coefficient estimation in a multivariate regression model of reduced rank in the presence of multicollinearity based on PLS (Partial Least Squares) and generalized singular value decomposition. Based on these same procedures, a biplot construction is now presented for a multivariate regression model of reduced rank in the presence of multicollinearity. This procedure, called PLSSVD GH biplot, provides a useful data analysis tool which allows the visual appraisal of the structure of the dependent and independent variables. This paper defines the procedure and shows several of its properties. It also provides an implementation of the routines in R and presents a real life application involving data from the FAO food database to illustrate the procedure and its properties.

To determine the effects of environment and genotypic differences on tuber yield and other related traits, 12 genotypes comprising 9 improved elite clones, two local landraces and 1 improved and released variety were evaluated for tuber yield, response to yam mosaic virus and leaf spot diseases at 16 growing environments. The multi-environment trials were conducted using randomized complete-block design with three blocks for four years in four representative agro-ecological zones (Atebubu, Kintampo, Ejura and Fumesua) in Ghana. The objective was to select high and stable yielding varieties for release as varieties in Ghana. The multi-environment data for the trials collected were subjected to combine analyses of variance using the ANOVA procedure of Statistical Tool for Agricultural Research (STAR) to determine the magnitude of the main effects and interactions. Genotype main effect and genotype by environment interaction effect (GGE) model was used to dissect the genotype by environment interaction (GEI) using the GGE biplot software (GGE biplot, 2007). GGE biplots analysis was applied for visual examination of the GEI pattern in the data set. A highly significant effects (P < 0.001) for Genotype (G), environment (E) and genotype by environment (GEI) interaction were occurred in the data set for highly significant for all the traits studied (P < 0.001), indicating genetic variability between genotypes by changing environments. This indicated changes in ranking order of the genotype performances across the test environments. The partitioning of the GGE effect for tuber yield through in GGE biplot analysis model showed that PC1 and PC2 accounted for 40.47.0% and 19.89.0% of the variation GGE sum of squares respectively for tuber yield, respectively explaining a total of 60.36% variation. Mankrong Pona was the most stable and high yielding (closest to the ideal genotype) followed by TDr95/19177. Genotypes TDr00/02472, TDr00/00539 and TDr98/00933 are desirable genotypes for further assessment on culinary characteristics and end-user assessment for release as varieties. All the four locations used for the study were highly relevant for research and development of yams. Ejura and Fumesua were the most discriminating and most representative for YMV respectively. In terms of yield, Kintampo environment was the most discriminating and Fumesua and Atebubu were the closest to ideal environment for evaluating yield.

AA análise multivariada e um conjunto de técnicas que são adequadas para situações onde varias variáveis correlacionadas estão envolvidas. Dentre essas técnicas temos as componentes principais e a analise fatorial. A técnica dos componentes principais reduz a dimensão de uma matriz de dados originais através de combinações lineares facilitando a interpretação desses dados e a analise fatorial que e o nome dado a uma classe de métodos estatísticos paramétricos (e não paramétricos) multivariados que correspondem a um grande numero de métodos e técnicas que utilizam simultaneamente todas as variáveis do conjunto na interpretação do inter-relacionamento das variáveis observadas (COSTA, 2006). O objetivo da analise fatorial e descrever as relações de covariância entre algumas variáveis em algum termo subjacente, mas não observável, de quantidades aleatórias chamadas fatores (JOHNSON e WICHERN, 1998). Biplot e um gráfico estático, desenvolvido por Gabriel (1971), que representa no mesmo gráfico as variáveis o as observações com o intuito de demonstrar graficamente as relações existentes entre variáveis, entre observações e entre variáveis e observações. O presente trabalho tem como objetivo inserir a metodologia de analise biplot tridimensional na técnica de analise fatorial multivariada. Foi usado o software SAS para a realização da analise fatorial e a construção do gráfico biplot e um conjunto de dados para a aplicação do mesmo. O estudo mostra que o gráfico Biplot e um método de analise multivariada de suma importância quando inserido na analise fatorial facilitando e complementando a interpretação dos resultados<br>Multivariate analysis is a set of techniques that are appropriate for situations where several correlated variables are involved. Among these techniques have the principal components and factor analysis. The technique of principal components reduces the size of an array of original data through linear combinations facilitating the interpretation of these data and factor analysis that is the name given to a class of parametric statistical methods (non parametric) multivariate corresponding to a large number of methods and techniques that use simultaneously all the variables set in the interpretation of the interrelationship of the observed variables (COSTA, 2006). The goal of factor analysis is to describe the covariance relationships between variables in any term underlying, but unobservable, random quantities called factors (Johnson and Wichern, 1998). Biplot is a statistical graph, developed by Gabriel (1971), which represents the in same graph the variables to the observations in order to demonstrate graphically the relationship between variables, between observations and between variables and observations. This paper aims to insert the biplot analysis methodology in three-dimensional technique of multivariate factor analysis. Was used SAS software to perform the factor analysis and construction of the biplot graph and a set of data for the application. The study shows that the graph Biplot is a method of analysis of the utmost importance when inserted in the factor analysis, facilitating and complementing the interpretation of results.

Thesis (MComm)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: The main topics of study in this thesis are the Principal Component Analysis (PCA) and Canonical Variate Analysis (CVA) biplots, with the primary focus falling on the quality measures associated with these biplots. A detailed study of different routes along which PCA and CVA can be derived precedes the study of the PCA biplot and CVA biplot respectively. Different perspectives on PCA and CVA highlight different aspects of the theory that underlie PCA and CVA biplots respectively and so contribute to a more solid understanding of these biplots and their interpretation. PCA is studied via the routes followed by Pearson (1901) and Hotelling (1933). CVA is studied from the perspectives of Linear Discriminant Analysis, Canonical Correlation Analysis as well as a two-step approach introduced in Gower et al. (2011). The close relationship between CVA and Multivariate Analysis of Variance (MANOVA) also receives some attention. An explanation of the construction of the PCA biplot is provided subsequent to the study of PCA. Thereafter follows an in depth investigation of quality measures of the PCA biplot as well as the relationships between these quality measures. Specific attention is given to the effect of standardisation on the PCA biplot and its quality measures. Following the study of CVA is an explanation of the construction of the weighted CVA biplot as well as two different unweighted CVA biplots based on the two-step approach to CVA. Specific attention is given to the effect of accounting for group sizes in the construction of the CVA biplot on the representation of the group structure underlying a data set. It was found that larger groups tend to be better separated from other groups in the weighted CVA biplot than in the corresponding unweighted CVA biplots. Similarly it was found that smaller groups tend to be separated to a greater extent from other groups in the unweighted CVA biplots than in the corresponding weighted CVA biplot. A detailed investigation of previously defined quality measures of the CVA biplot follows the study of the CVA biplot. It was found that the accuracy with which the group centroids of larger groups are approximated in the weighted CVA biplot is usually higher than that in the corresponding unweighted CVA biplots. Three new quality measures that assess that accuracy of the Pythagorean distances in the CVA biplot are also defined. These quality measures assess the accuracy of the Pythagorean distances between the group centroids, the Pythagorean distances between the individual samples and the Pythagorean distances between the individual samples and group centroids in the CVA biplot respectively.<br>AFRIKAANSE OPSOMMING: Die hoofonderwerpe van studie in hierdie tesis is die Hoofkomponent Analise (HKA) bistipping asook die Kanoniese Veranderlike Analise (KVA) bistipping met die primêre fokus op die kwaliteitsmaatstawwe wat daarmee geassosieer word. ’n Gedetailleerde studie van verskillende roetes waarlangs HKA en KVA afgelei kan word, gaan die studie van die HKA en KVA bistippings respektiewelik vooraf. Verskillende perspektiewe op HKA en KVA belig verskillende aspekte van die teorie wat onderliggend is tot die HKA en KVA bistippings respektiewelik en dra sodoende by tot ’n meer breedvoerige begrip van hierdie bistippings en hulle interpretasies. HKA word bestudeer volgens die roetes wat gevolg is deur Pearson (1901) en Hotelling (1933). KVA word bestudeer vanuit die perspektiewe van Linieêre Diskriminantanalise, Kanoniese Korrelasie-analise sowel as ’n twee-stap-benadering soos voorgestel in Gower et al. (2011). Die noue verwantskap tussen KVA en Meerveranderlike Analise van Variansie (MANOVA) kry ook aandag. ’n Verduideliking van die konstruksie van die HKA bistipping word voorsien na afloop van die studie van HKA. Daarna volg ’n indiepte-ondersoek van die HKA bistipping kwaliteitsmaatstawwe sowel as die onderlinge verhoudings tussen hierdie kwaliteitsmaatstawe. Spesifieke aandag word gegee aan die effek van die standaardisasie op die HKA bistipping en sy kwaliteitsmaatstawe. Opvolgend op die studie van KVA is ’n verduideliking van die konstruksie van die geweegde KVA bistipping sowel as twee veskillende ongeweegde KVA bistippings gebaseer op die twee-stap-benadering tot KVA. Spesifieke aandag word gegee aan die effek wat die inagneming van die groepsgroottes in die konstruksie van die KVA bistipping op die voorstelling van die groepstruktuur onderliggend aan ’n datastel het. Daar is gevind dat groter groepe beter geskei is van ander groepe in die geweegde KVA bistipping as in die oorstemmende ongeweegde KVA bistipping. Soortgelyk daaraan is gevind dat kleiner groepe tot ’n groter mate geskei is van ander groepe in die ongeweegde KVA bistipping as in die oorstemmende geweegde KVA bistipping. ’n Gedetailleerde ondersoek van voorheen gedefinieerde kwaliteitsmaatstawe van die KVA bistipping volg op die studie van die KVA bistipping. Daar is gevind dat die akkuraatheid waarmee die groepsgemiddeldes van groter groepe benader word in die geweegde KVA bistipping, gewoonlik hoër is as in die ooreenstemmende ongeweegde KVA bistippings. Drie nuwe kwaliteitsmaatstawe wat die akkuraatheid van die Pythagoras-afstande in die KVA bistipping meet, word gedefinieer. Hierdie kwaliteitsmaatstawe beskryf onderskeidelik die akkuraatheid van die voorstelling van die Pythagoras-afstande tussen die groepsgemiddeldes, die Pythagoras-afstande tussen die individuele observasies en die Pythagoras-afstande tussen die individuele observasies en groepsgemiddeldes in die KVA bistipping.

O objetivo deste trabalho foi avaliar os métodos estatísticos de análise da interação de genótipos com ambientes (G × A), enfatizando a adaptabilidade e a estabilidade fenotípica. As variáveis estudadas foram produção e teor de sólidos solúveis totais (SST) do melão do tipo Gália, testando 9 genótipos em 12 ambientes. O experimento foi conduzido no delineamento aleatorizado em blocos com 3 repetições, realizado no Pólo Agroindustrial Mossoró-Assu no Rio Grande do Norte. O desempenho dos cultivares foi analisado por meio da utilização de análises de variância, metodologias de adaptabilidade e estabilidade. Realizou-se as análises para a produção e o teor de sólidos solúveis, utilizando as metodologias AMMI (Additive Main Effects and Multiplicative Interaction) e SREG (Sites Regression), representando graficamente de forma simultânea os genótipos e ambientes através dos gráficos Biplot AMMI, GGEbiplot e Trilinear plot. A análise AMMI possui a vantagem de estudar detalhadamente a estrutura do efeito de interação, além de representar simultaneamente os escores dos efeitos da interação para cada fator. Na análise SREG, incorpora o efeito de genótipo e na maioria dos casos está altamente correlacionado com os escores do primeiro componente principal, possui a vantagem de permitir a avaliação gráfica direta do efeito de genótipo. Propõe-se, também a metodologia MDS (Multidimensional Scalling) para verificar as similaridades e dissimilaridades entre os ambientes, através de uma matriz de distancias, representando geometricamente os dados no espaço bidimensional (Biplot) para cada variável estudada, em que pode-se observar as disparidades entre os ambientes, mostrando que esses apresentam características diferentes<br>The objective of this study was to evaluate statistical methods of analysis of the interaction of genotypes with environments (G × A), emphasizing the adaptability and stability phenotype. The variables studied were production and soluble solids contents (SST) Melon Galia type, testing 9 genotypes in 12 environments. The experiment was conducted in a randomized block with 3 replications, it was done at Pole Agroindustrial Mossor´o-Assu in Rio Grande do Norte. The performance of cultivars was analyzed by using analysis of variance, methods of adaptability and stability. It carried out the analysis for the production and soluble solids, using the methodologies AMMI (Additive Main Effects and Multiplicative Interaction) and SREG (Sites Regression), graphing simultaneously the genotypes and environments through the AMMI Biplot graphs, GGE Biplot and trilinear plot. The AMMI analysis has the advantage of studying in detail the structure of the interaction effect, and represents both the scores of the interaction effects for each factor. The analysis SREG, incorporates the effect of genotype and in most cases is highly correlated with the scores of the first principal component, it has the advantage of allowing direct graphical assessment of the effect of genotype. It was also proposed the methodology MDS (Multidimensional Scalling) to check the similarities and dissimilarities between the environments, through a distance matrix, representing geometrically the data in two-dimensional space (Biplot) each variable studied, in wich one can be observed disparities environmental show different characteristics.

Dissertation (PhD)--Stellenbosch University, 2001.<br>ENGLISH ABSTRACT: Gower and Hand offer a new perspective on the traditional biplot. This perspective provides a unified approach to principal component analysis (PCA) biplots based on Pythagorean distance; canonical variate analysis (CVA) biplots based on Mahalanobis distance; non-linear biplots based on Euclidean embeddable distances as well as generalised biplots for use with both continuous and categorical variables. The biplot methodology of Gower and Hand is extended and applied in statistical discrimination and classification. This leads to discriminant analysis by means of PCA biplots, CVA biplots, non-linear biplots as well as generalised biplots. Properties of these techniques are derived in detail. Classification regions defined for linear discriminant analysis (LDA) are applied in the CVA biplot leading to discriminant analysis using biplot methodology. Situations where the assumptions of LDA are not met are considered and various existing alternative discriminant analysis procedures are formulated in terms of biplots and apart from PCA biplots, QDA, FDA and DSM biplots are defined, constructed and their usage illustrated. It is demonstrated that biplot methodology naturally provides for managing categorical and continuous variables simultaneously. It is shown through a simulation study that the techniques based on biplot methodology can be applied successfully to the reversal problem with categorical variables in discriminant analysis. Situations occurring in practice where existing discriminant analysis procedures based on distances from means fail are considered. After discussing self-consistency and principal curves (a form of non-parametric principal components), discriminant analysis based on distances from principal curves (a form of a conditional mean) are proposed. This biplot classification procedure based upon principal curves, yields much better results. Bootstrapping is considered as a means of describing variability in biplots. Variability in samples as well as of axes in biplot displays receives attention. Bootstrap a-regions are defined and the ability of these regions to describe biplot variability and to detect outliers is demonstrated. Robust PCA and CVA biplots restricting the role of influential observations on biplot displays are also considered. An extensive library of S-PLUS computer programmes is provided for implementing the various discriminant analysis techniques that were developed using biplot methodology. The application of the above theoretical developments and computer software is illustrated by analysing real-life data sets. Biplots are used to investigate the degree of capital intensity of companies and to serve as an aid in risk management of a financial institution. A particular application of the PCA biplot is the TQI biplot used in industry to determine the degree to which manufactured items comply with multidimensional specifications. A further interesting application is to determine whether an Old-Cape furniture item is manufactured of stinkwood or embuia. A data set provided by the Western Cape Nature Conservation Board consisting of measurements of tortoises from the species Homopus areolatus is analysed by means of biplot methodology to determine if morphological differences exist among tortoises from different geographical regions. Allometric considerations need to be taken into account and the resulting small sample sizes in some subgroups severely limit the use of conventional statistical procedures. Biplot methodology is also applied to classification in a diabetes data set illustrating the combined advantage of using classification with principal curves in a robust biplot or biplot classification where covariance matrices are unequal. A discriminant analysis problem where foraging behaviour of deer might eventually result in a change in the dominant plant species is used to illustrate biplot classification of data sets containing both continuous and categorical variables. As an example of the use of biplots with large data sets a data set consisting of 16828 lemons is analysed using biplot methodology to investigate differences in fruit from various areas of production, cultivars and rootstocks. The proposed a-bags also provide a measure of quantifying the graphical overlap among classes. This method is successfully applied in a multidimensional socio-economical data set to quantify the degree of overlap among different race groups. The application of the proposed biplot methodology in practice has an important byproduct: It provides the impetus for many a new idea, e.g. applying a peA biplot in industry led to the development of quality regions; a-bags were constructed to represent thousands of observations in the lemons data set, in tum leading to means for quantifying the degree of overlap. This illustrates the enormous flexibility of biplots - biplot methodology provides an infrastructure for many novelties when applied in practice.<br>AFRIKAANSE OPSOMMING: Gower en Hand bied 'n nuwe perspektief op die tradisionele bistipping. Hierdie perspektief verskaf 'n uniforme benadering tot hoofkomponent analise (HKA) bistippings gebaseer op Pythagoras-afstand; kanoniese veranderlike analise (KVA) bistippings gebaseer op Mahalanobis-afstand; nie-lineere bistippings gebaseer op Euclidies inbedbare afstande sowel as veralgemeende bistippings vir gebruik wanneer beide kontinue en kategoriese veranderlikes voorkom. Die bistippingsmetodologie van Gower en Hand word uitgebrei en toegepas in statistiese diskriminasie en klassifikasie. Dit lei tot diskriminantanalise met behulp van HKA bistippings, KVA bistippings, nie-lineere bistippings sowel as veralgemeende bistippings. Die eienskappe van hierdie tegnieke word in besonderhede afgelei. Die toepassing van die konsep van 'n klassifikasiegebied in die KVA bistipping baan die weg vir lineere diskriminantanalise (LDA) met behulp van bistippingsmetodologie. Situasies waar daar nie aan die aannames van LDA voldoen word nie kry aandag en verskeie bestaande altematiewe diskriminantanalise prosedures word in terme van bistippings geformuleer en naas HKA bistippings, word QDA, FDA en DSM bistippings gedefinieer, gekonstrueer en hul gebruike gedemonstreer. Dit word aangetoon dat bistippingsmetodologie op 'n natuurlik wyse voorsiening maak om kategoriese veranderlikes en kontinue veranderlikes gelyktydig te hanteer. Daar word met behulp van 'n simulasie-studie aangetoon dat tegnieke gebaseer op die bistippingsmetodologie wat ontwikkel IS, suksesvol by die sogenaamde ornkeringsprobleem by diskriminantanalise met kategoriese veranderlikes gebruik kan word. Verder word aangevoer dat daar baie praktiese situasies voorkom waar bestaande prosedures van diskriminantanalise faal omdat dit op afstande vanaf gemiddeldes gebaseer IS. Na 'n bespreking van self-konsekwentheid en hoofkrommes ('n vorm van nieparametriese hoofkomponente) word voorgestel om diskriminantanalise op afstand vanaf hoofkrommes ('n vonn van 'n voorwaardelike gemiddelde) te baseer. Sodoende is 'n bistippingklassifikasie prosedure wat op afstand vanaf hoofkrommes gebaseer is en wat baie beter resultate lewer, ontwikkel. Die variasie in die posisies van datapunte in die bistipping sowel as van die bistippingsasse word bestudeer met behulp van skoenlusmetodes. 'n Skoenlus a-gebied word gedefinieer en dit word gedemonstreer hoe so 'n a-gebied aangewend kan word om variasie in bistippings te beskryf en wegleers te identifiseer. Robuuste HKA en KV A bistippings wat die rol van invloedryke waamemings op die bistipping beperk, word bespreek. 'n Omvangryke biblioteek van S-PLUS rekenaarprogramme is geskryf VIr die implementering van die verskillende diskriminantanalise tegnieke wat met behulp van bistippingsmetodologie ontwikkel is. Die toepassing van die voorafgaande teoretiese ontwikkelinge en rekenaarprogramme word geillustreer aan die hand van werklike datastelle vanuit die praktyk. So word bistippings gebruik om die mate van kapitaalintensiteit van ondememings te ondersoek en om as hulpmiddel by risikobestuur van 'n finansiele instelling te dien. 'n Besondere toepassing van die HKA bistipping is die TQI bistipping wat in die industriele omgewing gebruik word ten einde te bepaal tot watter mate vervaardigde artikels aan neergelegde meerdimensionele spesifikasies voldoen. 'n Verdere interessante toepassing is om te bepaal of 'n Ou-Kaapse meubelstuk van stinkhout of embuia gemaak is. 'n Datastel verskaf deur Wes-Kaap Natuurbewaring in verband met die bekende padloper skilpad, Homopus areolatus, is met behulp van bistippings geanaliseer om te bepaal of daar morfometriese verskille tussen die padlopers afkomstig van bepaalde geografiese gebiede is. Allometriese beginsels moes ook in ag gene em word en die min waamemings in sommige van die subgroepe het tot gevolg dat konvensionele statistiese tegnieke nie sonder meer gebruik kan word nie. Die bistippingsmetodologie is ook toegepas op klassifikasie by 'n diabetes datastel om die gekombineerde gebruik van. hoofkrommes in 'n robuuste bistipping te illustreer en bistippingklassifikasie waar daar sprake van ongelyke kovariansiematrikse is. 'n Diskriminantanalise probleem waar die weidingsvoorkeure van wildsbokke 'n verandering in die dominante plantegroei tot gevolg kan he, word gebruik om bistippingklassifikasie met data waar kontinue sowel as kategoriese veranderlikes verskaf word, te illustreer. As voorbeeld van die gebruik van bistippings by 'n groot datastel is 'n datastel bestaande uit waamemings van 16828 suurlemoene met behulp van bistippingsmetodologie geanaliseer ten einde verskille in vrugte afkomstig van verskillende produsente-streke, kultivars en onderstamme te ondersoek. Die a-sakkies wat hier ontwikkel is, lei tot kwantifisering van die grafiese oorvleueling van groepe. Hierdie beginsel word suksesvol toegepas in 'n meerdimensionele sosio-ekonomiese datastel om die mate van oorvleueling van verskillende bevolkingsgroepe te kwantifiseer. Die toepassing van die voorgestelde bistippingsmetodologie in die praktyk lei tot 'n belangrike newe-produk: Dit verskaf die stimulus tot die ontstaan van nuwe idees, byvoorbeeld, die toepassing van 'n HKA bistipping in 'n industriele omgewing het tot die ontwikkeling van die konsep van 'n kwaliteitsgebied aanleiding gegee; a-sakkies is gekonstrueer om duisende waamemings in die suurlemoendatastel te verteenwoordig wat weer gelei het tot 'n metode om die graad van oorvleueling te kwantifiseer. Hierdeur is die geweldige veelsydigheid van bistippings geillustreer - bistippingsmetodologie verskaf die infrastruktuur vir baie vindingryke toepassings in die praktyk.

Interação genótipo × ambiente (GEI) é uma questão extremamente importante no melhoramento genético de plantas e produção. A seleção e recomendação de genótipos superiores são dificultadas devido à ocorrência de GEI e representa um grande desafio para os pesquisadores. Nesse contexto, as análises biplot têm sido cada vez mais utilizadas na análise de dados agronômicos, em que os dados são representados por uma tabela de dupla entradas de médias de GEI. Entretanto, as particularidades existentes no gráfico biplot dificultam sua interpretação, podendo induzir o pesquisador a erros. Existem vários modelos na literatura para análise de DGE (dados de GEI), entre eles, os mais utilizados são os modelos AMMI (Additive Main effects and Multiplicative Interaction) e GGE biplot (Genotype main effects + Genotype environment interaction). O modelo AMMI é um método estatístico para compreender a estrutura de interações entre genótipos e ambientes, que combina a análise de variância e a análise de componentes principais, para ajustar, respectivamente, os efeitos principais (G e E) e os efeitos da GEI. O GGE Biplot agrupa o efeito aditivo de genótipo com o efeito multiplicativo da GEI, e submete estes à análise de componentes principais. Existem dois problemas na utilização destes modelos: i) só pode ser utilizado para analisar dados MET (multi-ambientes), que tenha uma única característica e ii) cujos ambientes são heterogêneos. O presente trabalho tem como objetivos propor novos modelos W-GGE biplot (Weighted Genotype main effects + Genotype environment interaction) e AMMI-ponderado para análise de dados multi-ambientes, além de fazer uma comparação entre os modelos existentes como AMMI e GGE biplot; análise de mega-ambiente; avaliação de genótipos, ambiente de teste dentro de cada mega-ambiente e compreender as causas da GEI.<br>Genotype × environment interaction (GEI) is an extremely important issue in plant breeding and production. The selection and recommendation of superior genotypes are hampered due to the occurrence of GEI and represents a major challenge for researchers. In this context, biplot analyzes have been increasingly used in analyzing agronomic data, in which data are represented by a table of two entries of means of GEI. However, the particularities in the biplot graphic hamper its interpretation, and could lead the researcher to errors. There are several models in the literature for DGE analysis (GEI data), among them, the most used are the AMMI model (Additive Main effects and Multiplicative Interaction Models) and GGE biplot (Genotype main effects + Genotype environment interaction). The AMMI model is a statistical method to understand the structure of interactions between genotypes and environments, combining the analysis of variance and principal component analysis, to adjust, respectively, the main effects (G and E) and the effects of GEI. The GGE Biplot groups genotype of additive effect with multiplicative effect of GEI, and submit these to the principal component analysis. There are two problems in using these models: i) can only be used to analyze MET data (multi-environments), which has a unique feature and ii) whose environments are heterogeneous. This paper aims to propose new W-GGE biplot models (Weighted Genotype main efffects + Genotype environment interaction) and AMMI-weighted multi-environments for data analysis, and make a comparison between the existing models as AMMI and GGE biplot; mega-environment analysis; genotype evaluation, test environment within each mega-environment and understand the causes of GEI.

O objetivo deste trabalho é propor uma nova metodologia de interpretação da estabilidade dos métodos de agrupamento, para dados de vegetação, utilizando a metodologia AMMI e a reamostragem (bootstrap), para ganhar confiabilidade nos agrupamentos formados. Os dados utilizados são provenientes do departamento de genética da Escola Superior de Agricultura \"Luiz de Queiroz\", e visam à produtividade de soja. Primeiramente aplica-se a metodologia AMMI e então, é estimada a matriz de distâncias euclidianas - com base nos dados originais e obtidos via reamostragem (bootstrap) - para a aplicação dos métodos de agrupamento (vizinho mais próximo, vizinho mais distante, ligação média, centroide, mediana e Ward). Para a verificação da validade dos agrupamentos formados utiliza-se o coeficiente de correlação cofenética, e pelo teste de Mantel, é apresentada a distribuição empírica dos coeficientes de correlação cofenética. Os agrupamentos obtidos pelos diferentes métodos são, em sua maioria, semelhantes indicando que, em princípio, qualquer um desses métodos seria adequado para a representação. O método que apresenta resultados discrepantes em relação aos outros (tanto para os dados originais, quanto pelos dados obtidos via bootstrap) - na representação gráfica em dendrograma - é método de Ward. Este estudo é promissor na análise da validade de agrupamentos formados em dados de vegetação.<br>The objective of this work is to propose a new interpretation methodology of clustering methods for vegetation data stability, using the AMMI and bootstrap methodology, to gain reliability in the clusters formed. The database used is from the Departament of Genetics of Luiz de Queiroz College of Agriculture, aiming soybean yield. Firstly AMMI is applied, then the Euclidian distance matrix is estimated - based on the original data and on the acquired by the bootstrap method - for the application of clustering methods (nearest neighbor, furthest neighbor, average linkage, centroid , median and Ward). In order to assess the validity of clusters formed the cophenetic correlation coefficient is used, and the Mantel test, in order to show the empirical distribution of the cophenetic correlation coefficients. The clusters obtained by different methods are, in most cases, quite similar, indicating that in principle, any of these methods would be suitable for the representation. The method that presents discrepant results (for both the original and bootstrap method obtained data) - on the dendrogram graphical representation, compared to the others - is the Ward\'s. This study is promising in the analysis of validity of clusters formed in vegetation data.

Submitted by Rodolfo Buzinaro null (rodolfobuzinaro@hotmail.com) on 2017-12-19T18:23:38Z No. of bitstreams: 1 Tese_Rodolfo_Buzinaro.pdf: 2463211 bytes, checksum: 329da6c1e9d431004c67fc291ea085f7 (MD5)<br>Approved for entry into archive by Alexandra Maria Donadon Lusser Segali null (alexmar@fcav.unesp.br) on 2017-12-20T09:37:13Z (GMT) No. of bitstreams: 1 buzinaro_r_dr_jabo.pdf: 2463211 bytes, checksum: 329da6c1e9d431004c67fc291ea085f7 (MD5)<br>Made available in DSpace on 2017-12-20T09:37:13Z (GMT). No. of bitstreams: 1 buzinaro_r_dr_jabo.pdf: 2463211 bytes, checksum: 329da6c1e9d431004c67fc291ea085f7 (MD5) Previous issue date: 2017-11-10<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>O nitrogênio é um dos elementos mais exigidos pela cultura do milho, pois está diretamente ligado à capacidade de produção agrícola desta espécie. Grande parte dos solos brasileiros agricultáveis possuem deficiência de nitrogênio ou este nutriente não está disponível em quantidades suficientes para suprir a necessidade da cultura, sendo necessário realizar aplicações suplementares de nitrogênio para se alcançar elevadas produtividades. As cultivares atuais de milho são altamente dependentes de adubação nitrogenada, pois foram desenvolvidas para ambientes otimizados, não sendo adaptadas para as condições de cultivo sob baixo nitrogênio. O uso de bactérias diazotróficas, as quais possuem a capacidade de fixar nitrogênio e disponibilizá-lo para as plantas, bem como a associação das plantas com essas bactérias, pode se tornar uma alternativa sustentável e economicamente viável para a produção de milho em sistemas agrícolas sob baixa utilização de nitrogênio. Com base no exposto, os objetivos deste trabalho foram identificar e caracterizar genótipos de milho eficientes no uso do nitrogênio e Azospirillum brasilense em ambientes contrastantes quanto a disponibilidade de nitrogênio no solo e selecionar parentais superiores de milho para eficiência no uso de Azospirillum brasilense. Foram utilizados como material genético 48 genótipos de milho obtidos em esquema de dialelo parcial, utilizando-se oito linhagens parentais pertencentes ao grupo heterótico I, e seis linhagens parentais pertencentes ao grupo heterótico II, todas provenientes do Departamento de Genética da ESALQ/USP. Os experimentos foram conduzidos em três safras (primeira safra 2013/2014 e 2014/2015 e segunda safra 2014) no delineamento de blocos casualizados com duas repetições e três testemunhas. Em cada safra foram realizados três experimentos, os quais eram diferenciados pela aplicação com A. brasilense, aplicação de nitrogênio em cobertura e sem nenhuma aplicação, respectivamente. Os caracteres avaliados foram altura de planta, altura de espiga, florescimentos masculino e feminino, tombamento, produtividade de grãos, peso médio de grãos, número de grãos por fileira, número de fileiras de grãos e, diâmetro e comprimento de espiga. Os índices de eficiência no uso de A. brasilense foram obtidos utilizando os dados referentes aos experimentos com A. brasilense e sem aplicação, para todos os caracteres avaliados. Pela análise dialélica os efeitos aditivos foram mais importantes do que os efeitos não-aditivos para a eficiência no uso de A. brasilense para a maioria dos caracteres, com exceção do peso médio de grãos sob inoculação. Os genótipos do grupo heterótico II apresentaram maior concentração e acúmulo de genes favoráveis para a EUAz eficiência. Os genitores 6 e 8 do grupo heterótico I e os genitores 2’ e 5’ do grupo heterótico II, são os que apresentaram melhores estimativas de capacidade geral de combinação, enquanto o cruzamento 8x6’ apresenta maior estabilidade e complementariedade gênica para eficiência no uso de A. brasilense para a produtividade de grãos. A partir das análises dialélicas por modelos mistos para a produtividade de grãos, as combinações 8 x 6’, 2 x 4’ e 1 x 2’ e os parentais envolvidos nesses cruzamentos foram selecionados para desenvolvimento de híbridos. Os genitores 1, 2, 8 do grupo heterótico I e 1’, 2’, 4’, 5’ e 6’ do grupo heterótico II foram selecionados para formar população base para o programa de melhoramento genético para eficiência no uso de Azospirillum brasilense. Pela análise GGE biplot, para a característica produtividade de grãos, constatou-se que os genótipos 11, 5, 12 e 19 são os mais promissores para a eficiência no uso de A. brasilense. O genótipo dialélico 11 é o mais próximo do “genótipo ideal” dentre o conjunto dos genótipos avaliados. Os ambientes sob aplicação de A. brasilense possuem maior capacidade de discriminar os genótipos.<br>The nitrogen is one of the elements most required by maize crop because it is directly related to the agricultural production capacity of this species. Most brazilian soils have nitrogen deficiency or it is not available in sufficient quantities to meet the need of the crop, and it is necessary to make supplemental nitrogen applications to reach high yields. The current maize cultivars are highly dependent on nitrogen fertilization since they were developed for optimized environments and were not adapted to the conditions under low nitrogen cultivation. The use of diazotrophic bacteria, which have the ability to fix nitrogen and make it available to plants, as well as the association of plants with these bacteria, can become an ecologically sustainable and economically viable alternative for the production of corn in agricultural systems under low or non-use of nitrogen. Thus, the objectives of this study were to identify and characterize efficient maize genotypes in the use of nitrogen and Azospirillum brasilense in contrasting environments regarding nitrogen availability in the soil and to select higher parental maize for efficiency in the use of Azospirillum brasilense. Were used as genetic material 48 diallelic genotypes were obtained in a partial diallel scheme, using eight lines belonging to the heterotic group I, and six lines belonging to the heterotic group II, all from the Department of Genetics of ESALQ / USP. The experiments were conducted in three crops in a randomized block design with two replicates and three controls. In each season, three experiments were carried out, in which they were differentiated by the application of A. brasilense, application of nitrogen in coverage and without any application, respectively. The evaluated traits were plant height, ear height, male and female blossoms, tipping, grain yield, mean grain weight, number of grains per row, number of rows of grain and diameter and length of the spike. The indices of efficiency in the use of A. brasilense were obtained using the data referring to the experiments with A. brasilense and without application, for all evaluated characters. By conventional diallel analysis the additive effects were more important than the non-additive effects for the efficiency in the use of A. brasilense for most of the characters, except for the average grain weight. The genotypes of the heterotic group II present greater concentration and consistency of genes favorable for this efficiency. The heterotic group I and the heterotic group I and the heterotic group I and the heterotic group II, are the ones that present the best estimates of overall combining ability, while the 8x6 'crossroads present greater stability and gene complementarity for efficiency in the use of A. brasilense. From diallel analyzes by mixed models the combination 8 x 6’, 2 x 4’, 1 x 2’ and parents involved in these crosses were selected for development of hybrids. The genitors 1, 2, 8 from the heterotic group I and 1’, 2’, 4’, 5’ and 6’ from the heterotic groups II were selected to explore basic population in A. brasilense-use efficiency breeding program. From the GGE biplot analysis, for the characteristic grain yield, it was contacted that the diallel genotypes 11, 5, 12 and 19 are the most promising for the efficiency in the use of A. brasilense. The diallelic genotype 11 is the closest to the "ideal genotype" among the set of diallel genotypes evaluated. The environments under application of A. brasilense have a greater ability to discriminate the genotypes.

Since researchers Maxwell McCombs and Donald Shaw published the seminal work of Chapel Hill on agenda-setting theory (AS) at the beginning of the 1970s, its academics have developed their investigations using two methodological tools: a first phase of content analysis, used to study the news coverage and, therefore, the prominence or salience of certain topics or attributes in the agenda; and a second study of statistical nature, based on the verification of the level of correlation between the issues, their senders or the global of the different agendas. Content analysis has generally focused on the relative or comparative importance of some topics over others. It is assumed that some media or social actors are busier than others in certain topics, which has led to a general study of the relative volume of information, to the detriment of the detailed content analysis. Such differences (or similarities) have been traditionally evaluated from the comparison of the frequencies of their main contents, which, by means of Spearman correlations between the senders, have fostered an analysis on the hierarchical order, disregarding the relative or absolute and, therefore, any other information. In view of this need, and to resolve these procedures when the interest lies in the relative importance of the parts of a whole, the thesis deals with the analysis of Compositional Data (CoDa), founded by Aitchinson in 1982, along with the graphic representation through biplots, promoted by Gabriel in 1971, for the study and mapping of the AS theory. The interest in CoDa lies in the proportion of each part or component, since the absolute quantities are irrelevant and only expose the sample size. A very similar problem in all aspects that is established in the study of the AS, where the absolute data are irrelevant and mostly point to the content, the intent of the content, the popularity of the issuer, etc. In this line, only the proportions of each content category or the relative size of one type of content over another are 3/3 truly informative. According to these premises, the present doctoral thesis is structured in five articles that distinguish the originality of the study in presenting the CoDa-biplot as a tool for mapping and studying the AS theory. Using the period of the 2015 General Elections in Spain, the thesis is presented as a thematic unit that addresses, throughout five publications, the mapping and compositional study of the theory of AS of opinion on politics in the Spanish media system (media agenda), as well as political communications on Twitter of the main Spanish parties (political agenda). Likewise, the aggregate study of both agendas (the media and politics) allows us to approach the analysis and representation of the intermediate AS<br>Desde que a inicios de la década de los años setenta los investigadores Maxwell McCombs y Donald Shaw publicaran el trabajo seminal de Chapel Hill sobre la teoría de la agenda-setting (AS), sus académicos han abordado, fundamentalmente, las investigaciones mediante dos herramientas metodológicas: una primera fase de análisis de contenido, empleada para estudiar la cobertura noticiosa y, por ende, la prominencia o saliencia de determinados temas o atributos en la agenda; y un segundo estudio de naturaleza estadística, basado en la verificación del nivel de correlación existente entre los temas, sus emisores o el global de las distintas agendas. El análisis de los contenidos generalmente se ha enfocado en la importancia relativa o comparativa de algunos temas sobre otros. Se supone que algunos medios o actores sociales están más ocupados que otros en determinados temas, lo que ha llevado a un estudio general del volumen relativo de información, en detrimento del análisis detallado del contenido. Tales diferencias (o similitudes) se han evaluado tradicionalmente a partir de la comparación de las frecuencias de sus contenidos principales, que, por medio de las correlaciones de Spearman entre los emisores, han fomentado un análisis sobre el ordenamiento jerárquico, despreciando las diferencias relativas o 2/3 absolutas y, por lo tanto, cualquier otra información. A la vista de esta necesidad, y para resolver estos procedimientos cuando el interés reside en la importancia relativa de las partes de un todo, la tesis aborda el análisis de Datos Composicionales (CoDa), fundamentado por Aitchinson en 1982, junto con la representación gráfica a través de biplots, impulsada por Gabriel en 1971, para el estudio y mapeo de la teoría de la AS. El interés en CoDa reside en la proporción de cada parte o componente, ya que las cantidades absolutas son irrelevantes y sólo exponen el tamaño de la muestra. Un problema muy similar en todos los aspectos al que se establece en el estudio de la AS, donde los datos absolutos son irrelevantes y en su mayoría apuntan sobre la temática del contenido, la intencionalidad del mismo, la popularidad del emisor, etc. En esta línea, sólo las proporciones de cada categoría de contenido o el tamaño relativo de un tipo de contenido sobre otro, son verdaderamente informativos. De acuerdo con estas premisas, la presente tesis doctoral se estructura en cinco artículos que distinguen la originalidad del estudio en presentar el CoDa-biplot como herramienta para el mapeo y estudio composicional de la teoría de la AS. Sirviéndose del período de las Elecciones Generales en España de 2015, la tesis se presenta como una unidad temática que aborda, a lo largo de cinco publicaciones, el mapeo y estudio composicional de la teoría de la AS de la opinión sobre política en el sistema mediático español (agenda de los medios), así como de las comunicaciones de carácter político en Twitter de los principales partidos españoles (agenda política). Asimismo, el estudio agregado de ambas agendas (la de los medios y la política) nos permite abordar el análisis y representación de la intermedia AS

Introduktion: Vänsterkammarens ejektionsfraktion (VKEF) är ett central mått på systolisk funktion i vänster kammare och är en av de mest betydelsefulla parametrar vid ekokardiografiska undersökningar. Idag är Simpson biplan metoden den mest använda metoden för bestämning av ejektionsfraktionen. Vid ekokardiografiska undersökningar sänder givaren med piezoelektriska kristaller ut ultraljudsvågor med en frekvens över 20 000 Hz. Ljudvågorna som skickas ut i kroppen reflekteras och sedan återvänder de till givaren för att skapa en bild. Denna studie jämför den tvådimensionella (2D) ultraljudsmetoden Simpsons biplan med Philips Heart Model som är en automatiserad tredimensionella (3D) funktion för bedömning av VKEF.  Material och metod: I studien inkluderades 31 hjärtfriska försökspersoner mellan åldrarna 21-64. Det samlades in bilder på apikala 4- och 2 kammarbilder från alla försökspersoner där Simpsons biplan metoden användes för att beräkna ejektionsfraktion. Apikala 4-kammarbilder samlades in för att beräkna ejektionsfraktionen med Philips Heart Model 3D funktion.  Resultat: Resultatet från denna studie visade att det inte föreligger någon signifikant skillnad mellan Simpsons biplan metoden och Philips Heart Model metoden för bestämning av ejektionsfraktion. Båda metoderna visade likvärdiga mätresultat.  Diskussion: Philips Heart Model metoden är en relativ ny funktion som använder sig av artificiell intelligens för att analysera 3D bilder. Philips Heart Model metoden är en säker funktion att använda då de flesta studier bevisar likvärdiga och säkra mätresultat i jämförelse med andra metoder.  Konklusion: Enligt denna studie ger Philips Heart Model funktionen likvärdiga mätresultat av vänsterkammarens ejektionsfraktion i jämförelse med Simpsons biplan.<br>Introduction: Simpson’s biplane method is the most used method for determining the left ventricular ejection fraction (LVEF) in echocardiographic examinations. Ejection fraction is a central measurement of the heart's global systolic function. The probe with piezoelectric crystals emits ultrasound waves with a frequency above 20,000 Hz. The sound waves that are sent out into the body are reflected and then return to the probe to create an image. This study compares the two-dimensional (2D) ultrasound Simpson's biplane method with the Philips Heart Model method, which is an automated three-dimensional (3D) function for assessment of LVEF.                                                                                                                          Material and method: 31 subjects with no recorded heart pathologies between the ages of 21-64 were included in the study. Apical 4- and 2-chamber images were collected from the test subjects, where the Simpson's biplane method was applied to calculate the ejection fraction. 2D apical 4-chamber images were collected to convert to 3D and used to calculate the ejection fraction with the Philips Heart Model.    Results: The results of this study showed that there is no significant difference between the Simpson’s biplane method and the Philips Heart Model method for determining ejection fraction.    Discussion: The Philips Heart Model method is a relatively new feature that uses artificial intelligence to analyze 3D images. The Philips Heart Model method is a reliable feature to use as most studies have proven similar and reliable measurements when comparing it with other methods for determining LVEF.    Conclusion: According to this study, the Philips Heart Model feature provides equivalent measurements in comparison with the manual method Simpson's biplane.