Journal articles: 'Biplot analysis'

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Oliveira da Silva, Alberto, and Adelaide Freitas. "Time Series Components Separation Based on Singular Spectral Analysis Visualization: an HJ-biplot Method Application." Statistics, Optimization & Information Computing 8, no. 2 (May 18, 2020): 346–58. http://dx.doi.org/10.19139/soic-2310-5070-897.

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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.

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Yan, Weikai, and Nicholas A. Tinker. "Biplot analysis of multi-environment trial data: Principles and applications." Canadian Journal of Plant Science 86, no. 3 (July 7, 2006): 623–45. http://dx.doi.org/10.4141/p05-169.

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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

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Yan, Weikai, and L. A. Hunt. "Biplot Analysis of Diallel Data." Crop Science 42, no. 1 (2002): 21. http://dx.doi.org/10.2135/cropsci2002.0021.

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Neisse, Anderson Cristiano, Jhessica Letícia Kirch, and Kuang Hongyu. "AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data." Biometrical Letters 55, no. 2 (December 1, 2018): 97–121. http://dx.doi.org/10.2478/bile-2018-0008.

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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.

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Yan, Weikai, and Duane E. Falk. "Biplot Analysis of Host-by-Pathogen Data." Plant Disease 86, no. 12 (December 2002): 1396–401. http://dx.doi.org/10.1094/pdis.2002.86.12.1396.

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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.

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ARIAWAN, I. MADE ANOM, I. PUTU EKA NILA KENCANA, and NI LUH PUTU SUCIPTAWATI. "KOMPARASI ANALISIS GEROMBOL (CLUSTER) DAN BIPLOT DALAM PENGELOMPOKAN." E-Jurnal Matematika 2, no. 4 (November 29, 2013): 17. http://dx.doi.org/10.24843/mtk.2013.v02.i04.p053.

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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.

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Bocanski, Jan, Aleksandra Nastasic, Dusan Stanisavljevic, Zorana Sreckov, Bojan Mitrovic, Sanja Treskic, and Mirjana Vukosavljev. "Biplot analysis of diallel crosses of NS maize inbred lines." Genetika 43, no. 2 (2011): 277–84. http://dx.doi.org/10.2298/gensr1102277b.

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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.

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Cruz, Derivaldo Pureza da, Tâmara Rebecca Albuquerque de Oliveira, Andréa Barros Silva Gomes, Camila Queiroz da Silva Sanfim de Sant'Anna, Lília Marques Gravina, Richardson Sales Rocha, Mário Euclides Pechara da Costa Jaeggi, et al. "Selection of Cowpea Lines for Multiple Traits by GYT Biplot Analysis." Journal of Agricultural Studies 8, no. 2 (March 2, 2020): 124. http://dx.doi.org/10.5296/jas.v8i2.16003.

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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.

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Alvarez, Wilin, and Victor John Griffin. "GH Biplot in Reduced-Rank Regression based on Partial Least Squares." Statistics, Optimization & Information Computing 9, no. 3 (July 10, 2021): 717–34. http://dx.doi.org/10.19139/soic-2310-5070-1112.

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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.

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Otoo, E., K. Osei, J. Adomako, A. Agyeman, A. Amele, D. de Koeyer, P. Adebola, and R. Asiedu. "GGE Biplot Analysis of 12 Dioscorea rotundata Genotypes in Ghana." Journal of Agricultural Science 10, no. 1 (December 13, 2017): 249. http://dx.doi.org/10.5539/jas.v10n1p249.

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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.

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Oliveira, Tâmara Rebecca Albuquerque de, Hélio Wilson Lemos de Carvalho, Gustavo Hugo Ferreira Oliveira, Emiliano Fernandes Nassau Costa, Geraldo de Amaral Gravina, Rafael Dantas dos Santos, and José Luiz Sandes de Carvalho Filho. "Hybrid maize selection through GGE biplot analysis." Bragantia 78, no. 2 (June 2019): 166–74. http://dx.doi.org/10.1590/1678-4499.20170438.

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Zhang, Meiqin, Manjit S. Kang, Paul F. Reese, and Harbans L. Bhardwaj. "Soybean Cultivar Evaluation via GGE Biplot Analysis." Journal of New Seeds 7, no. 4 (March 2006): 37–50. http://dx.doi.org/10.1300/j153v07n04_03.

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Sajesh, T. A., and M. R. Srinivasan. "Detection of Multidimensional Outliers using Biplot Analysis." Mapana - Journal of Sciences 7, no. 2 (November 30, 2008): 10–24. http://dx.doi.org/10.12723/mjs.13.2.

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It is necessary to examine the valuable data being distorted by the presence of outliers before the same is subjected to necessary analysis. Outliers should be identified using reliable detection methods and tested prior to performing data analysis. Detection of outliers in multidimensional data is important in many applications as it will have far reaching consequences in its analysis. There are methods available in the literature for detecting multiple outliers but there exist no unified method for detecting the same. An attempt has been made to detect the multidimensional outliers through Biplot analysis using elliptical method with a well defined axis (a, b) based on Inter Quartile Range (IQR).The performance of the designed methods is examined by a comparison with the existing methods.

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Yan, Weikai. "Biplot Analysis of Incomplete Two‐Way Data." Crop Science 53, no. 1 (January 2013): 48–57. http://dx.doi.org/10.2135/cropsci2012.05.0301.

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Gardner, Sugnet, and Niël J. le Roux. "Extensions of Biplot Methodology to Discriminant Analysis." Journal of Classification 22, no. 1 (June 2005): 59–86. http://dx.doi.org/10.1007/s00357-005-0006-7.

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Edugbo, Richmond Emuohwo, Godson Emeka Nwofia, and Lawrence Stephen Fayeun. "An Assessment of Soybean (Glycine max, L. Merrill) Grain Yield in Different Environments Using AMMI and GGE Biplot Models in Humidorest Fringes of Southeast Nigeria." Agricultura Tropica et Subtropica 48, no. 3-4 (December 1, 2015): 82–90. http://dx.doi.org/10.1515/ats-2015-0012.

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Abstract The yield of four soybean (Glycine max, L. Merrill) genotypes under six planting dates in two years was assessed using the Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype and Genotype-by-Environment biplot models. The results of combined analysis of variance for grain yield of the four genotypes of soybean grown in 12 environments showed that soybean grain yield was significantly (P < 0.01) affected by environments (E), genotypes (G) and genotype by environment interactions (GE). Genotypes and environments accounted for about 6.56% and 47.66% of the variation, respectively, while the GE explained 14.47% of the variation, which is more than double of the genotypic effects of the total variation. AMMI biplot indicated genotype TGx1485-1D and the early July 2012 environment were above average for grain yield and had positive specific interactions with each other. However, TGx1485-1D had negative interactions with the other environments while genotypesTGx14482E, TGx1987-10F and TGx1835-10E had positive interactions with all the environments except E5. In the differential yield ranking of genotypes across the twelve environments TGx1485-1D had the highest yield in seven out of the twelve environments. TGx1835-10E was the highest yielding genotype in three environments, while TGx1448-2E gave the greatest yield in two environments. Although TGx1485-1D exhibited high GEI, in the GGE biplot it was ranked as the most desirable genotype. GGE biplot identified early July 2012(E5) as the best environment. The result showed that application of AMMI and GGE biplots facilitates visual comparison and identified superior genotypes for each target set of environments.

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Mokhsan, Masykuri. "REGENCY / CITY GROUPING IN KALBAR USING BIPLOT STANDARDIZED METHOD." JURNAL BORNEO AKCAYA 5, no. 1 (December 2, 2019): 1–11. http://dx.doi.org/10.51266/borneoakcaya.v5i1.105.

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In principle, a biplot is a graphical methode in two dimensional visual. The information of the biplot result contains the objects that represent the rows of data matrix and the observed variables that represent the columns of data matrix. Many of the observed variables in social, economics, politics and any other studies have the different measurement scaling. That different measurement scaling must be standardized.. The standardized biplot analysis is part of the biplot analysis that has been developed to solve the different of measurement scaling between variables. The weight for the standardized biplot use standard deviation.The Standardized Biplot Scaling method in this study is applied on the regency’s classification in West Kalimantan case based on Human Development Index variables. The HDI’s variables such as Life expectacy Rate, Expected Years of Schooling, Mean Years School and Purchasing Power Parity have different measurement scaling. Before standardization with standard deviations, each value of the HDI variable is centralized by reducing the value of each HDI variable to the average value of each HDI variable.

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Sampurna, I. Putu, Tjokorda Sari Nindhia, Ni Nyoman Werdi Susari, and I. Ketut Suatha. "Application of Indonesian's National Standard for Grouping of Bali Cattle with Cluster and Biplot Analysis." Journal of Veterinary and Animal Sciences 3, no. 2 (August 28, 2020): 57. http://dx.doi.org/10.24843/jvas.2020.v03.i02.p01.

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Research on the application of the Indonesian National Standard (SNI) of Bali cattle by cluster analysis and biplot aims to provide a visual picture in the form of tables and graphs, so that it is easier and faster and more communicative in making decisions, whether the cows studied are included in class I, class II, or class III based on SNI Bali cattle. This study was conducted on 70-year-old adult female cows of 70 animals raised at the Integrated Farming System (Simantri) in Badung regency. The data obtained were analyzed by cluster analysis and biplot, as variables were shoulder height, body length and chest circumference, while as objects were 70 adult cows and 3 classes of Balinese cattle based on SNI of Bali cattle as object identifiers. The results obtained that the application of SNI for Bali cattle can be done by cluster analysis and biplot and both analyzes give the same results to the grouping of Bali cattle objects based on SNI for Bali cattle. Grouping by cluster analysis is easier to see based on the cluster membership obtained, whereas with biplot analysis provides additional information about correlations and diversity between variables.

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Cubilla-Montilla, Mitzi, Ana Belén Nieto-Librero, M. Purificación Galindo-Villardón, and Carlos A. Torres-Cubilla. "Sparse HJ Biplot: A New Methodology via Elastic Net." Mathematics 9, no. 11 (June 5, 2021): 1298. http://dx.doi.org/10.3390/math9111298.

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The HJ biplot is a multivariate analysis technique that allows us to represent both individuals and variables in a space of reduced dimensions. To adapt this approach to massive datasets, it is necessary to implement new techniques that are capable of reducing the dimensionality of the data and improving interpretation. Because of this, we propose a modern approach to obtaining the HJ biplot called the elastic net HJ biplot, which applies the elastic net penalty to improve the interpretation of the results. It is a novel algorithm in the sense that it is the first attempt within the biplot family in which regularisation methods are used to obtain modified loadings to optimise the results. As a complement to the proposed method, and to give practical support to it, a package has been developed in the R language called SparseBiplots. This package fills a gap that exists in the context of the HJ biplot through penalized techniques since in addition to the elastic net, it also includes the ridge and lasso to obtain the HJ biplot. To complete the study, a practical comparison is made with the standard HJ biplot and the disjoint biplot, and some results common to these methods are analysed.

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Dehghani, Hamid, Ehsan Feyzian, Mokhtar Jalali, Abdolmajid Rezai, and Fenny Dane. "Use of GGE biplot methodology for genetic analysis of yield and related traits in melon (Cucumis melo L.)." Canadian Journal of Plant Science 92, no. 1 (January 2012): 77–85. http://dx.doi.org/10.4141/cjps2010-046.

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Dehghani, H., Feyzian, E., Jalali, M., Rezai, A. and Dane, F. 2012. Use of GGE biplot methodology for genetic analysis of yield and related traits in melon ( Cucumis melo L.). Can. J. Plant Sci. 92: 77–85. A complete diallel cross experiment of six local Iranian melon populations (Eyvanaki, Abasali, Tashkandi, Hose-sorkh, Mashhadi and Mirpanji) and one cultivar (Ananasi) was conducted. Fruit number, average weight per fruit, yield and acceptable yield were re-evaluated using GGE biplot methodology. The two principal components of biplot explained 70, 58, 86 and 88% of total observed variation for yield, acceptable yield, average weight per fruit and fruit number per plant, respectively. Mirpanji had the highest GCA for yield, acceptable yield and average weight per fruit, but the highest negative GCA for fruit number per plant. Abasali showed the highest positive GCA for fruit number. Biplot analysis allowed a rapid and effective overview of general combining ability (GCA) and specific combining ability (SCA) effects of the populations, their performance in crosses, as well as grouping patterns of similar genotypes.

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KARIMIZADEH, Rahmatollah, Mohtasham MOHAMMADI, Naser SABAGHNI, Ali Akbar MAHMOODI, Barzo ROUSTAMI, Faramarz SEYYEDI, and Fariba AKBARI. "GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition." Notulae Scientia Biologicae 5, no. 2 (May 28, 2013): 256–62. http://dx.doi.org/10.15835/nsb529067.

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This investigation was done to study GE interaction over twelve environments for seed yield in 18 genetically diverse genotypes. Grain yield performances were evaluated for three years at four locations in Iran using a randomized complete block design. The first two principal components (IPC1 and IPC2) were used to create a two-dimensional GGE biplot that accounted percentages of 49% and 20% respectively of sums of squares of the GE interaction. The combined analysis of variance indicated that year and location were the most important sources affecting yield variation and these factors accounted for percentages of 50.0% and 33.3% respectively of total G+E+GE variation. The GGE biplot suggested the existence of three lentil mega-environments with wining genotypes G1, G11 and G14. According to the ideal-genotype biplot, genotype G1 was the better genotype demonstrating high mean yield and high stability of performance across test locations. The average tester coordinate view indicated that genotype G1 had the highest average yield, and genotypes G1 and G12 recorded the best stability. The study revealed that a GGE biplot graphically displays interrelationships between test locations as well as genotypes and facilitates visual comparisons.

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dos Santos, Adriano, Francisco Eduardo Torres, Erina Vitório Rodrigues, Ariane de Andréa Pantaleão, Larissa Pereira Ribeiro Teodoro, Leonardo Lopes Bhering, and Paulo Eduardo Teodoro. "Nonlinear Regression and Multivariate Analysis Used to Study the Phenotypic Stability of Cowpea Genotypes." HortScience 54, no. 10 (October 2019): 1682–85. http://dx.doi.org/10.21273/hortsci14322-19.

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This study aimed to evaluate the adaptability and phenotypic stability of cowpea genotypes using a nonlinear regression analysis and multivariate analysis. Experiments were performed at four sites in Brazil using a randomized blocks design with 20 treatments and four replications. The adaptability and stability of genotypes were evaluated by Toler nonlinear regression and genotype plus genotype × environment (GGE) biplot methodologies. Most of the genotypes revealed linear response patterns, with no differences regarding the favorable and unfavorable environments. Regarding the genotype classification for stability and adaptability, the Toler and GGE biplot methodologies are congruent. Genotypes MNC99-537F-4, MNC00-561G-6, MNC99542F-5, and Patativa have high overall adaptability and adequate yield. Therefore, they should be recommended for cultivation in the tested environments. Genotypes closer to the ideotype by the GGE biplot method are considered doubly desirable by the nonlinear method.

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Gardner-Lubbe, S., N. J. le Roux, H. Maunders, V. Shah, and S. Patwardhan. "Biplot methodology in exploratory analysis of microarray data." Statistical Analysis and Data Mining: The ASA Data Science Journal 2, no. 2 (July 16, 2009): 135–45. http://dx.doi.org/10.1002/sam.10038.

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Pržulj, N., M. Mirosavljević, P. Čanak, M. Zorić, and J. Boćanski. "Evaluation of spring barley performance by biplot analysis." Cereal Research Communications 43, no. 4 (December 2015): 692–703. http://dx.doi.org/10.1556/0806.43.2015.018.

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Álvarez, Isabel Gallego, Beatriz Cuadrado Ballesteros, and Nicaury Mejía Rosario. "IFRS implementation at international level: a biplot analysis." International Journal of Accounting, Auditing and Performance Evaluation 12, no. 4 (2016): 422. http://dx.doi.org/10.1504/ijaape.2016.079866.

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Gallego Álvarez, Isabel, Nicaury Mejía Rosario, and Beatriz Cuadrado Ballesteros. "IFRS implementation at international level: a biplot analysis." International Journal of Accounting, Auditing and Performance Evaluation 12, no. 4 (2016): 422. http://dx.doi.org/10.1504/ijaape.2016.10000459.

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Serafim, A., R. Company, B. Lopes, N. Silva, E. Castela, M. J. Bebianno, and G. Castela. "Profile analysis of mothers susceptible to contaminant exposure in the Algarve region: Application of the HJ-BIPLOT method." Biometrical Letters 49, no. 1 (June 1, 2012): 57–66. http://dx.doi.org/10.2478/bile-2013-0004.

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Summary The HJ-BIPLOT method developed by Galindo (1986) was applied in order to identify and categorize mothers vulnerable to environmental contamination in the Algarve region (South Portugal). The application of the BIPLOT method made it possible to recognize the most important exposure routes for contamination, showing that workplace, diet and smoking habits seem the most significant factors contributing to maternal and foetal exposure vulnerability

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& et al., Motahhari. "THE ANALYSIS OF GENOTYPE × ENVIRONMENT INTERACTION USING RAPESEED (BRASSICA NAPUS L.) BY GGE BIPLOT METHOD." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 51, no. 5 (October 31, 2020): 1337–49. http://dx.doi.org/10.36103/ijas.v51i5.1143.

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This study was aimed to asses seed yield performances of 16 rapeseed genotypes in randomized complete block designs (RCBD) with three replications at four Agricultural Research Stations of cold and mid-cold regions over two years in Iran (2015-2017). GGE biplot analysis indicated that the first two components explained 83% of seed yield variations. Genotype, location and their interaction explained 18%, 52% and 30%of the total GE variation, respectively. In this research, a graphically represented GGE biplot analysis enabled selection of stable and high-yielding genotypes for all investigated locations, as well as genotypes with specific adaptability. The GGE biplot analysis was adequate in explaining GE interaction for seed yield in rapeseed. It can be concluded that genotypes G2, G4 and G13 had the highest mean seed yield and stability in four investigated locations. For specific adaptability, G13 was recommended for Isfahan, Karaj and Kermanshah and G4 for Mashhad.

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Sincik, Mehmet, Abdurrahim T. Goksoy, Emre Senyigit, Yahya Ulusoy, Mustafa Acar, Sahin Gizlenci, Gulhan Atagun, and Sami Suzer. "Response and yield stability of canola (Brassica napus L.) genotypes to multi-environments using GGE biplot analysis." Bioagro 33, no. 2 (April 29, 2021): 105–14. http://dx.doi.org/10.51372/bioagro332.4.

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he GxE interaction (GEI) provides essential information for selecting and recommending cultivars in multi-environment trials. This study aimed to evaluate genotype (G) and environment (E) main effects and GxE interaction of 15 canola genotypes (10 canola lines and 5 check varieties) over 8 environments and to examine the existence of different mega environments. Canola yield performances were evaluated during 2015/16 and 2016/17 production season in three different locations (Southern Marmara, Thrace side of Marmara, and Black Sea regions) of Turkey. The trial in each location was arranged in a randomized complete block design with four replications. The seed yield data were analyzed using GGE biplot and the yield components data were analyzed using ANOVA. The agronomical traits revealed that environments, genotypes, and GEI were significant at 1 % probability for all of the characters. The variance analysis exhibited that genotypes, environments, and GEI explained 21.6, 21.7, and 25.7 % of the total sum of squares for seed yield, respectively. The GGE biplot analysis showed that the first and second principal components explained 57.3 and 18.3 % of the total variation in the data matrix, respectively. GGE biplot analysis showed that the polygon view of a biplot is an excellent way to visualize the interactions between genotypes and environments.

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Čanak, Petar, Milan Mirosavljević, Miroslav Zorić, Mihajlo Ćirić, Bojana Vujošević, Bojan Mitrović, and Dušan Stanisavljević. "Biplot analysis of seed priming effects on maize seedling growth traits." Ratarstvo i povrtarstvo 55, no. 3 (2018): 111–17. http://dx.doi.org/10.5937/ratpov1803111c.

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Nataraj, V., N. Pandey, R. Ramteke, P. Verghese, R. Reddy, T. Onkarappa, S. P. Mehtre, et al. "GGE biplot analysis of vegetable type soybean genotypes under multi-environmental conditions in India." Journal of Environmental Biology 42, no. 2 (March 1, 2021): 247–53. http://dx.doi.org/10.22438/jeb/42/2/mrn-1405.

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Aim: To understand the magnitude and pattern of genotype-environment interaction in vegetable type soybeans and to identify mega environment(s) and best performing genotype(s) across environments. Methodology: Five vegetable type soybean genotypes were evaluated across five geographical locations viz., Indore, Parbhani, Adilabad, Bengaluru and Pune, during rainy season of 2018. Genotypes were grown in a plot size of 1.35 x 3 m2 in three replications in randomized block design. Data on green pod yield, green seed test weight, days to 50% flowering, days to maturity and plant height were recorded using standard methods. GGE biplot analysis was performed using software “GGE Biplot version 7.0”. Results: In the present investigation, except in case of green seed test weight, in remaining four traits, major portion of variation was contributed by location (52.95-79.4%) followed by genotype (17.7-42.7%) and genotype x location interaction (2.21-4.29%). Through GGE biplot analysis, Bengaluru was found to be near ideal environment and genotypes Karune and Harasoya were found to be the best performers across the locations with respect to green pod yield. Interpretation: Bengaluru was found to be near ideal environment for vegetable type soybean evaluation. Selection for genotypes having wider adaptability can be conducted at this location. Genotypes Karune and Harasoya were found to be the best performers with respect to green pod yield. These two genotypes can be included as parents for breeding as vegetable type soybean. Key words: GEE biplot, Multienvironmental analyses, Soybean genotypes

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Mattos, Pedro Henrique Costa de, Ricardo Augusto de Oliveira, João Carlos Bespalhok Filho, Edelclaiton Daros, and Mario Alvaro Aloiso Veríssimo. "Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis." Crop Breeding and Applied Biotechnology 13, no. 1 (March 2013): 83–90. http://dx.doi.org/10.1590/s1984-70332013000100010.

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The purpose of this study was to evaluate sugarcane genotypes for the trait tons of sugar per hectare (TSH), stratifying five production environments in the state of Paraná. The performance of 20 genotypes and 2 standard cultivars was analyzed in three consecutive growing seasons by the statistical methods AMMI and GGE Biplot. The GGE Biplot grouped the locations into two mega-environments and indicated the best-performing genotypes for each one, facilitating the selection of superior genotypes. Another advantage of GGEBiplot is the definition of an ideal genotype (G) and environment (E), serving as reference for the evaluation of genotypes and choice of environments with greater GE interaction. Both models indicated RB006970, RB855156 and RB855453 as the genotypes with highest TSH and São Pedro do Ivai as the environment with the greatest GE interaction. Both approaches explained a high percentage of the sum of squares, with a slight advantage of AMMI over GGE Biplot analysis.

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Ferreira, Haiany Aparecida, Érica Resende de Oliveira, Carla Regina Guimarães Brighenti, and Marcelo Ângelo Cirillo. "Gee-logit model corrected biplots with harvest effects on coffee beans grading." Bioscience Journal 37 (August 20, 2021): e37044. http://dx.doi.org/10.14393/bj-v37n0a2021-53679.

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In a granulometric analysis of coffee beans with different categories of defects, the data can be organized in contingency tables, and when considering the discrimination by harvest, they may have a structure that suggest a more complex model, by means of the counting of defective coffee beans compared to different crops interacting with the classification of defects and percentages of sieve grains, which characterizes a block design with multivariate responses. However, due to the techniques based on the analysis of variance, considering the uniform correlation structure for all plots, it becomes feasible to propose a model that allows contemplating different structures between the plots, associating the effects of the crops to the defects in the granulometric procedure applied to the coffee beans. Thus, the hypothesis of incorporating the effects of crops associated with defects arises using the biplot multivariate technique. This work aims to propose the use of corrected biplots by predictions obtained trhough the fit to the Generalized Linear Model in the coffee grain size classification, broken down by components of the effect of the harvests. In conclusion, the use of GEE models with the corrected biplot technique by the predictions is feasible for application to be applied to the granulometric analysis of defective coffee beans, presenting discrimination regarding the effects of harvests.

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Faheem, Muhammad. "Selection of Wheat Ideotype Based on Multiple Traits using Genotype by Yield-Trait Approach." International Journal of Agriculture and Biology 25, no. 06 (June 1, 2021): 1367–74. http://dx.doi.org/10.17957/ijab/15.1799.

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In plant breeding, a novel genotype-by-yield trait (GYT) biplot approach was introduced to select superior genotypes based on multiple traits. The present study demonstrated the application of the GYT biplot model to evaluate the superior wheat advanced lines from a panel of 24 genotypes to select the ideotype for end users. Results show that the genotype-by-trait (GT) biplot covered 57% of the total variation of the data to reveal that grain yield was strongly associated with 1000-grain weight and grain width. In contrast, the GYT biplot explained 90.2% of the total variation which was significantly much higher than GT biplot. According to tester vector view of GYT biplot almost all the yield trait combinations were associated with each other at different degree of association; whereas the genotypes present within the acute angles of tester vectors (yield trait combinations) had the trait profile contributed positively towards grain yield. The polygon biplot of GYT had eight sectors, out of which only three had the yield trait combinations. The eight genotypes were the polygon vertex among which the advanced line DF1906 of first sector was designated as the best genotype for spike length, number of spikelets per spike, grain weight per spike and number of grains per spike. Additionally, the DF1912 of second sector was early maturing coupled with high 1000-grain weight while DF1917 of third sector had short stature and gave the highest harvest index. The average tester coordination (ATC) biplot grouped 13 genotypes as superior and nine as inferior genotypes and recommended two advanced lines DF1912 and DF1917 as ideotype based on balanced traits profile. These findings strengthened the argument that the GYT biplot analysis is better than other selection indices and guaranteed the selection of superior genotypes and rejection of inferior ones based on multiple traits yield combinations. © 2021 Friends Science Publishers

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Ukalski, Krzysztof, and Marcin Klisz. "Application of GGE biplot graphs in multi-environment trials on selection of forest trees." Folia Forestalia Polonica 58, no. 4 (December 1, 2016): 228–39. http://dx.doi.org/10.1515/ffp-2016-0026.

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Abstract In the studies on selection and population genetics of forest trees that include the analysis of genotype × environment interaction (GE), the use of biplot graphs is relatively rare. This article describes the models and analytic methods useful in the biplot graphs, which enable the analyses of mega-environments, selection of the testing environment, as well as the evaluation of genotype stability. The main method presented in the paper is the GGE biplot method (G - genotype effect, GE -genotype × environment interaction effect). At the same time, other methods have also been referred to, such as, SVD (singular value decomposition), PCA (principal component analysis), linear-bilinear SREG model (sites regression), linear-bilinear GREG model (genotypes regression) and AMMI (additive main effects multiplicative interaction). The potential of biplot method is presented based on the data on growth height of 20 European beech genotypes (Fagus sylvatica L.), generated from real data concerning selection trials and carried out in 5 different environments. The combined ANOVA was performed using fixed- -effects, as well as mixed-effects models, and significant interaction GE was shown. The GGE biplot graphs were constructed using PCA. The first principal component (GGE1) explained 54%, and the second (GGE2) explained more than 23% of the total variation. The similarity between environments was evaluated by means of the AEC method, which allowed us to determine one mega-environment that comprised of 4 environments. None of the tested environments represented the ideal one for trial on genotype selection. The GGE biplot graphs enabled: (a) the detection of a stable genotype in terms of tree height (high and low), (b) the genotype evaluation by ranking with respect to the height and genotype stability, (c) determination of an ideal genotype, (d) the comparison of genotypes in 2 chosen environments.

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Dachowski, Ryszard, and Katarzyna Gałek. "Selection of the optimal solution of acoustic screens in a graphical interpretation of biplot and radar charts method." Open Engineering 8, no. 1 (December 26, 2018): 471–77. http://dx.doi.org/10.1515/eng-2018-0061.

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Abstract The choice of an optimal solution among various available technological and material undertakings often becomes a problem of the engineering community. A multi-criteria technical and economic analysis is used in order to facilitate decision making. The objective of this article is to present biplot methods and radar charts as a possibility of graphic presentation of research results by obtaining an optimal solution on the example of an analysis of technological and material undertakings of acoustic screens. The research consisted in identifying technological and material solutions of the selected acoustic screens, and then defining features (criteria) and cases (technological and material solutions). The results were presented in radar charts and biplot-type graphs. The methodology consisted in generating a data matrix, which is then processed, decomposed and finally scaled. The calculations were carried out in the Statistica programme. The carried-out analysis showed that the spider web and biplot methods differ from each other. The biplot graph more precisely describes the solutions, and it presents the correlations between variables and cases.

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ARUNA, C., S. RAKSHIT, P. K. SHROTRIA, S. K. PAHUJA, S. K. JAIN, S. SIVA KUMAR, N. D. MODI, D. T. DESHMUKH, R. KAPOOR, and J. V. PATIL. "Assessing genotype-by-environment interactions and trait associations in forage sorghum using GGE biplot analysis." Journal of Agricultural Science 154, no. 1 (March 24, 2015): 73–86. http://dx.doi.org/10.1017/s0021859615000106.

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SUMMARYForage sorghum is an important component of the fodder supply chain in the arid and semi-arid regions of the world because of its high productivity, ability to utilize water efficiently and adaptability to a wide range of climatic conditions. Identification of high-yielding stable genotypes (G) across environments (E) is challenging because of the complex G × E interactions (GEI). In the present study, the performance of 16 forage sorghum genotypes over seven locations across the rainy seasons of 2010 and 2011 was investigated using GGE biplot analysis. Analysis of variance revealed the existence of significant GEI for fodder yield and all eight associated phenotypic traits. Location accounted for a higher proportion of the variation (0·72–0·91), while genotype contributed only 0·06–0·21 of total variation in different traits. Genotype-by-location interactions contributed 0·02–0·13 of total variation. Promising genotypes for fodder yield and each of the associated traits could be identified effectively using a graphical biplot approach. The majority of test locations were highly correlated. A ‘Which-won-where’ study partitioned the test locations into two mega-environments (MEs): ME1 was represented by five locations with COFS 29 as the best genotype, while ME2 had two locations with S 541 as the best genotype. The existence of two MEs suggested a need for location-specific breeding. Genotype-by-trait biplots indicated that improvement for forage yield could be achieved through indirect selection for plant height, leaf number and early vigour.

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Sabaghnia, Naser, and Mohsen Janmohammadi. "Analysis of the impact of nano-zinc, nano-iron, and nano-manganese fertilizers on chickpea under rain-fed conditions." Annales Universitatis Mariae Curie-Sklodowska, sectio C – Biologia 70, no. 2 (October 20, 2016): 43. http://dx.doi.org/10.17951/c.2015.70.2.43.

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<p>Nanotechnology is an emerging field of science widely exploited in many scientific fields but its application in agriculture is rarely studied in the world. In the current study, application of nanotechnology in agricultural via the application of some micronutrient nano-fertilizers (nano-zinc, nano-iron, and nano-manganese) and different sulfur fertilizers have been investigated. Three levels of sulfur fertilizer (S1: no application, S2: 15 Kg ha-1, S3: 30 Kg ha-1) and three micronutrients nano-fertilizer (Nano1: nano-chelated zinc, Nano2: nano-chelated iron, and Nano3: nano-chelated manganese) were studied on some morphophysiological traits of chickpea. Results showed that the first two principal components of treatment × trait (TT) biplot accounted to 56% and 18% respectively of total variation. The vertex treatments in polygon biplot were S1-Nano2, S1-Nano3, S2-Nano1, S3-Nano1, and S3-Nano2 which S3-Nano1 treatment indicated high performance in day to maturity, plant height, first pod height, primary branch per plants, secondary branch per plant, number of pods per plant, number of seeds per plant and 1,000 seed weight. According to vector-view biplot, seed yield was positively associated with the number of pods per plant, harvest index and day to maturity. The ideal treatment identified the S3-Nano1 (30 kg ha-1 sulfur plus nano-chelated zinc) that might be used in selecting superior traits and it can be considered as the candidate treatment. The ideal trait of biplot showed that seed yield had the highest discriminating ability and they were the most representative and as the final target trait of producers, it has the ability of discrimination among different treatm ents. The best fertilizer treatment for obtaining of high seed yield was identified in the vector-view function of TT biplot as S3-Nano1 (30 kg ha-1 sulfur plus nano-chelated zinc).</p>

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Sharifi, Peyman, and Mohammad Reza Safari Motlagh. "Biplot analysis of diallel crosses for cold tolerance in rice at the germination stage." Crop and Pasture Science 62, no. 2 (2011): 169. http://dx.doi.org/10.1071/cp10207.

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This paper reports analysis of 7 × 7 diallel crosses using a genotype main effect plus genotype-by-environment interaction biplot for determining cold tolerance at the germination stage in rice. ANOVA indicated that there were highly significant differences among the replications, genotypes, general combining ability (GCA) and specific combining ability (SCA) for percentage of reduction in radicle length (RL), coleoptile length (CL) and germination percentage (GP). The hybrid Neda × Hassani had the highest mid-parent heterosis for RL, CL and GP (–58.84, –68.47 and –80.77%, respectively). This result indicated that the reduction of three traits in crosses of Neda × Hassani was lower than their parents. The graphical representation by biplot analysis allowed a rapid and effective overview of GCA and reveals that Deilamani was an ideal general combiner for all traits and this parent is a superior variety for these three traits. Three potential heterotic groups are suggested for RL reduction. Four potential heterotic groups were identified for the two other traits, in the biplot. The first two principal component (PC) axes in the biplot for reduction in GP explained 85% of the variation with first and second principal components (PC1 and PC2, respectively). An important inference that can be drawn from these results is that cross combinations involving Hassani and Deilamani as one of the parents recorded desirable SCA effects for all or most of the studied traits. The information obtained from this experiment can facilitate the identification of hybrids that combine cold resistance traits in rice.

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Priyanto, Slamet Bambang, Roy Efendi, Bunyamin Z., M. Azrai, and M. Syakir. "Evaluasi Stabilitas Hasil Jagung Hibrida Menggunakan Metode Genotype and Genotype by Environment Interaction Biplot (GGE BIPLOT)." Jurnal Penelitian Pertanian Tanaman Pangan 1, no. 2 (September 12, 2017): 97. http://dx.doi.org/10.21082/jpptp.v1n2.2017.p97-104.

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<p class="Abstrak">Visualization of GGE biplot analyses was able to explain the genotype by environment interaction. This research was aimed to determine the yield stability of promising experimental maize hybrids in eight locations based GGE biplot method. Ten promising experimental maize hybrids and two commercial hybrid varieties as check, namely: HBSTK01, HBSTK03, HBSTK05, HBSTK06, HBSTK07, HBSTK08, HBSTK09, HBSTK10, HBSTK11, HBSTK13 and Bima 16 and Pertiwi 3 were evaluated in eight locations, ie. Bangka (Bangka Belitung), Probolinggo (East Java), Minahasa Utara (North Sulawesi), Donggala (Central Sulawesi), Soppeng, South Sulawesi, Gowa (South Sulawesi, Konawe (Southeast Sulawesi)and Lombok Barat (West Nusa Tenggara) from May to October 2013. The treatments were arranged in a randomized complete block design (RCBD) with 3 replications. Variable measured was grain yield. Analysis of variance was performed for data from each study site, to determine the performance of each genotype at each location. Yield stability analysis was performed by GGE biplot method using PB tools software. Results showed that genotype H9 (HBSTK11) had the highest biological stability with grain yield of 10.37 t/ha, higer than the overall mean yield. The best hybrid with the highest yield and good stability was hybrid H6 (HBSTK08) of 11.08 t/ha. This experimental hybrid is considered potential to be released as new hybrid variety. North Minahasa is considered the most suitable location for testing, whereas Konawe and West Lombok are least suitable, compared with the other locations.</p>

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Losa, Fabio B., Rob van den Honert, and Alison Joubert. "The Multivariate Analysis Biplot as tool for conflict analysis in MCDA." Journal of Multi-Criteria Decision Analysis 10, no. 5 (September 2001): 273–84. http://dx.doi.org/10.1002/mcda.308.

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Mohammadi, Reza, Ahmed Amri, and Yousef Ansari. "Biplot Analysis of Rainfed Barley Multienvironment Trials in Iran." Agronomy Journal 101, no. 4 (July 2009): 789–96. http://dx.doi.org/10.2134/agronj2008.0203x.

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Bhan, M. K., S. Pal, B. L. Rao, A. K. Dhar, and M. S. Kang. "GGE Biplot Analysis of Oil Yield in Lemongrass (Cymbopogonspp.)." Journal of New Seeds 7, no. 2 (July 15, 2005): 127–39. http://dx.doi.org/10.1300/j153v07n02_07.

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Hron, K., M. Jelínková, P. Filzmoser, R. Kreuziger, P. Bednář, and P. Barták. "Statistical analysis of wines using a robust compositional biplot." Talanta 90 (February 2012): 46–50. http://dx.doi.org/10.1016/j.talanta.2011.12.060.

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Stojakovic, Milisav, Mile Ivanovic, Goran Bekavac, Aleksandra Nastasic, Bozana Purar, Bojan Mitrovic, and Dusan Stanisavljevic. "Evaluation of new NS maize hybrids using biplot analysis." Genetika 44, no. 1 (2012): 1–12. http://dx.doi.org/10.2298/gensr1201001s.

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The study analyzed two-year results of a testing of 20 new maize hybrids from FAO MG 600 as compared to a standard. Data on the hybrids NS6683, NS6686, NS281633, and NS396432 are discussed in the paper in greater detail. In order to study grain yield, grain moisture, root and stalk lodging, and resistance to pests and diseases, field trials using a RCB design with four replicates were conducted in six locations in 2009 and five locations in 2010. The results were presented in the form of GGE biplots in order to rank hybrids relative to the standard while taking into account the genotype x environment interaction and to identify the highest-yielding genotypes in different environments. It was determined that the new NS hybrids had higher grain yield than the standard by 0.883 to 1.720 tha-1, lower grain moisture by 0.85 to 2.54%, better tolerance to root and stalk lodging, and pest and disease resistance on a par to the standard. The study identified so-called ideal locations for particular hybrids, which may be of use when determining which areas the hybrids are best suited for.

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Bertoia, Luis, César López, and Ruggero Burak. "Biplot Analysis of Forage Combining Ability in Maize Landraces." Crop Science 46, no. 3 (May 2006): 1346–53. http://dx.doi.org/10.2135/cropsci2005.09-0336.

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Yang, Rong-Cai, Jose Crossa, Paul L. Cornelius, and Juan Burgueño. "Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution." Crop Science 49, no. 5 (September 2009): 1564–76. http://dx.doi.org/10.2135/cropsci2008.11.0665.

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Brown, Gordon S., and Timothy D. Walker. "Indicators of maturity in apricots using biplot multivariate analysis." Journal of the Science of Food and Agriculture 53, no. 3 (1990): 321–31. http://dx.doi.org/10.1002/jsfa.2740530305.

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Curcic, Zivko, Dario Danojevic, Bojan Mitrovic, Mihajlo Ciric, Ksenija Taski-Ajdukovic, and Nevena Nagl. "GGE biplot analysis of sugar beet multi-environment trials." Ratarstvo i povrtarstvo 54, no. 2 (2017): 61–67. http://dx.doi.org/10.5937/ratpov54-13241.

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Sari, Lina, and Pardomuan Robinson Sihombing. "PRINCIPAL COMPONENT ANALYSIS BIPLOT GLOBAL COMPETITIVENESS INDEX ASEAN COUNTRIES." JURNAL MATEMATIKA MURNI DAN TERAPAN EPSILON 14, no. 2 (March 2, 2021): 93. http://dx.doi.org/10.20527/epsilon.v14i2.2967.

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ASEAN's global competitiveness requires institutional and ASEAN countries appear to be a formidable economic actors in protecting the economic interests and at the same time having an open economic system that indicates the readiness of ASEAN to compete with the economic strength of the entire region in the world. In this case the measurement of global competitiveness factors become important aspects of state enterprises in the face of global competition. This study was conducted to determine how competitive the ASEAN countries with Biplot method of Principal Component Analysis. Results obtained from this study is the ASEAN countries have different advantages in each of the variables related to the global competitiveness index. In addition, the diversity of which can be explained more than 70% which is 90.69% which means that Principal Component Analysis Biplot describes well the overall total

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Journal articles on the topic 'Biplot analysis'

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