• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.393-399
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• 2001

자료행렬에서의 개체와 변수간의 관계 또는 분할표 자료에서의 열 범주와 행 범주간의 밀접도를 표준화된 자료행렬에 대한 요인행렬도에서 개체(행)와 변수(열)에 해당하는 두 벡터의 사이각의 코사인으로 정의하였다. 본 논문에서 정의한 개체와 변수간의 밀접도를 15대 및 16대 국회의원 선거자료에 적용하여 보았다.

• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.129-136
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• 2013

Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.407-418
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• 1996

Paired rankings data comes to us in two situations. One situation is when pairs of subjects, say husbands and wives, are asked to rank a group of objects. Another situation is when subjects are asked to rank a group of same objects at two time points, say, before and aster the treatment. In this study, we show how biplot techniques can be applied to represent graphically such paired rankings.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.439-451
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• 1995

Ranked data are widely used in the area of social sciences, for instance in polls and preference surveys, in which a number of objects (or stimuli) are evaluted and ranked by a panel of judges (or subject) according to their preference. We propose a graphical method for ranked data by quantifying objects and judges. In a plot for judges, the interpoint distances can be interpreted as Spearman or Kendall distance between two rankings given by respective judges. Similarly, we also construct a plot for objects with a sensible relationship to the previous plot for judges.

• The Plant Pathology Journal
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• v.24 no.3
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• pp.337-351
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• 2008

Host-pathogen interaction in rice bacterial blight pathosystem was analyzed for a better understanding of their relationship and recognition of stable pathogenicity among the populations of Xanthomonas oryzae pv. oryzae. A total number of 52 bacterial strains isolated from diseased leaf samples collected from 12 rice growing states and one Union Territory of India, were inoculated on 16 rice varieties, each possessing known genes for resistance. Analysis of variance revealed that the host genotypes(G) accounted for largest(78.4%) proportion of the total sum of squares(SS), followed by 16.5% due to the pathogen isolates(I) and 5.1% due to the $I{\times}G$ interactions. Application of the Additive Main effects and Multiplicative Interaction(AMMI) model revealed that the first two interaction principal component axes(IPCA) accounted for 66.8% and 21.5% of the interaction SS, respectively. The biplot generated using the isolate and genotypic scores of the first two IPCAs revealed groups of host genotypes and pathogen isolates falling into four sectors. A group of five isolates with high virulence, high absolute IPCA-1 scores, moderate IPCA-2 scores, low AMMI stability index '$D_i$' values and minimal deviations from additive main effects displayed in AMMI biplot as well as response plot, were identified as possessing stable pathogenicity across 16 host genotypes. The largest group of 27 isolates with low virulence, small IPCA-1 as well as IPCA-2 scores, low $D_i$ values and minimal deviations from additive main effect predictions, possessed stable pathogenicity for low virulence. The AMMI analysis and biplot display facilitated in a better understanding of the host-pathogen interaction, adaptability of pathogen isolates to specific host genotypes, identification of isolates showing stable pathogenicity and most discriminating host genotypes, which could be useful in location specific breeding programs aiming at deployment of resistant host genotypes in bacterial blight disease control strategies.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.3
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• pp.349-354
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• 2015

The AMMI (additive main effects and multiplicative interaction) and GGE (genotype main effect and genotype by environment interaction) biplot which were accounted for a substantial part of total sum of square in the analysis of variance suggested to be more appropriate models for explaining G $\times$ E interaction. The grain yield of total ten sesame genotypes was significantly affected by environment which explained 61% of total variation, whereas genotype and genotype x environment interaction (G $\times$ E) were explained 16%, 24% respectively. From the results of experiment, three genotypes Miryang49, Koppoom and Ansan were unstable, whereas other three genotypes Kyeongbuk18, Miryang50 and Kanghuk which were shorter projections to AEA ordinate were relatively stable over the environments. Yangbak which was closeness to the mean yield and short projection of the genotype marker lines was regarded as genotype indicating good performance with stability. Ansan, Miryang48 and Yangbaek showed the best performance in the environments of Naju, Suwon, Iksan and Andong. Similarly, genotype Miyrang47 exhibited the best performance in the environments of Chuncheon and Miryang. Andong is the closest to the ideal environment, and therefore, is the most desirable among eight environments.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.555-566
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• 2017

For multivariate datasets with large number of variables, classical dimensional reduction methods such as principal component analysis may not be effective for data visualization. The underlying reason is that the dimensionality of the space of variables is often larger than two or three, while the visualization to the human eye is most effective with two or three dimensions. This paper proposes a working procedure which first partitions the variables into several "latent" clusters, explores individual data subsets, and finally integrates findings. We use R pakacage "ClustOfVar" for partitioning variables around latent dimensions and the principal component biplot method to visualize within-cluster patterns. Additionally, we use the technique for embedding supplementary variables to figure out the relationships between within-cluster variables and outside variables.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.637-644
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• 2011

Partially ranked data refers to the situation in which there are p distinct objects; however each judge specifies only first s (s < p) choices. The group theoretic formulation for partially ranked data analysis was set up by Critchlow (1985). We propose a graphical method for partially ranked data by quantifying objects and judges. In a plot for judges, the interpoint distances can be interpreted as Spearman or Kendall distances between two rankings given by respective judges. Similarly, we also construct a plot for objects with a sensible relationship to the previous plot for judges. This study extends the Han and Huh (1995) quantification method of fully ranked data using Gabriel's (1971) biplot technique for multivariate data matrix.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.10a
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• pp.257-257
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• 2017

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.65-76
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• 2008

Biplots are the multivariate analogue of scatter plots. They approximate the multivariate distribution of a sample in a few dimensions, typically two, and they superimpose on this display representations of the variables on which the samples are measured(Gower and Hand, 1996, Chapter 1). And the relationships between the observations and variables can be easily seen. Thus, biplots are useful for giving a graphical description of the data. However, this method does not give some concise interpretations between variables and observations when the number of observations are large. Therefore, in this study, we will suggest to interpret the biplot analysis by applying the K-means clustering analysis. It shows that the relationships between the clusters and variables can be easily interpreted. So, this method is more useful for giving a graphical description of the data than using raw data.