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Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R
Ouma Victor Otieno,
Onyango Nelson Owuor
Issue:
Volume 5, Issue 2, June 2019
Pages:
22-31
Received:
6 May 2019
Accepted:
5 June 2019
Published:
26 June 2019
Abstract: Complexity of Genotype by environment interaction (GxEI) in sugarcane multi-environmental trial (MET) requires further evaluation for genotypes performance determination. Genotype and genotype by environment (GGE) is one of the many statistical techniques for evaluating the interaction with emphasis on genotypes. Many statistical analysis tools for GGE exists with usage depending on cost and knowhow. R open source analytical software ensures availability and the knowledge on the necessary packages is required thus the objective of the paper on utilization of GGE using R software in the evaluation of genotypes with presence GxEI. The application used secondary data of Kenyan Mtwapa series of 96 and 97 preliminary varietal trial stage 4 established under randomized complete block design (RCBD), consisting of 15 test genotypes and three controls in the environments of SONYsugar, Mumias and KibosF9 with the plant crop and ratoon crop cycles as seasons. The 2-way GEI data was handled using singular value decomposition (SVD) through the R package; GGEbiplot programmed scripts and graphical user interface (GUI) were used in ranking genotypes and environments, determining genotypes performance overall and in each environment, determining stabilities and adaptability of the genotypes and identifying mega trial environments. GGEbiplot unpacked the GEI through the principle components (PC) 1 and 2 that sufficiently explained 85.37% of the variations.
Abstract: Complexity of Genotype by environment interaction (GxEI) in sugarcane multi-environmental trial (MET) requires further evaluation for genotypes performance determination. Genotype and genotype by environment (GGE) is one of the many statistical techniques for evaluating the interaction with emphasis on genotypes. Many statistical analysis tools for...
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On Transmuted Four Parameters Generalized Log-Logistic Distribution
Femi Samuel Adeyinka,
Akintayo Kehinde Olapade
Issue:
Volume 5, Issue 2, June 2019
Pages:
32-37
Received:
5 May 2019
Accepted:
5 June 2019
Published:
16 July 2019
Abstract: In this article we transmute the four parameters generalized log-logistic distribution using quadratic rank transmutation map to develop a transmuted four parameters generalized log-logistic distribution. The quadratic rank transmutation map helps to introduce extra parameter into the baseline distribution to enhance more flexibility in the analysis of data in various disciplines such as reliability analysis in engineering, survival analysis, medicine, biological sciences, actuarial science, finance and insurance. The mathematical properties such as moments, quantile, mean, median, variance, skewness and kurtosis of this distribution are discussed. The reliability and hazard functions of the four parameters generalized log-logistic distribution are obtained. The probability density functions of the minimum and maximum order statistics of the four parameters generalized log-logistic distribution are established and the relationships between the probability density functions of the minimum and maximum order statistics of the parent model and the probability density functions of the four parameters generalized log-logistic distribution are considered. The parameter estimation is done by the maximum likelihood method. The flexibility of the model in statistical data analysis and its applicability is demonstrated by using it to fit relevant data. The study is concluded by demonstrating that the four parameters generalized log-logistic distribution has a better goodness of fit than its parent model. We hope this model will serve as an alternative to the existing ones in fitting positive real data.
Abstract: In this article we transmute the four parameters generalized log-logistic distribution using quadratic rank transmutation map to develop a transmuted four parameters generalized log-logistic distribution. The quadratic rank transmutation map helps to introduce extra parameter into the baseline distribution to enhance more flexibility in the analysi...
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On Bayesian Estimation of Dirichlet Process Lognormal Mixture Models and Comparison of Treatments in Censoring
Henry Ondicho Nyambega,
George Otieno Orwa
Issue:
Volume 5, Issue 2, June 2019
Pages:
38-45
Received:
13 June 2019
Accepted:
12 July 2019
Published:
30 July 2019
Abstract: One current interest in medical research is the comparison of treatments in the analysis of survival times of patients. This is particularly problematic, especially for censored data, and when these data consists of several groups, where each group has distinct properties and characteristics but belong to the same distribution. There are various modeling schemes that have been contemplated to overcome these complexities inherent in the data. One such possibility is the Bayesian approach which integrates prior knowledge in analysis. In this paper, we focus on the use of Bayesian lognormal mixture model (MLNM) with related Dirichlet process (DP) prior distribution for estimating patient survival. The advances in the Bayesian paradigm have considerably bolstered the development and application of mixture modelling methodology in the field of survival analysis. The proposed MLN model is compared with the conventional parametric lognormal and the nonparametric Kaplan Meier (K-M) models used to estimate survival to establish model robustness. A simulation study that investigates the impact of censoring on these models is also described. Real data from past research is used to show the resulting Dirichlet process mixture model’s robustness in the comparison of censored treatment. The results indicate that the proposed lognormal mixtures provide a better fit to complex data. Further, the MLN models are able to estimate various survival distributions and therefore appropriate to compare treatments. Clinicians will find these models useful especially when confronted with the obstacle of choosing a suitable therapy for a disease.
Abstract: One current interest in medical research is the comparison of treatments in the analysis of survival times of patients. This is particularly problematic, especially for censored data, and when these data consists of several groups, where each group has distinct properties and characteristics but belong to the same distribution. There are various mo...
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