Resumo -O objetivo deste trabalho foi propor uma abordagem bayesiana do método de Eberhart & Russell para avaliar a adaptabilidade e da estabilidade fenotípica de genótipos de alfafa (Medicago sativa), bem como avaliar a eficiência da utilização de distribuições a priori informativas e pouco informativas. Foram utilizados dados de um experimento em blocos ao acaso, no qual se avaliou a produção de massa de matéria seca de 92 genótipos. A metodologia bayesiana proposta foi implementada no programa livre R por meio da função MCMCregress do pacote MCMCpack. Para representar as distribuições a priori pouco informativas, utilizaram-se distribuições de probabilidade com grande variância; e, para representar distribuições a priori informativas, adotou-se o conceito de meta-análise, que se caracteriza pela utilização de informações provenientes de trabalhos anteriores. A comparação entre as distribuições a priori foi realizada por meio do fator de Bayes, com a função BayesFactor do pacote MCMCpack, que indicou a priori informativa como a mais adequada nas condições deste estudo.Termos para indexação: Medicago sativa, fator de Bayes, priori informativa, interação genótipo x ambiente, MCMC. Bayesian approach for the evaluation of adaptability and stability of alfalfa genotypesAbstract -The objective of this work was to propose a Bayesian approach for the Eberhart & Russell method to evaluate the phenotypic adaptability and stability of alfafa (Medicago sativa) genotypes, as well as to evaluate the efficiency of the use of prior informative and noninformative distributions. Data from a randomized block design experiment evaluating the forage dry weight of 92 genotypes were used. The Bayesian methodology proposed was implemented in the free software R by the MCMCregress function of the MCMCpack package. To represent the noninformative prior distributions, a probability distribution with high variance was used; and, to represent the informative prior, a meta-analysis concept was adopted, characterized by the use of information provided by previous studies. The comparison between the prior distributions was done using the Bayes Factor, with the BayesFactor function of the MCMCpack package, which indicated that an informative prior is more appropriate under the conditions of this study.
Este artigo pode ser copiado, distribuído, exibido, transmitido ou adaptado desde que citados, de forma clara e explícita, o nome da revista, a edição, o ano e as páginas nas quais o artigo foi publicado originalmente, mas sem sugerir que a RAM endosse a reutilização do artigo. Esse termo de licenciamento deve ser explicitado para os casos de reutilização ou distribuição para terceiros. Não é permitido o uso para fins comerciais.• RAM, REV. ADM. MACKENZIE, V. 13, N. 5 • SÃO PAULO, SP • SET./OUT. 2012 • ISSN 1518-6776 (impresso) • ISSN 1678-6971 (on-line) • Submissão: 19 jul. 2010. Aceitação: 1º ago. 2012. Sistema de avaliação: às cegas dupla (double blind review). UNIVERSIDADE PRESBITERIANA MACKENZIE. Walter Bataglia (Ed.), p. 101-134.
Motivation:In a microarray time series analysis, due to the large number of genes evaluated, the first step toward understanding the complex time network is the clustering of genes that share similar expression patterns over time. Up until now, the proposed methods do not point simultaneously to the temporal autocorrelation of the gene expression and the model-based clustering. We present a Bayesian method that considers jointly the fit of autoregressive panel data models and hierarchical gene clustering. Results: The proposed methodology was able to cluster genes that share similar expression over time, which was determined jointly by the estimates of autoregression parameters, by the average level of expression) and by the quality of the fitted model. Availability and implementation: The R codes for implementation of the proposed clustering method and for simulation study, as well as the real and simulated datasets, are freely accessible on the Web
Examinou-se o processo da volatilidade dos retornos de duas importantes commodities agrícolas brasileiras, o café e a soja, por meio de modelos da classe ARCH. Os resultados empíricos sugerem fortes sinais de persistência e assimetria na volatilidade de ambas as séries. Além disso, os resultados sugerem que a implementação de políticas que criem, facilitem o acesso e estimulem a utilização de instrumentos de hedging baseados no mercado podem ser estratégias adequadas para tais setores diante da persistência de choques e volatilidade pronunciadas constatadas para os retornos destas commodities
We examined the volatility process of the returns of two important Brazilian agricultural commodities, coffee and soy, using ARCH class models. Empirical results suggest strong signs of persistence and asymmetry in the volatility of both series. Furthermore, the results suggest that the design of policies that create, facilitate the access and stimulate the use of market-based hedging devices can be proper strategies for such sectors in view of the persistence of shocks and the pronounced volatility found for the returns of these commodities
Gene expression time series (GETS) analysis aims to characterize sets of genes according to their longitudinal patterns of expression. Due to the large number of genes evaluated in GETS analysis, an useful strategy to summarize biological functional processes and regulatory mechanisms is through clustering of genes that present similar expression pattern over time. Traditional cluster methods usually ignore the challenges in GETS, such as the lack of data normality and small number of temporal observations. Independent Component Analysis (ICA) is a statistical procedure that uses a transformation to convert raw time series data into sets of values of independent variables, which can be used for cluster analysis to identify sets of genes with similar temporal expression patterns. ICA allows clustering small series of distribution-free data while accounting for the dependence between subsequent time-points. Using temporal simulated and real (four libraries of two pig breeds at 21, 40, 70 and 90 days of gestation) RNA-seq data set we present a methodology (ICAclust) that jointly considers independent components analysis (ICA) and a hierarchical method for clustering GETS. We compare ICAclust results with those obtained for K-means clustering. ICAclust presented, on average, an absolute gain of 5.15% over the best K-means scenario. Considering the worst scenario for K-means, the gain was of 84.85%, when compared with the best ICAclust result. For the real data set, genes were grouped into six distinct clusters with 89, 51, 153, 67, 40, and 58 genes each, respectively. In general, it can be observed that the 6 clusters presented very distinct expression patterns. Overall, the proposed two-step clustering method (ICAclust) performed well compared to K-means, a traditional method used for cluster analysis of temporal gene expression data. In ICAclust, genes with similar expression pattern over time were clustered together.
Resumo -O objetivo deste trabalho foi utilizar o método Bayesiano no ajuste do modelo de Wood a dados de produção de leite de cabras da raça Saanen. Dois grupos de animais da primeira e segunda lactação foram considerados. Amostras das distribuições marginais a posteriori dos parâmetros do modelo de Wood e das funções de produção derivadas desses parâmetros -pico de produção, tempo do pico de produção, persistên-cia e produção total de leite -foram obtidas pelo algoritmo Gibbs Sampler. As inferências foram feitas em cada população e os resultados mostraram diferenças na taxa de decréscimo da produção após o pico e na persistên-cia, indicando maior produção nos animais de segunda lactação. Realizou-se um estudo de simulação de dados para avaliar o método Bayesiano sob diferentes estruturas de matrizes de covariâncias dos parâmetros. Os resultados desse estudo indicam que o método é eficiente no estudo das curvas de lactação quando a matriz de covariância apresenta alta correlação dos parâmetros.Termos para indexação: Gibbs Sampler, matriz de covariância, produção de leite. Bayesian approach in the lactation curve of Saanen goats from first and second calving ordersAbstract -The objective of this work was to use the Bayesian method in the fitting of the Wood´s model for milk production of Saanen goats. Two groups of animals from first and second lactation were considered in the analysis. The posterior marginal distributions for each parameter and production functions, peak milk yield, time of peak yield, persistency and total milk production, were obtained via Gibbs Sampler algorithm. The inference was done for each population. The results showed differences in the slope of the curve after the peak and in persistency, indicating highest production for the second lactation. The data were simulated for evaluating Bayesian method under several covariance matrices structures. The simulation results indicate the efficiency of this method for lactation curves studies when the covariance matrices show high correlation for parameters.
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