GEM² - GRUPO DE ESTUDO EM MODELOS MISTOS

Geral

1. Estudar modelos misto em uma abordagem teórica e computacional

Especificos, Abordodagem em:

1. Experimentação Agropecuária
2. Genética
3. Modelagem Ambiental

• Paulo Justiniano Ribeiro Jr
• Silvia Emiko Shimakura
• Elias Teixeira Krainski
• Wagner Hugo Bonat
• Walmes Marques Zeviani
• Fernanda Bührer Rizzato
• Cesar Taconeli
• Eder David Borges da Silva
• Renato Gonçalves de Oliveira

• Random Effects - Faraway
• REML e ML Comparação entre verossimilhança (ML) e verossimilhança residual (REML) em modelos mistos
• Scritps Extending the Linear Model with R
• Random Effects Material Atualizado do livro
• PQL para dados Poisson com efeito aleatório individual não estruturado (Silvia)

• The Estimation of Environmental and Genetic Trends from Records Subject to Culling
• Simulation of Examine Distributions of Estimators of Variances and Ratios of Variances
• Estimation of Variance and Covariance Components
• C. R. Henderson: Contributions to Predicting Genetic Merit
• Rapid Method for Computing the Inverse of a Relationship Matrix
• A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values
• Best Linear Unbiased Estimation and Prediction under a Selection Model
• Variance-Covariance Matrix of Estimators of Variances in Unweighted Means ANOVA
• A Comparison of Variance Component Estimators

• John Fox - Introduction to Mixed-Effects Models for Hierarchical and Longitudinal Data (I);
• John Fox - Introduction to Mixed-Effects Models for Hierarchical and Longitudinal Data (II);
• Claudia Czado - Lecture Notes on Linear Mixed Models;
• R. D. Murison - Mixed Models Lecture Notes;
• Søren Højsgaard & Erik Jørgensen - Lecture Slides on Mixed Models;
• Jason Fridley - Lecture notes from Bio793: Plant Ecology/Niche modeling/Modeling in R;
• Mixed-Effect Models and Longitudinal Data Analysis -- Lecture Notes;
• Elizabeth Ann Housworth - Lecture Notes for M761: Topics in Applied Statistics;
• Roger Levy - Linguistics 251: Probabilistic Methods in Linguistics;
• Dan Nettleton - Statistics 511 Lecture Notes;
• Peter D. Hoff - CSSS-Statistics 560 Lecture Notes;
• Lauri Mehtätalo - Forest biometrics with examples in R;
• Tim Hanson - Stat 771: Longitudinal Data Analysis;
• Lectures notes on generalized mixed models;
• yeroon.net/lme4 R Web Application;
• Douglas Bates - Mixed models in R using the lme4 package;
• Douglas Bates - Fitting Linear Mixed-Effects Models Using the lme4 Package in R;
• Douglas Bates - Generalized Linear and Nonlinear Mixed-Effects Models;
• Douglas Bates Tour em Amsterdam(2011), Madson(2011), Gaithersburg(2010), Seewiesen(2009), Rennes(2009), Lausanne(2009);
• John Maindonald - The Anatomy of a Mixed Model Analysis, with R's lme4 Package;
• [R] GLMM (lme4) vs. glmmPQL output (summary with lme4 revised);
• Los Angeles area R users group (data analysis/statistics);
• Hugo Quené - Mixed-effects modeling with crossed random effects in R;
• Hugo Quené - Mixed-Effects Modeling Multilevel Modeling;
• Using lmer and nlme at the same time;
• Brief Introduction to LINEAR MIXED MODELS in R;
• Research and Statistical Support;
• Linear Mixed Models Appendix to An R and S-PLUS Companion to Applied Regression - John Fox;
• Likelihood Ratio Tests in Linear Mixed Models with One Variance Component - Ciprian M. Crainiceanu and David Ruppert;
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models - Charles E. McCulloch;
• Generalized Maximum Likelihood Method in Linear Mixed Models with an Application in Small-Area Estimation - P.Lahiri and Huilin Li;
• CONDITIONAL LIKELIHOOD INFERENCE IN GENERALIZED LINEAR MIXED MODELS N. Sartori and T. A. Severini;
• Mixed models in R using the lme4 package Part 6: Nonlinear mixed models Douglas Bates;
• Likelihood Inference for Models with Unobservables: Another View - Youngjo Lee and John A. Nelder;
• Asymptotic Properties of Maximum Likelihood Estimates in the Mixed Model of the Analysis of Variance;
• A direct derivation of the REML likelihood function;
• Topics in Mixed Effects Models - José Carlos Pinheiro;
• A CLASSICAL APPROACH FOR THE ANALYSIS OF GENERALIZED LINEAR MIXED MODELS - MJ Hammujuddy;
• Marginal maximum likelihood estimation of variance components in Poisson mixed models using Laplacian integration;
• Longitudinal data, mixed models and incomplete data - Geert;
• Thomas Kneib - Restricted maximum likelihood estimation of variance components in generalized linear mixed models;
• Fit generalized linear mixed models (GLMM), using of different likelihoods;
• Modelos Lineares - João Gil de Luna;
• Generalized Linear Mixed Models (illustrated with R on Bresnan et al.’s datives data);

• Em preto: conteúdo abordado no encontro;
• Em verde: conteúdo previsto para o próximo encontro;

—-

• (17/06/2011)
  • WB: disponibilizar os scripts apresentadas em 17/06;
  • WZ: preparar documento com a distribuição marginal em LMM;
  • PJ & WZ: preparar documento com a derivação e implementação (didática) do método REML;
• (01/07/2011)
  • WZ: documentar os procedimentos de estimação empregando métodos numéricos para a integração dos efeitos aleatórios escritos por WB, rodar glmm.PQL(), glmer();
  • SS: implementar o PQL;
  • EB: implementar o REML;
  • PJ: procedimentos para predição dos efeitos aleatórios;

[200?, book]

Magalhães, M. N., & Lima, A. C. P. (200?). Noções de Probabilidade e Estatística (1 ed.) Edusp.

[2009, techreport | www]

Ribeiro Júnior, P. J. (2009). Introdução ao Ambiente Estatístico R.

[2002, book | www]

Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S Birkhäuser.

[2009, book | www]

Everitt, B. S., & Hothorn, T. (2009). A Handbook of Statistical Analyses Using R, Second Edition (2 ed.) Chapman \& Hall.

[2008, book | www]

Dalgaard, P. (2008). Introductory Statistics with R (2nd ed.) Springer.

[2008, book | www]

Sarkar, D. (2008). Lattice: Multivariate Data Visualization with R (1 ed.) Springer.

1. The R project for Statistical Computing: página do programa R
   1. nlme Linear and Nonlinear Mixed Effects Models
2. Uma página interessante com um introdução ao R
3. O Xemacs é uma outra opção de editor que facilita a edição de arquivos do e R e disponível para plataformas Linux e Windows.
4. R-br é a lista de discussão em português sobre o uso do R