China-R-2010-Nlme-Package.pdf

Introduction to Hierarchical Data Theory Real Example NLME package in R Jiang Qi Department of Statistics Renmin University of China June 7, 2010 Jiang Qi NLME package in R Introduction to Hierarchical Data Theory Real Example The problem Grouped data, or Hierarchical data: correlations between subunits within subjects. It arises in many areas as diverse as agriculture, biology, economics, manufacturing, and geophysics. The most popular means to model Grouped data is Mixed Effect Model. Mixed Effect Model decomposes the outcome of an observation as fixed effect (population mean) and random effect (group specific), and account for the correlation structure of variations among groups. Jiang Qi NLME package in R Introduction to Hierarchical Data Theory Real Example The problem Grouped data, or Hierarchical data: correlations between subunits within subjects. It arises in many areas as diverse as agriculture, biology, economics, manufacturing, and geophysics. The most popular means to model Grouped data is Mixed Effect Model. Mixed Effect Model decomposes the outcome of an observation as fixed effect (population mean) and random effect (group specific), and account for the correlation structure of variations among groups. Jiang Qi NLME package in R Introduction to Hierarchical Data Theory Real Example The problem Grouped data, or Hierarchical data: correlations between subunits within subjects. It arises in many areas as diverse as agriculture, biology, economics, manufacturing, and geophysics. The most popular means to model Grouped data is Mixed Effect Model. Mixed Effect Model decomposes the outcome of an observation as fixed effect (population mean) and random effect (group specific), and account for the correlation structure of variations among groups. Jiang Qi NLME package in R Introduction to Hierarchical Data Theory Real Example The problem Grouped data, or Hierarchical data: correlations between subunits within subjects. It arises in many areas as diverse as ag