Statistical Modeling & Regression · Mixed & Multilevel Models (GLMM)

Frequentist mixed models: Computational implementation and interpretation of results

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction # Advanced Methods in R

Overview

Topics covered

  • Introduction
  • Frequentist Mixed Models in R
  • Introduction and Frequentist Foundations
  • Mathematical Formulation of the Linear Mixed Model (LMM)
  • Distribution and Structure Assumptions
  • Marginal Distribution and Interpretation

Related concepts

Main connections

  • Mixed & multilevel models (GLMM)
  • Maximum likelihood estimation
  • Variance & standard deviation
  • Linear algebra & matrices
  • Binomial distribution
  • Mean & expected value

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

yij=𝐱ij⊤𝛃+𝐳ij⊤𝐛j+εij
View LaTeX sourcey_{ij} = \mathbf{x}_{ij}^\top \boldsymbol{\beta} + \mathbf{z}_{ij}^\top \mathbf{b}_j + \varepsilon_{ij}