(Generalized) Linear Models and Mixed Models with R

Mô hình hồi quy tuyến tính & Mô hình hỗn hợp (tổng quát hóa) với R

This is a decidedly conceptual introduction to the linear model framework and linear mixed effects models in R. It is intended to be very basic.

Tutorial 1

Tutorial 2

Linear Mixed effect Models are becoming a common statistical tool for analyzing data with a multilevel structure. I will start by introducing the concept of multilevel modeling where we will see that such models are a compromise between two extreme: complete pooling and no pooling

Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when data are clustered or there are both fixed and random effects.

The purpose of these notes is to provide a quick introduction to R, particularly as a tool for fitting linear and generalized linear models.

1. Introduction

2. Getting Started

3. Reading and Examining Data

4. Linear Models

5. Generalized Linear Models

6. Conclusion

The goal of this course is to give you the skills to do the statistics that are in current
published papers, and make pretty figures to show off your results. While we will go over the mathematical concepts behind the statistics, this is NOT meant to be a classical statistics class. We will focus more on making the connection between the mathematical equation and the R code, and what types of variables fit into each slot of the equation.

Lesson 0 – Introduction and Set-up

Lesson 1 – R Basics

Lesson 2 – Linear Regression

Lesson 3 – Logistic Regression

Lesson 4 – Multiple Regression

Lesson 5 – Analysis of Variance (ANOVA)

Lesson 6, Part 1 – Linear Mixed Effects Models (Generalized linear mixed model)

Lesson 6, Part 2 – Linear Mixed Effects Models (Generalized linear mixed model)


Trả lời

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