R 로 하는 Mixed Model
들어가기
Overview
Goals
준비하기
워크샵
핵심 패키지
Mixed Models
용어
Kinds of Clustering
Random Intercepts Model
예: 학점 (GPA)
일반적인 회귀 모형
The Mixed Model
첫모델
Multi-level model
적용
시각화
표준 회귀
Regression by cluster
Running a mixed model
Cluster Level Covariates
Mixed Model 기초사항의 요약
연습문제
수면
클러스터 레벨 공변량(covariate) 추가하기
mixed model 시뮬레이트하기
추가 Random Effect
적용
개별 회귀모델과 비교
효과 시각화
요약
Exercises for Random Slopes
확장모델
추가 군집 구조
Cross-classified models
계층구조
Crossed vs. nested
잔차 구조
이종분산
Autocorrelation
일반화 선형 Mixed Model
Exercises for Extensions
Sociometric data
Patents
이슈사항
Variance Accounted For
Common Alternatives to Mixed Models
성장곡선모델
Sample Sizes
Small number of clusters
Small number of observations within clusters
Balanced/Missing values
Big data
Model Comparison
Convergence
베이지언 방법
Priors
Fixed 효과
Variance components
Demonstration
예제 모델
모델 외
심화
Other Distributions
Other Contexts
비선형 Mixed Effects Models
Connections
Summary
Supplemental
A Comparison to Latent Growth Curve Models
Random effects as latent variables
Random effects in SEM
Running a growth curve model
Random intercepts
Random intercepts and slopes
Random effects with heterogeneous variances
Other covariates
Some differences between mixed models and growth curves
Recommended packages that can do growth curve models
Summary of LGC
Correlation Structure Revisited
Summary of residual correlation structure
Appendix
Data
Programming languages
R
Python
Julia
Proprietary
Reference texts and other stuff
R 로 하는 Mixed Model
R 로 하는 Mixed Model
랜덤효과 시작하기
Michael Clark
m-clark.github.io
Translator :
김설기