## Linear mixed model fit by maximum likelihood ['lmerMod']### AIC BIC logLik deviance df.resid## 116.3 125.1 -52.1 104.3 26### Scaled residuals:## Min 1Q Median 3Q Max## -1.7501 -0.6725 -0.1219 0.6223 1.7882### Random effects:## Groups Name Variance Std.Dev.## site (Intercept) ...
Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, Volumes I and II by Sophia Rabe-Hesketh and Anders Skrondal In the spotlight: meglm In the spotlight: Nonlinear multilevel mixed-effects models Multilevel/mixed models using Stata training course Multilevel mixed-effects models flyer...
## Linear mixed model fit by maximum likelihood ['lmerMod'] ## ## AIC BIC logLik deviance df.resid ## 116.3 125.1 -52.1 104.3 26 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -1.7501 -0.6725 -0.1219 0.6223 1.7882 ## ## Random effects: ## Groups Name Variance Std.Dev. ...
The multilevel/mixed-effects model (MLMM) withmixed Theory and intuition Random-intercept models Random-coefficient (random-slope) models Estimation methods: maximum likelihood, restricted maximum likelihood, generalized least squares, and small-sample inference ...
R语言混合效应逻辑回归(mixed effects logistic)模型分析肺癌数据 R语言如何用潜类别混合效应模型(LCMM)分析抑郁症状 R语言基于copula的贝叶斯分层混合模型的诊断准确性研究 R语言建立和可视化混合效应模型mixed effect model R语言LME4混合效应模型研究教师的受欢迎程度 ...
model分析藻类数据实例R语言混合线性模型、多层次模型、回归模型分析学生平均成绩GPA和可视化R语言线性混合效应模型(固定效应&随机效应)和交互可视化3案例R语言用lme4多层次(混合效应)广义线性模型(GLM),逻辑回归分析教育留级调查数据R语言 线性混合效应模型实战案例R语言混合效应逻辑回归(mixed effects logistic)模型分析...
5.R语言线性混合效应模型实战案例 6.线性混合效应模型Linear Mixed-Effects Models的部分折叠Gibbs采样 7.R语言LME4混合效应模型研究教师的受欢迎程度 8.R语言中基于混合数据抽样(MIDAS)回归的HAR-RV模型预测GDP增长 9.使用SAS,Stata,HLM,R,SPSS和Mplus的分层线性模型HLM...
## lmListsummary(PQL)## Linear mixed-effects model fit by maximum likelihood## Data: recog## AIC BIC logLik## NA NA NA### Random effects:## Formula: ~1 | Observer## (Intercept)## StdDev: 0.3312### Formula: ~1 | Test.ID %in% Observer## (Intercept) Residual## StdDev: 0.5295 7.12...
5.R语言线性混合效应模型实战案例 6.线性混合效应模型Linear Mixed-Effects Models的部分折叠Gibbs采样 7.R语言LME4混合效应模型研究教师的受欢迎程度 8.R语言中基于混合数据抽样(MIDAS)回归的HAR-RV模型预测GDP增长 9.使用SAS,Stata,HLM,R,SPSS和Mplus的分层线性模型HLM...
## Fixed effects: ## Estimate Std. Error t value ## (Intercept) 10.1272 0.2670 37.929 ## c.urchinden 0.5414 0.2303 2.351 ## c.fishmass 0.4624 0.4090 1.130 ## c.axD 0.3989 0.4286 0.931 ## ## Correlation of Fixed Effects: ## (Intr) c.rchn c.fshm ...