90 likes | 228 Views
Psychology 290 Special Topics Study Course: Advanced Meta-analysis. February 12, 2014. Overview. Debugging strategies. Optimizing likelihood surfaces using R ’s optimizer. Mixed-effects models. Maximum likelihood estimation of the variance component.
E N D
Psychology 290Special Topics Study Course: Advanced Meta-analysis February 12, 2014
Overview • Debugging strategies. • Optimizing likelihood surfaces using R’s optimizer. • Mixed-effects models.
Maximum likelihood estimation of the variance component • The historical reasons for seeking closed-form solutions no longer apply. • Estimation by maximum likelihood. • Optimizing likelihood surfaces. • Using maximum likelihood to estimate random-effects models. • (Long digression in R.)
The models, more formally • So far, we have been informal about exactly what we are doing when we estimate a random-effects model. • FE:
The random-effects model • RE:
Adding moderators • FE: • Or, more compactly,
Adding moderators (cont.) • RE: • In other words, the mixed-effects model is a random-intercept regression.
Estimating these models • The task of estimating fixed-effects models with moderators or mixed-effects models is simply a matter of replacing the mean in the likelihood with the conditional mean given the predictors. • Digression in R: • revisiting the gender differences data set; • Smith, Glass & Miller phobia data.
Next time • Restricted maximum likelihood. • Starting to think about simulation.