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Tools for Education Data in R12 days ago
Introduction | Administrative Data Functions | Manipulating and Cleaning Data | Checking and Summarizing Data | Datasets | Deprecated functions
An Introduction to merTools1 months ago
Introduction | Illustrating Model Effects | Random Effects | Substantive Effects | Uncertainty
Exploring Contextual Effects with merTools1 months ago
Contextual effects | The data | A contextual-effects model | Predictions that separate the two effects | How much do schools matter across the rank distribution? | References
Analyzing Imputed Data with Multilevel Models and merTools1 months ago
Introduction | Missing Data and its Discontents | Fitting and Summarizing a Model List | Output of a Model List | Specific Model Information Summaries | Diagnostics of List Components | Model List Generics | Cautions and Notes
Using merTools to Marginalize Over Random Effect Levels1 months ago
Marginalizing Random Effects | Summarizing | Plotting
Prediction Intervals from merMod Objects1 months ago
Introduction | Conceptual description | Comparison to existing methods | Step 1: Estimating the model and using predictInterval() | Step 1a: Adjusting for correlation between fixed and random effects | Step 2: Comparison with arm::sim() | Step 3: Comparison with lme4::bootMer() | Step 3a: lme4::bootMer() method 1 | Step 3b: lme4::bootMer() method 2 | Step 3c: lme4::bootMer() method 3 | Step 3d: Comparison to rstanarm | Computation time | Simulation
Validating predictInterval() against brms1 months ago
Why compare to brms? | A random-slopes model | Point estimates are essentially identical | The prediction intervals agree | And they are equally well calibrated | At a fraction of the cost | What about an entirely new group? | A generalized linear mixed model | Takeaways