Introduction to Mixed Models
Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, March 2016.
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These training sessions were given to staff and research students at the Roslin Institute. The material is also used for the Animal Biosciences MSc course taught at the Institute.
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*Recommended YouTube playback settings for the best viewing experience: 1080p HD
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Content:
Model Assumptions
- Residuals and random effects should be:
--- Normally distributed
--- Uncorrelated
- Consequences of violating assumptions not fully known
- Model checking methods not developed in depth
Simple Visual Checks
- Detect important deviations from assumptions
- Carry out for residuals and random effects
1. Residual plots
--- Plot residuals (or random effects) against their predicted values
--- Rough check of normality
--- Check whether variance is constant across range of values
--- Spot outliers
2. Normal probability plots
--- Plot of residuals (or random effects) against their predicted values given their ranks
--- Further check of normality
3. Compare residual (or random effect) variance between groups, eg treatments
--- Check homogeneity of variances
Residual plots and statistics (SAS output)
Checking Normality of Centre Effects
