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| Name | 12. Practical considerations – Negative Variance Components |
| Video Uploader | Video From The Roslin Institute - Training |
| Upload Date | This Video Uploaded At 10-05-2016 12:07:05 |
| Video Discription | Introduction to Mixed Models Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, March 2016. ************************************************ 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. ************************************************ *Recommended YouTube playback settings for the best viewing experience: 1080p HD ************************************************ Content: Practical considerations - Zero variance component estimates - Significance testing - Model checking - Sample size estimation - Explaining a mixed model Negative Variance Components - Estimation method allows negative estimates of variance components - BUT random effects are assumed normally distributed --- Negative variances are not permissible --- Likely to be an underestimate of true VC which is small or zero - Greater chance of negative VC by chance when :- --- Ratio of true VC to residual is small --- Small number of random effect categories, eg few biological replicates --- Small number of observations per random effect category, eg few technical replicates What to do when a Variance Component is Negative? A. Fix variance component at zero --- Packages often do this by default B. Remove random effect from model --- Same fixed effect estimates result from A and B but different DFs cause differences in significance tests C. Define model in terms of correlation parameters --- Correlations may be negative A Negative Variance Component Occasionally Indicates Negative Correlation - Eg Animal feeding experiment --- Animals grouped in cages, weight measured --- Greediest animals in cage get most food --- Cage effects fitted as random gives negative cage variance component - To model negative correlation within cages redefine model in terms of correlated error terms in R matrix and omit cage as random Variance matrix for a covariance pattern model - Variance matrix for model with cages fitted as random: - Redefined in terms of correlation (?) between animals --- no random effects --- correlation allowed between animals in same cage [QBvzjdMRo8w] |
| Category | Education |
| Tags | data | statistics | data analysis | Statistical Modelling | bioinformatics | The Roslin Institute | Roslin | University of Edinburgh |
