Simulation and power analysis of generalized linear mixed models
University of Iowa
- Demo Shiny App!
Linear Mixed Model (LMM)
- Power is the ability to statistically detect a true effect (i.e. non-zero population effect).
- For simple models (e.g. t-tests, regression) there are closed form equations for generating power.
- R has routines for these:
n <- seq(4, 1000, 2)
power <- sapply(seq_along(n), function(i)
power.t.test(n = n[i], delta = .15, sd = 1, type = 'two.sample')$power)
Power for (G)LMM
- Power for more complex models is not as straightforward;
- particularly with messy real world data.
- There is software for the GLMM models to generate power:
Power is hard
- In practice, power is hard.
- Need to make many assumptions on data that has not been collected.
- Therefore, data assumptions made for power computations will likely differ from collected sample.
- A power analysis needs to be flexible, exploratory, and well thought out.
simglm aims to simulate (G)LMMs with up to three levels of nesting (aim to add more later).
- Flexible data generation allows:
- any number of covariates and discrete covariates
- change random distribution
- unbalanced data
- missing data
- serial correlation.
- Also has routines to generate power.
Demo Shiny App
shiny::runGitHub('simglm', username = 'lebebr01', subdir = 'inst/shiny_examples/demo')
- Must have following packages installed:
simglm, shiny, shinydashboard, ggplot2, lme4, DT.