<h1>Simulation and power analysis of generalized linear mixed models</h1> <h2>Brandon LeBeau</h2> <h3>University of Iowa</h3> # Overview 1. (G)LMMs 2. Power 3. `simglm` package 4. Demo Shiny App! # Linear Mixed Model (LMM) ![](/figs/equations.png) # Power - 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: `power.t.test, power.anova.test` + Gpower3 # Power Example ```r 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) ``` ![](/figs/power_plot-1.png) # 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: + Optimal Design: <http://hlmsoft.net/od/> + MLPowSim: <http://www.bristol.ac.uk/cmm/software/mlpowsim/> + Snijders, *Power and Sample Size in Multilevel Linear Models*. # 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` Overview - `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 ```r shiny::runGitHub('simglm', username = 'lebebr01', subdir = 'inst/shiny_examples/demo') ``` or ```r devtools::install_github('lebebr01/simglm') library(simglm) run_shiny() ``` - Must have following packages installed: `simglm, shiny, shinydashboard, ggplot2, lme4, DT`. # Questions? - Twitter: @blebeau11 - Website: <http://brandonlebeau.org> - Slides: <http://brandonlebeau.org/2016/06/29/user2016.html> - GitHub: <http://github.com/lebebr01>