- (G)LMMs
- Power
`simglm`

package- Demo Shiny App!

- 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

- 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 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*.

- 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.

```
shiny::runGitHub('simglm', username = 'lebebr01', subdir = 'inst/shiny_examples/demo')
```

or

```
devtools::install_github('lebebr01/simglm')
library(simglm)
run_shiny()
```

- Must have following packages installed:
`simglm, shiny, shinydashboard, ggplot2, lme4, DT`

.

- Twitter: @blebeau11
- Website: http://educate-r.org
- Slides: http://educate-r.org/2016/06/29/user2016.html
- GitHub: http://github.com/lebebr01