(This article was first published on EducateR  R, and kindly contributed to Rbloggers)
Make Power Fun (Again?)
Brandon LeBeau
University of Iowa
Overview
 (G)LMMs
 Power
simglm
package Shiny Demo – Broken!
Linear Mixed Model (LMM)
Power
 Power is the ability to statistically detect a true effect (i.e. nonzero population effect).
 For simple models (e.g. ttests, 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:
Power Example
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 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.
Power is Fun?
 Three common reasons to do power analysis:
 Power evidence for grant/planning
 Post Hoc to explore insignificant results
 Monte Carlo studies
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 distribution of continuous covariates
 change random distribution
 unbalanced data
 missing data
 serial correlation
Power with simglm
 Power with
simglm
takes on a Monte Carlo approach This can provide a more thorough analysis/understanding of power.
 Always outputs a data frame
 Useful for plotting
 Data manipulation
 etc.
 Serves as a wrapper around data generation process.
Power Analysis with simglm

Factorial Design:
 Idenfity factors that influences power
 Determine number of replications
 Explore results

Future Development
 Add ability for data generation and power model to differ
 Add ability for data generation and power model to differ
Simple Example
 Suppose we wished to generate data for a simple logistic regression.
library(simglm)
fixed < ~ 1 + act + diff
fixed_param < c(0.1, 0.5, 0.3)
cov_param < list(dist_fun = c('rnorm', 'rnorm'),
var_type = c("single", "single"),
opts = list(list(mean = 0, sd = 2),
list(mean = 0, sd = 4)))
n < 50
temp_single < sim_glm(fixed = fixed, fixed_param = fixed_param,
cov_param = cov_param,
n = n, data_str = "single")
Output
head(temp_single)
## X.Intercept. act diff Fbeta logistic sim_data ID
## 1 1 0.02913722 0.4430546 0.04748497 0.4881310 1 1
## 2 1 0.66199364 2.1443743 1.07430910 0.7454155 1 2
## 3 1 1.44621026 1.1909231 0.46582819 0.6143959 0 3
## 4 1 0.26011629 3.4395304 1.00180096 0.7314125 0 4
## 5 1 0.09984213 0.8485436 0.30464201 0.5755769 1 5
## 6 1 2.72704127 3.3246515 0.26612517 0.4338586 0 6
Simple Power Analysis
 Suppose we wish to use the same generating model for a power analysis
pow_param < c('(Intercept)', 'act', 'diff')
alpha < .01
pow_dist < "z"
pow_tail < 2
replicates < 100
power_out < sim_pow_glm(fixed = fixed, fixed_param = fixed_param,
cov_param = cov_param,
n = n, data_str = "single",
pow_param = pow_param, alpha = alpha,
pow_dist = pow_dist, pow_tail = pow_tail,
replicates = replicates)
Output
power_out
## # A tibble: 3 × 6
## var avg_test_stat sd_test_stat power num_reject num_repl
##
## 1 (Intercept) 0.878713 0.6709319 0.01 1 100
## 2 act 2.342617 0.5777646 0.34 34 100
## 3 diff 2.609432 0.5506204 0.56 56 100
Varying Arguments
 Now suppose we wish to vary the following arguments:
 Vary n – 50 vs 150
 vary effect size on diff – .3 vs .45
terms_vary < list(n = c(50, 150),
fixed_param = list(c(0.1, 0.5, 0.3),
c(0.1, 0.5, 0.45)))
power_out < sim_pow_glm(fixed = fixed, fixed_param = fixed_param,
cov_param = cov_param,
n = n, data_str = "single",
pow_param = pow_param, alpha = alpha,
pow_dist = pow_dist, pow_tail = pow_tail,
replicates = replicates,
terms_vary = terms_vary)
Output
power_out
## Source: local data frame [12 x 8]
## Groups: var, n [?]
##
## var n fixed_param avg_test_stat sd_test_stat power
##
## 1 (Intercept) 50 0.1,0.5,0.3 0.7778328 0.5863240 0.00
## 2 (Intercept) 50 0.1,0.5,0.45 0.8364212 0.6377631 0.01
## 3 (Intercept) 150 0.1,0.5,0.3 0.8629973 0.5814426 0.00
## 4 (Intercept) 150 0.1,0.5,0.45 0.9183353 0.6879182 0.01
## 5 act 50 0.1,0.5,0.3 2.4246997 0.6222346 0.44
## 6 act 50 0.1,0.5,0.45 2.2247451 0.6688308 0.34
## 7 act 150 0.1,0.5,0.3 4.3196568 0.6233962 0.99
## 8 act 150 0.1,0.5,0.45 3.9515646 0.6332452 0.97
## 9 diff 50 0.1,0.5,0.3 2.7887204 0.4892985 0.73
## 10 diff 50 0.1,0.5,0.45 3.0747886 0.3988745 0.89
## 11 diff 150 0.1,0.5,0.3 4.7892881 0.5025082 1.00
## 12 diff 150 0.1,0.5,0.45 5.6060130 0.2823105 1.00
## # ... with 2 more variables: num_reject , num_repl
Move to Mixed Models
 It is simple to move from single level to multilevel or mixed models.
fixed < ~1 + time + diff + act + time:act
random < ~1 + time
fixed_param < c(0, 0.2, 0.1, 0.3, 0.05)
random_param < list(random_var = c(3, 2), rand_gen = "rnorm")
cov_param < list(dist_fun = c('rnorm', 'rnorm'),
var_type = c("lvl1", "lvl2"),
opts = list(list(mean = 0, sd = 3),
list(mean = 0, sd = 2)))
n < 50
p < 6
data_str < "long"
temp_long < sim_glm(fixed = fixed, random = random, fixed_param = fixed_param,
random_param = random_param, cov_param = cov_param,
n = n, p = p, k = NULL, data_str = data_str)
Output
head(temp_long)
## X.Intercept. time diff act time.act b0 b1
## 1 1 0 6.76572749 0.3932853 0.0000000 1.947485 2.295427
## 2 1 1 0.15530420 0.3932853 0.3932853 1.947485 2.295427
## 3 1 2 0.07605058 0.3932853 0.7865707 1.947485 2.295427
## 4 1 3 1.11192544 0.3932853 1.1798560 1.947485 2.295427
## 5 1 4 4.17141062 0.3932853 1.5731413 1.947485 2.295427
## 6 1 5 4.77024867 0.3932853 1.9664267 1.947485 2.295427
## Fbeta randEff logistic prob sim_data withinID clustID
## 1 0.79455835 1.947485 2.742044 6.053757e02 0 1 1
## 2 0.07788055 4.242913 4.165032 1.529175e02 0 2 1
## 3 0.25029093 6.538340 6.288049 1.854935e03 0 3 1
## 4 0.31182906 8.833767 8.521938 1.990136e04 0 4 1
## 5 0.18621627 11.129195 10.942978 1.768142e05 0 5 1
## 6 1.26071793 13.424622 12.163904 5.215325e06 0 6 1
Doing Power
 Power is also easily extended.
pow_param < c('time', 'diff', 'act')
alpha < .01
pow_dist < "z"
pow_tail < 2
replicates < 20
power_out < sim_pow_glm(fixed = fixed, random = random,
fixed_param = fixed_param,
random_param = random_param, cov_param = cov_param,
k = NULL, n = n, p = p,
data_str = data_str, unbal = FALSE, pow_param = pow_param,
alpha = alpha, pow_dist = pow_dist, pow_tail = pow_tail,
replicates = replicates)
Output
power_out
## # A tibble: 3 × 6
## var avg_test_stat sd_test_stat power num_reject num_repl
##
## 1 act 12.06197 46.70227 0.20 4 20
## 2 diff 11.89673 45.13827 0.25 5 20
## 3 time 18.78877 79.36869 0.05 1 20
Vary Arguments
 Perhaps our effect size estimate is conservative.
terms_vary < list(fixed_param = list(c(0, 0.2, 0.1, 0.3, 0.05),
c(0, 0.2, 0.3, 0.3, 0.05)))
power_out < sim_pow_glm(fixed = fixed, random = random,
fixed_param = fixed_param,
random_param = random_param, cov_param = cov_param,
k = NULL, n = n, p = p,
data_str = data_str, unbal = FALSE, pow_param = pow_param,
alpha = alpha, pow_dist = pow_dist, pow_tail = pow_tail,
replicates = replicates,
terms_vary = terms_vary)
Output
power_out
## Source: local data frame [6 x 7]
## Groups: var [?]
##
## var fixed_param avg_test_stat sd_test_stat power num_reject
##
## 1 act 0,0.2,0.1,0.3,0.05 1.1914255 0.8114762 0.10 2
## 2 act 0,0.2,0.3,0.3,0.05 22.9059014 96.3531136 0.15 3
## 3 diff 0,0.2,0.1,0.3,0.05 1.3071639 0.8681348 0.05 1
## 4 diff 0,0.2,0.3,0.3,0.05 17.4774138 62.2814403 0.95 19
## 5 time 0,0.2,0.1,0.3,0.05 0.9281452 0.7670600 0.05 1
## 6 time 0,0.2,0.3,0.3,0.05 12.1678311 49.9607401 0.05 1
## # ... with 1 more variables: num_repl
Shiny App
 Note: This app currently looks nice, but is utterly broken!
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
.
simglm
timeline
 Aim to have this package submitted to CRAN by the end of March.
 Fix Shiny application.
 For now look for the package on GitHub http://github.com/lebebr01/simglm
Questions?
 Twitter: @blebeau11
 Website: http://educater.org
 Slides: http://educater.org/2017/02/24/csp2017.html
 GitHub: http://github.com/lebebr01/simglm
To leave a comment for the author, please follow the link and comment on their blog: EducateR  R.
Rbloggers.com offers daily email updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...