package | Brandon LeBeau

Simulation and Power Analysis of Generalized Linear Mixed Models

August 2, 2017 slides, r, simglm, package

Overview Power simglm package Shiny Demo 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 n <- seq(20, 1000, 5) power <- sapply(seq_along(n), function(i) power.t.test(n = n[i], delta = .15, sd = 1, type = 'two.

Make Power Fun (Again?)

February 24, 2017 slides, r, simglm, package

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. 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 n <- seq(4, 1000, 2) power <- sapply(seq_along(n), function(i) power.t.test(n = n[i], delta = .

Extending accessibility of open-source statistical software to the masses A shiny case study

October 7, 2016 slides, R, shiny, package

R R is an open source statistical programming language. Pros: Common statistical procedures are found in R Can extend functionality with packages/functions Cons: Need to be comfortable with code Flexibility of R R is powerful and flexible due to the many user written packages. However, to capture this flexibility: users need to be comfortable with programming users need to find the package users need to understand package specific syntax R package documentation and examples https://www.