r | Brandon LeBeau

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.

Simulation and power analysis of generalized linear mixed models

June 29, 2016 slides, r, simglm, power, simulation

Overview (G)LMMs Power simglm package Demo Shiny App! 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 = .

Interactively building test forms from an IRT perspective An application of R and Shiny

February 18, 2016 r, slides, shiny, buildform

Overview 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 Reproducible Research Reproducible research has become popular. Commonly a document that contains both analysis and text. This can be done with Rmarkdown and knitr. Iterative/Interactive Data Analysis This type of analysis requires some input from the user.