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.
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Background Began after Tim Brewster was fired at the University of Minnesota. Now they have a new coach again! Wanted to try to predict next great coach. Proceeded to explore data available to answer this question. Data Data came from a few sources: http://www.cfbdatawarehouse.com/ Wikipedia Goals Predict the ‘ability’ of the coaches. Find other variables that explain variation in the ‘ability’ of the coaches.
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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 = .
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