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Showing posts from December, 2012

Misusage of the new shiny package: A nerdy drink tracker for your next party

Currently a lot of people are talking about the new shiny package. So I got curious and built an own, more or less useful app: A drink tracker


This app can be used to track how much someone drank and therefore it is very useful for every party, especially when you plan to play some drinking games.


The usage is very simple:

Start an R sessionRun the following script (uncomment to install the packages) and change the names in the persons vectorGame should start in your default browserEvery person who had a shot / a sip beer / whatever can be chosen from the drop-down  list. If the same person has to drink again simply push "Drink again!". You can switch between the timeline and the leaderboard by clicking on the tabsHave fun!

Get the party started

Have you already used trees or random forests to model a relationship of a response and some covariates? Then you might like the condtional trees, which are implemented in the party package.

In difference to the CART (Classification and Regression Trees) algorithm, the conditional trees algorithm uses statistical hypothesis tests to determine the next split. Every variable is tested at each splitting step, if it has an association with the response. The variable with the lowest p-value is taken for the next split. This is done until the global null-hypothesis of independence of the response and all covariates can not be rejected.

Conditional trees is my subject in a university seminar this semester. Here are my slides explaining the functionality of conditional trees, which I wanted to share with you. It includes the theory and two short examples in R.


Conditional trees from Christoph Molnar