Thinking about different ways to analyze sub-groups in an RCT

Here’s the scenario: we have an intervention that we think will improve outcomes for a particular population. Furthermore, there are two sub-groups (let’s say defined by which of two medical conditions each person in the population has) and we are interested in knowing if the intervention effect is different for each sub-group. And here’s the question: what is the ideal way to set up a study so that we can assess (1) the intervention effects on the group as a whole, but also (2) the sub-group specific intervention effects? This is a pretty straightforward, text-book scenario. Sub-group analysis is common in many areas of research, including health services research where I do most of my work. It is definitely an advantage to know ahead of time if you want to do a sub-group analysis, as you would in designing a…
Original Post: Thinking about different ways to analyze sub-groups in an RCT

Recent R Data Packages

It has never been easier to access data from R. Not only does there seem to be a constant stream of new packages that access the APIs of data providers, but it is also becoming popular for package authors to wrap up fairly large datasets into R packages. Below are 44 R packages concerned with data in one way or another that have made it to CRAN over the past two months. alphavantager v0.1.0: Implements an interface to the Alpha Vantage API to fetch historical data on stocks, physical currencies, and digital/crypto currencies. See the README to get the key. AmesHousing v0.0.2: Contains raw and processed versions of the Ames Iowa Housing Data. See De Cock (2011). billboard v0.1.0: Contains data sets regarding songs on the Billboard Hot 100 list from 1960 to 2016, including ranks for the given year,…
Original Post: Recent R Data Packages

An example of how to use the new R promises package

The long awaited promises will be released soon! Being as impatient as I am when it comes to new technology, I decided to play with currently available implementation of promises that Joe Cheng shared and presented recently in London at EARL conference. From this article you’ll get to know the upcoming promises package, how to use it and how it is different from the already existing future package. Promises/Futures are a concept used in almost every major programming language. We’ve used Tasks in C#, Futures in Scala, Promises in Javascript and they all adhere to a common understanding of what a promise is. If you are not familiar with the concept of Promises, asynchronous tasks or Futures, I advise you to take a longer moment and dive into the topic. If you’d like to dive deeper and achieve a higher…
Original Post: An example of how to use the new R promises package

2017 Beijing Workshop on Forecasting

Later this month I’m speaking at the 2017 Beijing Workshop on Forecasting, to be held on Saturday 18 November at the Central University of Finance and Economics.I’m giving four talks as part of the workshop. Other speakers are Junni Zhang, Lei Song, Hui Bu, Feng Li and Yanfei Kang.Full program details are available online. Related To leave a comment for the author, please follow the link and comment on their blog: R on Rob J Hyndman. R-bloggers.com offers daily e-mail 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… If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook…
Original Post: 2017 Beijing Workshop on Forecasting

Survey of Kagglers finds Python, R to be preferred tools

Competitive predictive modeling site Kaggle conducted a survey of participants in prediction competitions, and the 16,000 responses provide some insights about that user community. (Whether those trends generalize to the wider community of all data scientists is unclear, however.) One question of interest asked what tools Kagglers use at work. Python is the most commonly-used tool within this community, and R is second. (Respondents could select more than one tool.) Interestingly, the rankings varied according to the job title of the respondent. R and Python received top-ranking for every job-title subgroup except one (database administrators, who preferred SQL), according to the following division: R: Business Analyst, Data Analyst, Data Miner, Operations Researcher, Predictive Modeler, Statistician Python: Computer Scientist, Data Scientist, Engineer, Machine Learning Engineer, Other, Programmer, Researcher, Scientist, Software Developer You can find summaries of the other questions in the survey…
Original Post: Survey of Kagglers finds Python, R to be preferred tools

Survey of Kagglers finds Python, R to be preferred tools

Competitive predictive modeling site Kaggle conducted a survey of participants in prediction competitions, and the 16,000 responses provide some insights about that user community. (Whether those trends generalize to the wider community of all data scientists is unclear, however.) One question of interest asked what tools Kagglers use at work. Python is the most commonly-used tool within this community, and R is second. (Respondents could select more than one tool.) Interestingly, the rankings varied according to the job title of the respondent. R and Python received top-ranking for every job-title subgroup except one (database administrators, who preferred SQL), according to the following division: R: Business Analyst, Data Analyst, Data Miner, Operations Researcher, Predictive Modeler, Statistician Python: Computer Scientist, Data Scientist, Engineer, Machine Learning Engineer, Other, Programmer, Researcher, Scientist, Software Developer You can find summaries of the other questions in the survey…
Original Post: Survey of Kagglers finds Python, R to be preferred tools

Webinar: Taking Semantic Search to Full Text, Nov 7

[unable to retrieve full-text content]Learn about content challenges of R&D teams in the life sciences, the benefits of semantic enrichment, and a solution that reduces overhead and adds value to information discovery and innovation initiatives.
Original Post: Webinar: Taking Semantic Search to Full Text, Nov 7