3116 search results for "twitter"

Choosing R packages for mixed effects modelling based on the car you drive

April 13, 2017
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Choosing R packages for mixed effects modelling based on the car you drive

Choosing R packages for mixed effects modelling based on the car you drive There are many roads you can take to fit a mixed effects model (sometimes termed hierarchical models) in R. There are numerous packages that each deploy different engines to fi...

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Come Fly With Me (well, not really) — Comparing Involuntary Disembarking Rates Across U.S. Airlines in R

April 13, 2017
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Come Fly With Me (well, not really) — Comparing Involuntary Disembarking Rates Across U.S. Airlines in R

By now, word of the forcible deplanement of a medical professional by United has reached even the remotest of outposts in the #rstats universe. Since the news brought this practice to global attention, I found some aggregate U.S. Gov data made a quick, annual, aggregate look at this soon after the incident: Overall annual boarding... Continue reading...

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Dataviz of the week, 12/4/17

April 12, 2017
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Dataviz of the week, 12/4/17

This week, a chart with some Bayesian polemic behind it. Alexander Etz put this on Twitter: He is working on an R package to provide easy Bayesian adjustments for reporting bias with a method by Guan & Vandekerchhove. Imagine a study … Continue reading →

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Sow the seeds, know the seeds

April 11, 2017
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When you do simulations, for instance in R, e.g. drawing samples from a distribution, it’s best to set a random seed via the function set.seed in order to have reproducible results. The function has no default value. I think I mostly use set.seed(1)....

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8th MilanoR Meeting – Presentations, photos and video!

April 10, 2017
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8th MilanoR Meeting – Presentations, photos and video!

Hello R-users <3 The 8th MilanoR Meeting was great! Thank you very much for your interest and participation! A short recap for those who weren't there: the meeting was last Wednesday evening at the Microsoft House (a very nice location, thanks @dr_mattia for your support!). We had two exceptional speakers: Stefano Iacus, member of the R The post

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In case you missed it: March 2017 roundup

April 10, 2017
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In case you missed them, here are some articles from March of particular interest to R users. A tutorial and comparison of the SparkR, sparklyr, rsparkling, and RevoScaleR packages for using R with Spark. An analysis of Scrabble games between AI players. The doAzureParallel package, a backend to "foreach" for parallel computations on Azure-based clusters. The UK government project...

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How and when: ridge regression with glmnet

April 10, 2017
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How and when: ridge regression with glmnet

@drsimonj here to show you how to conduct ridge regression (linear regression with L2 regularization) in R using the glmnet package, and use simulations to demonstrate its relative advantages over ordinary least squares regression.  Ridge regression Ridge regression uses L2 regularisation to weight/penalise residuals when the parameters of a regression model are being learned. In the context of...

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#4: Simpler shoulders()

April 8, 2017
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#4: Simpler shoulders()

Welcome to the fourth post in the repulsively random R ramblings series, or R4 for short. My twitter feed was buzzing about a nice (and as yet unpublished, ie not-on-CRAN) package https://github.com/dirkschumacher/thankr by Dirk Schumacher which compiles a a list of packages (ordered by maintainer count) for your current session (or installation or ...) with a...

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The faces of R, analyzed with R

April 7, 2017
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The faces of R, analyzed with R

Maëlle Salmon recently created a collage of profile pictures of people who use the #rstats hashtag in their Twitter bio to indicate their use of R. (I've included a detail below; click to see the complete version at Maëlle's blog.) Naturally, Maëlle created the collage using R itself. Matching Twitter bios were found using the search_users function in the...

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R benchmark for High-Performance Analytics and Computing (II): GPU Packages

April 7, 2017
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R benchmark for High-Performance Analytics and Computing (II): GPU Packages

Share This: 1. Overview In the previous post (here), we have analyzed the performance gain of R in the heterogeneous system by accelerators, including NVIDIA GPU and Intel Xeon Phi. Furthermore, GPU accelerated packages can greatly improve the performance of R. Figure 1 shows the download statistics of CRAN over the years. Obviously, GPU is

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