New Yorkers On Why They Put Up With Maddeningly Slow Buses: 'I Don't Have No Choice'

Andre Cuevas slumped over his phone in the back of the bus. His construction boots, coated in a thin layer of dust, straddled his helmet resting on the floor. Cuevas had opted that day to take the Bx19 bus along 145th Street to get to the 2 train after work, but only because he was particularly tired. Most of the time, he will walk instead. He says walking is faster. [ more › ] Gothamist http://bit.ly/2Rrgpwe January 30, 2019 at 09:45PM

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Former San Diego mayor joins race for California governor

Former San Diego Mayor Kevin Faulconer is running for California governorABC News: Top Stories https://ift.tt/3oNBFgt February 02, 2021 at 08:39AM

Using RStudio and LaTeX

(This article was first published on r – Experimental Behaviour , and kindly contributed to R-bloggers) This post will explain how to integrate RStudio and LaTeX, especially the inclusion of well-formatted tables and nice-looking graphs and figures produced in RStudio and imported to LaTeX. To follow along you will need RStudio, MS Excel and LaTeX. Using tikzdevice to insert R Graphs into LaTeX I am a very visual thinker. If I want to understand a concept I usually and subconsciously try to visualise it. Therefore, more my PhD I tried to transport a lot of empirical insights by means of visualization . These range from histograms, or violin plots to show distributions, over bargraphs including error bars to compare means, to interaction- or conditional effects of regression models. For quite a while it was very tedious to include such graphs in LaTeX documents. I tried several ways, like saving them as pdf and then including them in LaTeX as pdf, or any other file ...

Explaining models with Triplot, part 1

[This article was first published on R in ResponsibleML on Medium , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Explaining models with triplot, part 1 tl;dr Explaining black box models built on correlated features may prove difficult and provide misleading results. R package triplot , part of the DrWhy.AI project, is aiming at facilitating the process of explaining the importance of the whole group of variables, thus solving the problem of correlated features. Calculating the importance of explanatory variables is one of the main tasks of explainable artificial intelligence (XAI). There are a lot of tools at our disposal that helps us with that, like Feature Importance or Shapley values, to name a few. All these methods calculate individual feature importance for each variable separately. The problem arises when features used ...

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