Web-interface for Delphi method

I recently worked on a fun project at work. I developed a web-interface for administering a questionnaire using the Delphi method. The Delphi method aims to bring consensus on a given topic using a concept similar to ‘the wisdom of crowds‘.
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Web-interface for Delphi method

My favorite pizza dough

Because lately I have been writing too often about serious stuff, today I will share the result of another type of experiments: the recipe of my favorite pizza dough. I have been experimenting making my own pizza dough for the last three years and I believe I have now found a recipe which yields a good result. The recipe distills and combines the wisdom of three very positive influences, Dan Leppard, Emmanuel Hadjiandreou, and il cucchiaio d’argento.
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My favorite pizza dough

Streamgraph in R [final]

This post is an update on the previous post translating Byron and Wattenberg’s streamgraphs algorithm into R. Byron and Wattenberg’s algorithm produces beautiful streamgraphs with the synthetic data produced by their streams generator. However, the implementation yields an ugly streamgraph when applied to data which might not be as wiggly as the synthetic ones. In the attempts I made I got very peaky wiggles, not smoothed and irregular. In short the graphs did not transmit the idea of a stream, but of a blurry blob or a peaky primitive bat (the wooden club, not the animal, that would be cool!). In this post I bring-up some points to bear in mind when producing a streamgraph. Continue reading “Streamgraph in R [final]”

Streamgraph in R [final]

Streamgraphs in base::R [e.III]

This is the third post on streamgraph in R. After a simple introduction on how to generate a streamgraphs and an example with actual data it was time for a more general implementation to the creation streamgraphs using R. Continue reading “Streamgraphs in base::R [e.III]”

Streamgraphs in base::R [e.III]

Multiple mediation: extracting output

Lavaan’s output is copious. Especially when fitting a complex model, lavaan’s output is literally a mine of information. Finding what is relevant is not always easy, therefore I will try to describe a way to summarize lavaan’s summary output. Continue reading “Multiple mediation: extracting output”

Multiple mediation: extracting output

Plotting multiple mediation

This posts on multiple mediation on lavaan supplements the two previous ones (1 – introducing multiple mediator analysis with lavaan and 2 – showing an example analysis) by describing how to process lavaan’s output graphically. I discovered the handy package semPlot and I am very positive about it. I will make the example as reproducible as possible, so that each step can be repeated. Also, I am going to try to provide more explanation about the R commands I used because a friend pointed out that the description of the steps was sometimes a bit dry and abstract.
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Plotting multiple mediation

Multiple-mediation example with lavaan

This post extends this previous one on multiple-mediation with lavaan. Here I modeled a ‘real’ dataset instead of a randomly generated one. This dataset we used previously for a paper published some time ago. There we investigated whether fear of an imperfect fat self was a stronger mediator than hope of a perfect thin self on dietary restraint in college women. At the time of the paper’s publication we performed the analysis using the SPSS macro INDIRECT . However,
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Multiple-mediation example with lavaan