The run pacer

Hi, Thank you for your visit. I moved this post to my running blog. Happy reading!

Beginning with SEM in lavaan III

This post concludes the translation from LISREL’s to R’s of Iacobucci’s paper: Everything You Always Wanted to Know About SEM (Structural Equations Modeling) But Were Afraid to Ask. This post covers the full structural equation model.

Advertisement

Objectifying plant watering

Every spring I struggle with my superpower: killing perfectly healthy plants by watering them too much. Every year, around July or August, I accept the same conclusion: I should give up trying to cultivate plants and, next year, I should spare plants the misery of being nurtured by me. But then spring comes again and brings with it the scent of flowers, the sight, and odor of cooking herbs and therefore the temptation to try again. This year instead of suppressing my craving I will adopt a smarter approach. I bought a sensor to monitor the plants’ moisture which I will use to prevent me from drowning the plants. With the moisture values as a starting base, I will apply the scientific method to the subtle art of watering plants.

Web-interface for Delphi Method III

This post is the third in the series describing the web-interface I created to administer a questionnaire with the Delphi method. In this post I describe the code used to give feedback to the participants in round 2 of the Delphi study on functional magnetic resonance imaging on tinnitus. If you are interested in the previous posts, this post describes the interface for round 1 whereas this post describes (the first part of) the interface for round 2.

Web-interface for Delphi Method II

This post describes the web-interface I built for the second round of the Delphi method study on functional magnetic resonance imaging and tinnitus. In the second round, the experts who participated in the first round saw the responses that they gave to the first round side-by-side with the responses of all the other experts. As represented in the figure above, in round 2 the Delphi interface displayed one question and two bar plots showing the distribution of experts’ responses for the given question in round 1. To keep the post short and to the point, I divided the description of the interface for round 2 into two parts.

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‘.

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.

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.

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.

Streamgraphs in base::R [e.II]

Until recently I did not have a practical application in which to use streamgraphs. In fact, I still find the visualisation complex to understand, abstract and a bit too artistic. While I recognise that the strength of streamgraphs is the display of all the time series’ values into one (possibly interactive) plot, the amount of data displayed is massive, with many streams and even more data points. Because of the amount of data displayed