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”
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.
Continue reading “Plotting multiple mediation”
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,
Continue reading “Multiple-mediation example with lavaan”
I wrote this brief introductory post for my friend Simon. I want to show how easy the transition from SPSS to R can be. In the specific case of mediation analysis the transition to R can be very smooth because, thanks to lavaan, the R knowledge required to use the package is minimal. Analysis of mediator effects in lavaan requires only the specification of the model, all the other processes are automated by the package. So, after reading in the data, running the test is trivial.
Continue reading “Multiple-mediator analysis with lavaan”
This scatterplot is one of the best data visualisation I made. I like it because it concentrates a lot of information into a single visualisation. The scatterplot displays four dimensional data (i.e., four variables) using a two dimensional scatterplot. I made the first implementation in R, but because I wanted to add interactivity I switched to d3.js. Below I describe the choices I made to display the information and how I coded them in d3.js. Continue reading “Four dimensions in two dimensions”
This is the fourth and last assignment of Machine Learning for Data Analysis by Wesleyan University on Coursera. My assignment diverges quite a bit from the approach taken by the instructor since I wanted to have only three clusters to determine pumps functionality (functional, functional needs repair, and Continue reading “Clustering Pumps [mlw4]”
This is the third assignment of the Machine Learning for Data Analysis by Wesleyan University on Coursera. I applied least absolute shrinkage and selection operator (LASSO) to the DrivenData data set pumpItUp. LASSO is a technique which does variable selection shrinking the ‘useless’ coefficients (i.e., variables) toward zero. Applying this method Continue reading “Shrinking pumps? [mlw3]”