The survey-package from Thomas Lumley is a great toolkit when analyzing complex samples. It provides svyglm(), to fit generalised linear models to data from a complex survey design. svyglm() covers all families that are also provided by R’s glm() – however, the survey-package has no function to fit negative binomial models, which might be useful for overdispersed count models. Yet, the package provides a generic svymle() to fit user-specified likelihood estimations. In his book, Appendix E, Thomas Lumley describes how to write your own likelihood-function, passed to svymle(), to fit negative binomial models for complex samples. So I wrote a small „wrapper“ and implemented a function svyglm.nb() in my sjstats-package.
The functions returns an object of class svymle, so all methods provided by the survey-package for this class work – it’s just that there are only a few, and common methods like predict() are currently not implemented. Maybe, hopefully, future updates of the survey-package will include such features.
When describing a sample, researchers in my field often show proportions of specific characteristics as description. For instance, proportion of female persons, proportion of persons with higher or lower income etc. Since it happens often that I like to know these characteristics when exploring data, I decided to write a function, prop(), which is part of my sjstats-package – a package dedicated to summary-functions, mostly for fit- or association-measures of regression models or descriptive statistics.
Yesterday, I had the pleasure to give a talk at the 8th Hamburg R User-Group meeting. I talked about data wrangling and data transformation, and how the philosophy behind the tidyverse makes these tasks easier. If you like, you can download the slides here (or at the GitHub-Repo from the Hamburg R-User-Group). Feel free to add your comments to the slide here.
I’m pleased to announce sjmisc 2.3.0, which was just updated on CRAN. The update might break existing code – however, functions were largely revised to work seamlessly within the tidyverse. In the long run, consistent design makes working with sjmisc more intuitive.
Basically, sjmisc covers two domains of functionality:
Reading and writing data between R and other statistical software packages like SPSS, SAS or Stata and working with labelled data; this includes easy ways to get and set label attributes, to convert labelled vectors into factors (and vice versa), or to deal with multiple declared missing values etc.
Data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data.
This posting briefly describes some of the changes to the function design that do data transformation tasks.
My sjPlot-package was just updated on CRAN with some – as I think – useful new features.
First, I have added some vignettes to the package (based on the existing online-documentation) that cover some core features and principles of the sjPlot-package, so you have direct access to these manuals within R. The vignettes are also online on CRAN.
This is another post of my series about how my packages integrate into a pipe-friendly workflow. The post focusses on my sjmisc-package, which was just updated on CRAN, and highlights some of the new features. Examples are based on data from the European Social Survey, which are freely available.
Please note: The statistical analyses at the end of this post mainly serve the purpose of demonstrating some features of the sjmisc-package that target „real life“ problems! For clarity reasons, I ran a quick-and-dirty model, which is not of high statistical quality or standard!
Recent development in R packages are increasingly focussing on the philosophy of tidy data and a common package design and api. Tidy data is an important part of data exploration and analysis, as shown in the following figure:
Tidying data not only includes data cleaning, but also data transformation, both being necessary to perform the core steps of data analysis and visualization. This is a complex process, which involves many steps. You need many packages and functions to perfom those tasks. This is where a common package design and api comes into play: „A powerful strategy for solving complex problems is to combine many simple pieces“, says the tidyverse manifesto. For a coding workflow, this means:
compose single functions with the pipe
design your API so that it is easy to use by humans
The latter bullet point is helpful to achieve the first bullet point.
When conducting meta-analysis, you most likely have to calculate or convert effects sizes into an effect size with common measure. There are various tools to do this – one easy to use tool is the Practical Meta-Analysis Effect Size Calculator from David B. Wilson.