By Joel Cadwell
R may be the lingua franca, yet many of the packages within the R library seem to be written in different languages. We can follow the R code because we know how to program but still feel that we have missed something in the translation.
R provides an open environment for code from different communities, each with their own set of exemplars, where the term “exemplar” has been borrowed from Thomas Kuhn’s work on normal science. You need only to examine the datasets that each R package includes to illustrate its capabilities in order to understand the diversity of paradigms spanned. As an example, the datasets from the Clustering and Finite Mixture Task View demonstrate the dependence of the statistical models on the data to be analyzed. Those seeking to identifying communities in social networks might be using similar terms as those trying to recognize objects in visual images, yet the different referents (exemplars) change the meanings of those terms.
Thinking in Terms of Causes and Effects
Of course, there are exceptions, for instance, regression models can be easily understood across applications as the “pulling of levers” especially for those of us seeking to intervene and change behavior (e.g., marketing research). Increased spending on advertising yields greater awareness and generates more sales, that is, pulling the ad spending lever raises revenue (see the R package CausalImpact). The same reasoning underlies choice modeling with features as levers and purchase as the effect (see the R package bayesm).
The above picture captures this mechanistic “pulling the lever” that dominates much of our thinking about the marketing mix. The exemplar “explains” through analogy. You might prefer “adjusting the dials” as an updated version, but the paradigm remains cause-and-effect with each cause separable and under the control of the marketer. Is this not what we mean by the …read more
Source:: r-bloggers.com