Today’s first focus sessions was planned around modeling. Two presentations stood out for me, were the ones by Christian Kleiber on generalized regression on count data, and Gianmarco AltoÃ¨ on bootstrapped model comparison.

Christan Kleiber presented a very interesting package regarding regression models for count data. Classical count data models are for instance poisson regression, which is offered by several packages already in R-Project. Using many of the code already available in R, Kleiber wrote several functions, for instance for efficiently estimating zero-inflated models or so-called Hurdle models. Although apparently developed for use in econometrics, I can easily see the use for this package, especially regarding the zero-inflated models.

I think that the presentation given by Gianmarco AltoÃ¨, and especially the package DeltaR he developed, can be very valuable to some types of research. As a statistician, he was asked for the possibility to compare the proportion of variance explained by different regression models, estimated using *different* samples. I don’t see myself using this, since as a sociologist I try to get samples that cover the whole population as best as possible anyway. However, especially in disciplines such as psychology, management studies, or perhaps even development studies, I can see the use of model comparisons.

I do wonder though, that if we are comparing the models based on different samples, if we are not implicitly assuming that the two samples are subsets of a single sample. If that should be the case, we don’t need to apply this type of comparison and we could better merge the data and perform a single analysis, focused on the comparison between the two groups.

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