The latest update of the DHARMa R package (0.4.6) includes an option to perform simulated likelihood ratio tests (LRTs) for GLMMs based on a parametric bootstrap. I wanted to shortly comment on how this works and why this is useful. Why is this useful? A well-known issue with mixed models is that the df lost…
Note also the blog post by Daniel Lakens on the same topic here. Update March 23, 2022: a more concise / slightly modified version of these thoughts was published as a letter in TREE, co-authored together with Fred Barraquand. Background Criticism of p-values has been rampant in recent years, as were predictions of their imminent…
In regression analysis, a common problem is to decide on the right functional form of the fitted model. On the one hand, we would like to make the model as flexible as possible so that it can adjust itself bias-free to the true data-generating process. On the other hand, the more freedom we give the…
Do you remember the notorious hurricane / himmicane study (Jung et al., PNAS, 2014)? At the time, there was a heavy backlash against the study, and probably rightly so, as the statistical analysis turns out to be highly unstable against a change of the regression formula. You can find some links here. Over the years,…
tl;dr: DHARMa tests will pick up on overdispersion before you see a rise of Type I error. Overdispersion is a common problem in GL(M)Ms with fixed dispersion, such as Poisson or binomial GLMs. Here an explanation from the DHARMa vignette: GL(M)Ms often display over/underdispersion, which means that residual variance is larger/smaller than expected under the…
Although one would think that the basic concepts of statistics should be the same across all sciences, there is an amazing heterogeneity between fields in how statistics is taught and practiced. One example are the validity concepts (e.g. construct validity, internal validity, external validity) that are heavily taught in the social sciences and economics (see…
A guest post by @MaxMaPichler, MSc student in the Group for Theoretical Ecology / UR Artificial neural networks, especially deep neural networks and (deep) convolutions neural networks, have become increasingly popular in recent years, dominating most machine learning competitions since the early 2010’s (for reviews about DNN and (D)CNNs see LeCun, Bengio, & Hinton, 2015). In ecology,…
By Lisa Hülsmann and Florian Hartig (U Regensburg), with short comments by Ryan Chisholm (NUS) and Matteo Detto (Princeton). By Betteridge’s law, the answer to this question is of course no. Or better: we don’t know. But let’s back up a bit: Global gradients in CNDD Almost a year ago, LaManna and coauthors published a paper in Science…
This (co-)guest post by Carsten F. Dormann & Florian Hartig summarizes a comprehensive review on model averaging for predictive inference, just published in Ecological Monographs.
Over at dynamic ecology, Jeremy Fox argues that Technical statistical mistakes are overrated; ecologists (especially students) worry too much about them. Individually and collectively, technical statistical mistakes hardly ever appreciably slow the progress of entire subfields or sub-subfields. And fixing them rarely meaningfully accelerates progress. continuing with Don’t agree? Try this exercise: name the most…
This is a somewhat belated introduction of a package that we published on CRAN at the beginning of the year already, but I hadn’t found the time to blog about this earlier. In the R environment and beyond, a large number of packages exist that estimate posterior distributions via MCMC sampling, either for specific statistical models (e.g.…
As I said before, I firmly side with Andrew Gelman (see e.g. here) in that model checking is dangerously neglected in Bayesian practice. The philosophical criticism against “rejecting” models (double-using data etc. etc.) is all well, but when using Bayesian methods in practice, I see few sensible alternatives to residual checks (both guessing a model and…
A guest post by Carsten F. Dormann Over the last years, I have been using null models more often than I liked. I had to, when there was no other way to figure out if an ecological pattern was unexpected, or trivial. Inspired by some recent (and also some older) posts, I thought I might throw around…
I just released a small R package that I have been working on for a while. The motivation for this package came from the observation that I kept on receiving questions about residual checks for GLMMs. The problem that people have is that they have fitted their GLMM, maybe they tested it for overdispersion and…
For a few years now, Jörn Pagel, Joseph Chipperfield, Björn Reineking (in the beginning) and I have been teaching an introductory summer school in Bayesian statistics. In previous years, we were in Bayreuth 2011 Bayreuth 2012, Göttingen 2013, Freiburg 2013 and Bergen 2014. This year, we will be again in Lygra, a beautiful field station…
The central quantity in Bayesian inference, the posterior, can usually not be calculated analytically, but needs to be estimated by numerical integration, which is typically done with a Monte-Carlo algorithm. The three main algorithm classes for doing so are Rejection sampling Markov-Chain Monte Carlo (MCMC) sampling Sequential Monte Carlo (SMC) sampling I have previously given…
Two events I’m involved in that may be interesting for people that work on connecting process-based vegetation models to data. Both are organized through CA 1304 PROFOUND, and fully funded by COST: There will be a training school on Bayesian calibration, forecasting and multi-model predictions of process-based vegetation models in Rencurel / Grenoble, France, 12.-16.…
As announced a while ago, we had moved our now already traditional summer school in Bayesian Statistics to Bergen, Norway this year. Maybe fitting for such a course, the weather turned out to be very different from the long-term frequency, in what must be the upper 1% quantile of sun intensity for the region at…
I’ve just returned from two weeks in France, the first week on the International Statistical Ecology Conference 2014 in Montpellier, and the second at the Laboratoire d’Écologie Alpine (LECA) in Grenoble, visiting the groups of Wilfried Thuiller and Sébastien Lavergne, which was both great. Some impressions from the ISEC: First of all, my compliments to…
I have blogged a few days a ago about a study by Kiju Jung that suggested that implicit bias leads people to underestimate the danger of female-named hurricanes. The study used historical data to demonstrate a correlation between femininity and death-toll, and subsequent experiments seemed to show that people indeed estimate hurricanes to be less…
theoretical ecology 