Dear all, I am using the lmrob() function from the robustbase package, and I have a few questions. To keep the threads clear, I have a general inquiry here, and will ask more specific Qs in a second thread. NB: I don't typically update in the middle of a project, so am running on R version 3.4.1. (1) I am curious the community's thoughts on our approach: We have several hundred ecological time series and we're using robust linear models to determine if the time series are increasing, decreasing, or not changing, by looking at the modeled slope. This approach follows several others, including Lotze et al. 2017 (doi: 10.1111/cobi.12957) and Magare et al. 2013 (doi:10.1371/journal.pone.0077908). I don't have much experience with RLMs, so any thoughts on this approach would be very welcome. More specifically, following the work noted above, we are running (with the time series indexed with "DBx"): lm_test<-lmrob(log(pop_status+1)~observation_year,DBx) (2) Previous use of RLMs to identify the direction of ecological time series was asked in peer review to use "non-significant change" to reference time series with a slope of zero within the 95% confidence intervals. I can see excluding time series where there is no agreement on the direction of slope, but I think that slope=0 is more "stable" or "no change" and is not necessarily "non-significant". Any thoughts? Thank you all very much for any feedback you may have. I will start a second thread on a few warnings I am getting. Emily
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Emily S. Klein, Senior Postdoctoral Associate (she / her / hers) The Frederick S. Pardee Center for the Study of the Longer-Range Future | Boston University *Co-Chair*, ICES Working Group on the History of Fish & Fisheries (WGHIST) esklein04 at gmail.com http://www.bu.edu/pardee/ http://www.ices.dk/community/groups/Pages/WGHIST.aspx [[alternative HTML version deleted]]