My first blog post was a guest post

August 30, 2016 Etc PVA publications Hungary

The title says it all. I wrote this piece about Publication Viability Analysis pondering about a pattern that I observed while looking at Hungarian ecologists publication output through time using the Web of Science database (the original post is in Hungarian).

I fit Ricker growth model to observed publication numbers and a non-stationary 2-phase model was the best fit. An intrinsic growth rate of 0.39 with carrying capacity K = 14 publications per year was characteristic to the pre-democracy (Soviet occupation era) phase (1978–1997). The democratic era (1998–2012) showed growth rate of 0.21 with a much higher K = 100 (variance went from 0.44 to 0.03).

The motivation for the analysis and post was that the number of publications has been persistently around K = 100 for 5 years. So I looked at the correlation between the number of PhD students and the number of publications, using different lag times between 0 and 10 years. The correlation was highest for a 7-year lag. This more or less indicates a cohort of PhD students (myself included) who started PhD around 2000.

This cohort represents the grandchildren of the post-WWII boom, births have declined after this cohort – therefore less PhD students to produce papers. If this is true, it also means that it takes considerable time for PhD students to reach peak publication productivity. So I proposed to shorten the lag to 3–5 years to boost publication numbers. Win for the students and win for Hungary.

Why am I bringing this old post up 3 years later? Because I wanted to see how well my estimates have held up from a short-term forecasting standpoint. Well, here are the results of a recent query with identical settings (ADDRESS=HUNGARY; CATEGORIES=ECOLOGY) for the years 2013–2015:

Year Count
2013 87
2014 114
2015 108

PVA

The figure shows the two phases used in modeling (grey and gold), and the forecast (tomato) with horizontal lines for carrying capacity. I wish these numbers have improved, but it is what it is. You can’t argue with science. In case you want to argue, just leave a comment!

Statistical computing meets biodiversity conservation and natural resource management

What is new in the intrval R package?

An update (v 0.1-1) of the intrval package was recently published on CRAN. The package simplifies interval related logical operations (read more about the motivation in this post). So what is new in this version? Some of the inconsistencies in the 1st CRAN release have been cleaned up, and I have been pushed hard (see GitHub issue to implement all the 16 interval-to-interval operators. These operators define the open/closed nature of the lower/upper limits of the intervals on the left and right hand side of the o in the middle as in c(a1, b1) %[]o[]% c(a2, b2).

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