Gam

This post has been stimulated by a discussion with a colleague who asked about the normalization method for the root mean square error (NRMSE) in the INDperform R package, which is based on the indicator testing framework outlined in my article (Otto et al. 2018)1. At the time of writing the article and package I simply used a common approach and didn’t test it much further. But sparked by this discussion I started to test it thoroughly (as you will see below), which will make me revise the package.

Release indicator package INDperform

By Saskia in indicator

January 7, 2019

INDperform Overview INDperform is an R package that implements a quantitative framework for selecting and validating the performance of state indicators tailored to meet regional conditions and specific management needs as described in Otto et al. (2018) 1 (see also my post on indicators). The package builds upon the tidy data principles and offers functions to identify temporal indicator changes, model relationships to pressures while taking non-linear responses and temporal autocorrelation into account, and to quantify the robustness of these models.

Testing indicator performance

By Saskia in indicator

October 16, 2018

Indicators are useful and versatile tools applied in disciplines such as engineering, chemistry, medicine, economy or sociology. In ecosystem-based management a key role of an indicator is to inform on the current status of the system component as well as the effectiveness of specific management measures to move the component into a different state. In European Union (EU) marine policy, indicator development has recently progressed as part of the implementation of the Marine Strategy Framework Directive (MSFD)1 to aid the achievement of Good Environmental Status (GES) of the EU’s marine waters by 2020.

How to normalize the RMSE

Release indicator package INDperform

Testing indicator performance

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