BONUS events on 3-4 May 2017 in Helsinki:


  • BONUS Blue Baltic projects kick-off conference 
  • Triple meeting of Project Coordinators, Advisory Board and Steering Committee
  • BONUS 10th anniversary jubilee evening school and gala dinner


Open consultation of BONUS Art 185, have your say by 30 April 2017! Take part in the consultation here

Copyright Markku Viitasalo, SYKE

News from projects

16.02.2017 BONUS BaltCoast published second special issue of the Coastal & Marine Magazine

The BONUS BaltCoast project published the second out of three special issues of the Coastal & Marine Magazine.

Read more

12.01.2017 BONUS Soils2Sea Stakeholder Workshops

Summary of three BONUS Soils2Sea Workshop online.

Read more


03.01.2017 New BONUS SOILS2SEA publications

Three deliverables are available online at the Website of the project.

Read more


Project blogs

20.02.2017 14:01One-way ticket from Matlab to R, and back! (BONUS BALTICAPP - Race Against Eutrophication Blog) Matti Sihvonen

Hi everybody!

I’ve decided to write this somewhat monthly blog post in somewhat more technical fashion compared to my previous posts, which are written in very general fashion. The methods that I’ve been using so far in writing my first two articles have been non-linear weighted OLS estimation and dynamic programming. I’ve used Matlab for both of these. Matlab is a great program for many things, particularly optimizing, as far as my experiences are considered. However, I’ve started to question the reliability of the estimation results that Matlab provides. In addition, I haven’t been able to get all the statistics that I would need for the proper analysis. These statistics include the basics standard errors, t-values and p-values for the estimates. I also can’t reproduce the goodness-of-fit statistics by basics calculations myself. I noticed that the 95 % confidence intervals that Matlab gives for the estimated parameters are very wide. Those even go beyond the range were the function is determined, which I found very suspicious. Thus, I turned to R, which is very much-applied program for statistical analysis. I learned to do the weighted non-linear estimation in R and I finally got all the statistics that I needed. In addition, I understand the statistics it provides and there’s nothing mysterious going on, which I found very important when the actual research work is considered; one has to know where all the numbers have come from.

Needless to say, I had to reconsider some models and results. However, although I found R more suitable for statistical analysis than Matlab, the issue with non-linear estimation in R is the starting values for the estimates. It seems that those have to be very close to final values so that the program will be able to do the fitting. So how do you get to those starting values? I used Matlab for those. Matlab’s curve fitting toolbox, although I don’t trust in the statistics it provides, is very useful, as it illustrates the curve and the residuals right away. It also is very successful in fitting the curve or surface even with the default starting values, once the iteration account is set high enough. This might be related to Matlab’s good optimization abilities, as it is matrix-based program, and estimation is optimization, after all. Thus, I used Matlab for initial examination for a particular functional form and to get the starting values. Then I inserted the functional form and the starting values into R to get the final estimates for the parameters as well as the associated statistics. Then I used Matlab again for drawing illustrative simulation figures and for the economic optimization. I must say that for those purposes the Matlab is superior compared to R, at least as far as my very limited programming skills are considered. All the figures in my work are drawn with Matlab, with the exception of one schematic diagram.

Anyway, now that I’ve been shifting back and forth between Matlab and R a couple of weeks, I believe that the first article is finally ready and it’s time to continue the work with the second article, which focuses entirely on optimization. In first work we concentrated on model derivation and examination of the structural and parameter uncertainty. In the next paper we will examine analytically and numerically the private optimums a bit further and then we move to examine the social optimum, where also the environmental externalities are considered. I will write more about those in the upcoming blog posts. I guess my main tool when doing the second article will be Matlab again, because at least currently is seems that no statistical analysis will be involved.

Thus, see you next time!


Best regards: Matti Sihvonen

Read more | 0 Comments

Strategic research agenda

Developed together with over 800 stakeholders across the Baltic Sea region, this serves as the backbone of the BONUS programme.

Call information

Copyright Rodeo
BONUS call 2015: Blue Baltic closed in March 2016 for independent evaluations. The final outcome of projects to be funded by late 2016.

Read more


Copyright Shutterstock
Find out more about projects funded from BONUS calls open 2012-2015, as well as BONUS+ pilot projects implemented 2009-2011.


Copyright FIMR, IL
Find out more about implementing BONUS projects, including scientific and financial management and other information. 

Read more

Young scientists

Copyright_Viktorija Vaitkeviciene
NEW! REGISTRATION is OPEN for the next BONUS Young Scientist Club that will convene on 12 June 2017 at the 11th BSSC in Rostock, Germany. 

Read more



In horizon: The tenth anniversary of BONUS, the eleventh Baltic Sea Science Congress and the sixth BONUS Young Scientist Club...

Read more


BONUS publications
BONUS reports, newsletter, briefings, brochure and other can be obtained in soft copy downloads or in hard copy by post.

Tweets from @BONUSBaltic