I wander if there is a menu driven software which can perform variance partitioning (Borcard et al., 1992, Borcard and Legendre, 1994) in order to separate the importance of spatial dependence and spatial autocorrelation for community distribution data?
Yes, most of the times R is the answer to our problems... I tried running away from it for a long time but i had to eventually surrender.
Anyway, in R there are some "menu driven" user interfaces that have been created, but they are very limiting. What made me overcome some of my fears was an interface called RStudio. It is quite simple and only has a few menus that make a lot of simple tasks more intuitive and user-friendly (like importing and exporting data, loading packages and reading help info on different commands).
Moreover, it also helps because you can "rehearse" all the steps you need for your analysis by writing them in a .txt file (you can even add some text explanations to tell to your future self what you were doing with that code). Then you just change the ".txt" extension to ".R", open the file in RStudio and it will be there right under the R console and you can run code simply by selecting portions of this txt file and pressing "run". Almost like a menu! :)
You could try Canoco but it would be maybe easier for you to start using R and follow the steps in Borcard et al 2011.
Hi Levan,
As suggested by Tristan, you should try to start using R. If you don't have Borcard et al (2011) (which is a book "Numerical Eology with R"), then you can try the exemples given in the "varpart" command of the "vegan" package in R. If I remember well the examples therein are based on the 2 Borcard's papers that you mention (the "mite" dataset).
Of course it is not a "menu driven" software but it is much much better (IMHO) !
Should you realy prefer menus, try the "Rcmdr" package in R, that gives you some kind of "menus" to lauch R packages and commands (although to a limited extent, however).
Hope this helps,
Tito
Yes, most of the times R is the answer to our problems... I tried running away from it for a long time but i had to eventually surrender.
Anyway, in R there are some "menu driven" user interfaces that have been created, but they are very limiting. What made me overcome some of my fears was an interface called RStudio. It is quite simple and only has a few menus that make a lot of simple tasks more intuitive and user-friendly (like importing and exporting data, loading packages and reading help info on different commands).
Moreover, it also helps because you can "rehearse" all the steps you need for your analysis by writing them in a .txt file (you can even add some text explanations to tell to your future self what you were doing with that code). Then you just change the ".txt" extension to ".R", open the file in RStudio and it will be there right under the R console and you can run code simply by selecting portions of this txt file and pressing "run". Almost like a menu! :)
Thanks for your replay. I have already start using R, but I am not yet good with it :))
You are right Miguel, Rstudio is also a nice piece of "add on" for R.
Levan, you can find it here: http://rstudio.org/
Hope this helps
Tito
R has a steep learning curve Levan, but you'll realize very soon that it's worthwhile to invest the effort. Absolutely go with R, and RStudio is a great starting point. You'll find plenty of online help for any specific question you might have.
Some analytical problems in R are grouped in 'tasks', and there is a specific one for spatial analyses, the CRAN Task View: Analysis of Spatial Data, maintained by Roger Bivand:
http://cran.r-project.org/web/views/Spatial.html
Tasks views group libraries related to a specific data analysis problem. There are very useful ones:
http://cran.r-project.org/web/views/
Another source of useful info for spatial analysis is in:
https://geodacenter.asu.edu/projects/rsp
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