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help needed with discriminant function analysis (1 Viewer)

tjbirder999

Well-known member
I am currently analyzing my data from mist-netting of weavers in Sudan. I am trying look for a way to separate female Cinnamon and Northern Masked Weavers, and also to separate females and males in non-breeding plumage. From what I can gather, discriminant function analysis is one way of doing this. I have found some information online, but I am a bit out of my depth and could use some help.
The main thing is that I do not have decent statistical software on my Mac to play around with and try to figure it out. From what I can gather, Excel cannot do it. At university I used Minitab, but this is really expensive to buy. In the past, I have used the one-month Mintab download to do analyses, but I would like something more permanent. Can anyone recommend a cheaper alternative I could use that is not too complicated, and would allow me to carry out my analyses? I can afford to pay a couple of hundred dollars, or thereabouts, if it will be a useful package. Or does anyone know of any alternative ways to do it?
Any help appreciated.
Thanks

Tom
 
Perhaps you could use Weka for this, it's free and includes several different tools for classification, for example logistic regression that might be an alternative to DFA.

http://www.cs.waikato.ac.nz/ml/weka/

There is also a separate package for DFA in Weka, but you need to install it first (I haven't used it myself).

http://weka.sourceforge.net/packageMetaData/discriminantAnalysis/index.html
http://stackoverflow.com/questions/26987843/discriminant-analysis-package-for-weka

Using R would be another alternative, widely used for statistical applications etc (also freeware).

http://www.statmethods.net/advstats/discriminant.html
https://www.youtube.com/watch?v=s8pvp2Ctxfc
you can find more on R and DFA on google etc.
 
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R is free but has a steep initial learning curve. On the other hand, it is becoming more and more popular in academic science and time spent learning to use it is unlikely to be wasted if you will have other data to analyse in the future.
 
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