First, run Pearson correlation within all your independent variables and, ideally, any two variables with r > 0.8, you should drop one. To decide on which one to drop, check their contribution/importance in explaining the distribution of your species (easy to do in MaxEnt) and drop the less important one.
![ปฏิเสธไม่ไหว (Crush on you) - LIPTA feat. No One Else [Official MV]](http://i.ytimg.com/vi/NBw1jF342vw/mqdefault.jpg)
Fascinating topic.. specially seeing from a remote sensing and gis prospective
Great! I learned a lots from the lectures
Thanks a lot for this course, it´s fantastic!
very useful, thank you!! lovely illustrations
Thank you for your lecture. I have one question how can we eliminate multicollinearity in the environmental variable.
Many thanks for this. Its really been helpful.
Thank's a million
That's great, thanks!
Great :D
Thank you for your lecture. I have one question how can we eliminate multicollinearity in the environmental variable.
First, run Pearson correlation within all your independent variables and, ideally, any two variables with r > 0.8, you should drop one. To decide on which one to drop, check their contribution/importance in explaining the distribution of your species (easy to do in MaxEnt) and drop the less important one.