Dear author,
When I run RDA anaysis, I find that there are not full environment variables in the correlation plot, like this: I use 42 environment variables, but there are only 21 in this plot. I have read the A&Q in issue511,but I am still confused about the alias variables:
In my data, the rda only considers the first 21 variables and ignores the rest. Then, I delete the first 21 variables and perform the rda, it still consider the first 21 variables in the rest variables. But these variables do not have strong correlation, for example: UV and bio1-11.
**Thus, my problems are:
If I want to draw the explanatory plot of all 42 variables (like the plots below), what should I do?
How could I know variable and its alias variable? Could I use one of the variables to perform RDA？
Also, in rda forward selection, we also meet alias variables, if one is significant, does another is significant too?
When I use the results of RDA to get the intersects of the results of LFMM, there are little intersects. Do u have any suggestions? (my workflow is : use forward selection to select envrionment variables--run rda anaysis and LFMM anaysis--get the intersects)**
Thank you sir very much ! I really appreciate it if u could help me!
Dear author, When I run RDA anaysis, I find that there are not full environment variables in the correlation plot, like this: I use 42 environment variables, but there are only 21 in this plot. I have read the A&Q in issue511,but I am still confused about the alias variables: In my data, the rda only considers the first 21 variables and ignores the rest. Then, I delete the first 21 variables and perform the rda, it still consider the first 21 variables in the rest variables. But these variables do not have strong correlation, for example: UV and bio1-11.
**Thus, my problems are:
Thank you sir very much ! I really appreciate it if u could help me!
all variables
change the sorts
delete bio12-19