I am running MaxEnt modeling for my target species distribution. There I need to select the least correlated variables to avoid multicollinearity. This multicollinearity can be tested using various tests such as Pearson's correlation coefficient (r), Variance inflation factor (VIF), and Principal component analysis (PCA). Among these, I find PCA a bit difficult to understand. My questions are
1). Can I go with any one of these methods, to check collinearity?
2). Which one of the tests is the best? if any.
3). Will it be okay if I only go with Pearson's correlation coefficient (r)? Will it make my result and interpretation sufficient?
1. Yes
2. VIF is a very simple test to assess multicollinearity in your regression model. Statistical software calculates a VIF for each independent variable. VIFs start at 1 and have no upper limit. A value of 1 indicates that there is no correlation between this independent variable and any others. VIFs between 1 and 5 suggest that there is a moderate correlation, but it is not severe enough to warrant corrective measures. VIFs greater than 5 represent critical levels of multicollinearity.
3. Yes.
It is a popular detection method. If the value above .8 (or .7 or .9 or some other high number), the rule of thumb says you have multicollinearity.
And it is certainly true that a high correlation between two predictors is an indicator of multicollinearity. But there are two problems with treating this rule of thumb as a rule.
First, how high that correlation has to be before you’re finding inflated variances depends on the sample size. There is no one good cut off number.
Second, it’s possible that while no two variables are highly correlated, three or more together are multicollinear.
Please find the variance inflated factor (VIF) (Hair et al., 2017).
The cut-off value must be less than 5. Less than 5 is accepted.
Multicollinearity can be detected using the variance inflation factor (VIF) and correlation analysis. See here for more details to identify Multicollinearity in your dataset https://www.reneshbedre.com/blog/variance-inflation-factor.html
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