For the selection of variables for a regression model, I did a pairwise correlation matrix between the different predictors and the response variable. From the pairwise correlation matrix, I realise there could be a linearity relationship between the log transformed predictors and the response variable. I would like to ask if it is better to check for multicollinearity by using the vif function in R after transforming the predictors or just simply check for multicollinearity on the independent variables?
Also, my response variable is symmetric after I log transformed it. Is it a sensible decision in doing so before checking for multicollinearity? Any advice or suggestions are welcomed. Thanks!