Ridge Trace Plot - Interpretation In my research, I aimed to perform a regression model with four predictors and one response variable. When I verified a high collinearity among the predictors, I was instructed to handle this problem using a ridge regression. So, I developed this analysis using R's glmnet package, and I generated the ridge trace plot below.

I understand that the most important predictor is the one whose coefficients converge more slowly to 0 as the shrinkage penalty increases. Considering this, could I say that the variable corresponding to the green line on my plot would be the most important? I concluded this because it took longer to shrink regarding to the red and blue lines, which decreased remarkably as λ increased. And with respect the purple line, would it be correct to say that it was the second most important variable?

To me it seems that the most important variable is represented by the red line, and second most important by the blue line. That's because initially, when the penalty for having high coefficient values is low, they are both quite high.
After the penalty increases the model is forced to shrink the coefficients and the third variable - represented by the green line - takes their place to compensate.
In contrast the purple line is least important because its influence (coefficient) remains close to zero regardless of the penalty.
Note: I have never interpreted such a plot before, so don't take this as being authoritative. Also this answer assumes that all the variables are standardised for this kind of plot and are represented by comparable units.
