Interpreting Ridge Trace Plot

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?



Less is more. Stay pure. Stay poor.
It is hard to tell in this plot. You should overlay a faint reference line at y =0. From my eye, purple converges to null early and green as well. I use LASSO more, but the larger coefficients my have a larger penalty but they have a larger coefficient latter on.