Interpreting Ridge Trace Plot

#1
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?
 

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hlsmith

Less is more. Stay pure. Stay poor.
#2
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.