Thanks Hideaki. It does indeed work through Variable Importance.
However, as I asked about in a previous post, I would like to select all the variables as candidate predictors, but the column selector dialog only allows one to choose from among the first 300 hundred. So - as you see in that post - I was advised to carry out Random Forest through '+' > Build Model > Random Forest > Regression.
Also, I see that in the Variable Importance analytics, there is no 'Importance Table' view, or 'Prediction Matrix' (as in the tutorial: https://docs.exploratory.io/analytics/var_importance.html).
I don't know much about data science or code, so I wouldn't be able to find the preprocessing or to fix the error. If you have any further advice, I would be very grateful. Otherwise, I'll try to use the 'Variable Importance' analytics as they are shown, or to find other solutions for building strong predictive models for my outcome variables.
By the way, would you or anyone happen to know anything about the MICE package for multiple imputations? I was also given help for this through the very generous and kind Exploratory support team. However, I am getting an error in that procedure: Error in cor(xobs[, keep, drop = FALSE], use = "all.obs") : 'x' is empty.
Anyway, thanks again!