I am attempting to model the yield of various crops as a function of weather data, namely one temperature variable and 7 moisture-related variables (measuring different aspects of moisture content). The moisture readings exhibited a significant degree of collinearity and were all using different units, and so as recommended by some other answers on this site, I scaled the moisture variables and applied Principal Component Analysis, picking the PCs that accounted for > 95% of the variance cumulatively.
However, I now have a question regarding when to scale the data prior to applying machine learning techniques. I'm trying to build a mixed effects model with
lme4 package. Since the PCs were obtained by scaling only the moisture data, if I wanted to make a model of the form
yield ~ temperature + PC1 +... + PCN + (1|categorical vars), would I need to re-scale the dataset consisting of
Also, is it recommended to scale the response variable as well? Any clarification and help would be much appreciated; I'm only just getting started on this path.