Centering and rescaling numeric data for logistic regression

Hi Everyone,

I have a dataset with 7 numerical variables and 3 categorical variables (imported as factors). I am trying to run a multiple logistic regression with a random intercept using the glmer () function. When I run the model currently, I get a warning about the predictor variables being on different scales:
"Some predictor variables are on very different scales: consider rescaling
optimizer (Nelder_Mead) convergence code: 0 (OK)
boundary (singular) fit: see ?isSingular"

I therefore am trying to figure out how to efficiently center and rescale my numeric data while keeping my whole dataset together (categorical and numeric data) to run my model. Thanks so much for any advice.

Maybe have a look at the scaling and centreing section here

or perhaps chapter 12 in Regression and Other Stories - link to pdf on page.

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