effect sizes from stepwise regression I'm new to stepwise regression and I've been asked to conduct one for my boss. In doing so, they also asked for the effect sizes from each predictor in the model. Disregarding any debates around whether or not one should conduct a stepwise regression (remember, this is for my boss), is it possible to get the requested effect sizes?
Here is the model that I am working  with:
faModel <- lm(respTime ~ separation + exp + field, data=faM)

response time is measured in seconds
separation is continuous
exp and field are categorical with 3 and 3 levels respectively and were converted to factors. 

Here is the stepwise regression code:
faP <- ols_step_both_p(faModel, penter= 0.05, prem = 0.05, 
        details = TRUE)

Note the ols_step_both_p() is from the olsrr package.
Here is an image of the final model output:

So you can see that R-squared for exp is 0.274 and the final R-squared with field is 0.286, which suggests that the field R-squared is 0.012 (I think?). So can I use the formula: f = R-squared/(1-R-squared) to find the final model effect size? And the same formula for each predictor included in the model (e.g., 0.274 and 0.012)?

Given that you have to use stepwise, why not bootstrap it?  See for instance How to evaluate collinearity or correlation of predictors in logistic regression?
Then, as @Dave said in a comment, calculate effect sizes the way you would do it with a prespecified model, but repeat it in every bootstrap resample.  The same you can do with other fit statistics.  The variability between resamples will give some indication of the reliability of the effect size estimates.
