if the lm has 'log(response) ~ target' formula, and you use the precid() formula to assign the result to a variable, you will get the models predicted log(response) values into your desired variable name.
you can then make a new variable which is the previous variable applied to the function exp()

This allows me to identify the effect of one variable (INCOME) even when the regression has more than one explanatory variable (INCOME+WEALTH), and plots the estimated effect with CONSUMPTION on the vertical axis y rather than ln(CONSUMPTION), with INCOME on the horizontal axis.

The associated estimates are virtually identical to what I would get from the log-linear regression:
results2 <- lm(I(log(CONSUMPTION)) ~ INCOME+WEALTH, data=Consumption )

I appreciate you for taking the time to help me with my problem.