Interpreting adjusted R-squared of a log transformed regression model

I am running a linear regression model where the dependent variable (Y) is log-transformed. I am struggling on how to interpret the adjusted R-squared of this log-transformed model that is meaningful. Any insight is very much appreciated! Thank you.

• After the fit, you will need to de-transform the dependent variable and model predictions, and then manually calculate. Oct 4, 2018 at 20:15
• @JamesPhillips Thanks for your answer. Could you please elaborate a bit more what do you mean by manual calculation and how to perform it? Oct 4, 2018 at 20:33
• I calculate R-squared (R2) as "R2 = 1.0 - (absolute_error_variance / dependent_data_variance)" and the R-squared adjusted (R2adj) as "R2adj = 1.0 - (1.0 - R2)*((number_of_data_points - 1.0)/(number_of_data_points-number_of_parameters))" Oct 4, 2018 at 22:36
• @JamesPhillips If the relationship is assumed to be linear when the outcome is logged, why should OP back-transform the predictions to calculate an $R^2$? Oct 5, 2018 at 1:08
• @HeteroskedasticJim one of the reasons is that the R-squared calculation I wrote in my comment requires variance of the dependent variable, not variance of the log(dependent variable). Oct 5, 2018 at 10:55