Thank you for this. My problem is I want to tease out the estimated, nonlinear effect associated with just one variable and then plot that effect.

I was able to figure out a workaround using Poisson regression:

```
results1 <- glm(CONSUMPTION ~ INCOME+WEALTH, family=poisson, data=Consumption )
effect_plot(results1,pred=INCOME,data=Consumption)
```

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.