Regression on transformed variable
https://cran.r-project.org/web/packages/emmeans/vignettes/transformations.html#bias-adj is probably the easiest place to start. That machinery is assuming that the transformation is stated *explicitly* in the model formulation; the example used in the vignette is pigs.lm <- lm(log(conc) ~ source + factor(percent), data = pigs) I think it wouldn't work if the transformation was done upstream (i.e. if your response variable was `log_conc`), and possibly not if you had an unusual transformation. Looking at the function in emdbook, I don't think it's directly useful for what you want. car::deltaMethod looks more useful. However, it's designed for specific nonlinear functions of *parameters*, I don't know if it can easily do bias correction on predictions. On Thu, Mar 19, 2026 at 11:42?AM Christofer Bogaso
<bogaso.christofer at gmail.com> wrote:
Thanks Ben.
Could you please help pointing out names of specific functions on Bias
correction? I searched with like ls('package:emdbook') etc. however
failed to identify relevant functions.
On Thu, Mar 19, 2026 at 6:55?PM Ben Bolker <bbolker at gmail.com> wrote:
There are functions in the emdbook, metafor, and car packages that
do some version of the delta method (although people use "delta
method" to refer both to adjusting E[f(y)] using a second-order
correction [since the first-order term disappears] and to adjusting
V[f(y)] using a first-order correction ...)
emmeans also has such capabilities, search the vignettes for "bias correction"
cheers
Ben Bolker
On Thu, Mar 19, 2026 at 8:57?AM Christofer Bogaso
<bogaso.christofer at gmail.com> wrote:
Hi, In many case, we need to transform the dependent variable before fitting a regression equation, to make it "well-behaved" like close to normal curve etc. like, f(y) = alpha + beta1 X1 + beta2 X2 + ... + epsilon Now for prediction, R will typically calculate E[f(y)] based on the fitted coefficients. However, in real scenario, we actually need to find E[y]. Typically, we perform reverse transformation like on fitted E[f(y)] directly. However, I believe that in this process, we also need to make some additional correction for non-linearity in the f() to correctly calculate E[y]. Onr possible way to do it, may be using Taylors approximation. My question is there any R function that would directly do that based on the shape of f()? Thanks for your time.
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