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. ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
