First, note that all attachments are removed from messages sent to R-help, please use plain text only. I took the time to bring up the attachment in a web browser (there is a link to it), but you are unlikely to get a lot of readers when the question is captured as a pdf.
1. Tobit regression on the raw data: The "scale" is the residual standard deviation, or estimate of "sigma" in the usual linear models terminology. For linear regression with uncensored data, the estimate of scale is independent of the estimate of the other coefficients, and can be computed at the end. For censored data this is not so, the scale and the coefficients must be estimated together. During the maximization step the routine uses log(scale), it forces positive coefficients and also works better numerically; as a consequence the full variance/covariance matrix includes both beta and log(scale). 2. Tobit regression on the rescaled data. You must have rescaled both x and y, since the estimated residual variance changed. I see multiple disadvantages, and no advantages, to regression on rescaled data. I know it is common in some fields, but why? With respect to your questions on how to interpret rescaled coefficients, I don't know how to interpret them either. 3. The "residual" for a censored observation is not a well defined quantity. Hence both the computation and meaning of R^2 are unclear to me. Terry Therneau Terry Therneau ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.