Dear R mailing list, I'm trying to fit a censored regression model to a large (dimension of the design matrix is 2e5 by 7) right truncated data by means of the survreg(Surv()) function, as suggested by Paul Johnson on his "R Tips" web page. Possibly due to the sensitivity to the initial values of the Newton-Raphson routine in use by survreg(), resulting regression outputs turn out to be unreliable. Should the "init" field be left blank, the maximisation barely starts (2 iterations), and ends up yielding estimates close to 0 or +/- infinity. I guess the problem mainly lies in reasonably specifying starting values for the optimisation embedded in survreg(). Even by inputing OLS estimates (the default in the SAS lifereg method) ensuing inferences are remarkably different, and with significantly lower log-likelihood, than the ones returned by the SAS proc lifereg. Has anybody encountered a similar problem? If so, any suggestion/advise would be grately appreciated. Many thanks in advance, all best, -- Dr. Stefano Conti Department of Probability and Statistics The Hicks Building University of Sheffield Sheffield S3 7RH UK Phone # +44 (0)114 2223854 Fax # +44 (0)114 2223759