Dear R-useRs, I'm looking for an R-function for censored linear regression. I have the following data x1 <- rnorm(100) x2 <- rnorm(100) y <- x1 + 2*x2 + rnorm(100,0,0.5) stat <- rep(1,100) stat[50:100] <- 0 data <- data.frame(y,x1,x2,stat) y is the dependent variable, x1 and x2 are the independent variables in a linear model. the variable y could be right-censored, this information is in the variable stat, where 1 denotes observed and 0 denotes censored. If stat is 0, then the value in y is the observed right-censored value and could be greater. Using the Tobit-model would not be the right thing here because the Tobit model assumes the same limit for all observations, in my data each value of y[50:100] could have a different limit. If i use linear regression lm1 <- lm(y ~ x1 + x2, data=data) summary(lm1) the censoring is not incorporated, so my idea is to use survreg from the survival package library(survival) s1 <- survreg(Surv(y, stat) ~ x1 + x2, data, dist='gaussian') summary(s1) my question is, is this the right approach for my aim? Is it right, that here each censored observations could have its own limit? Thanks and best regards Andreas