[R] R function for censored linear regression

[Andreas Wittmann]

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