Hello again, thank you very much for your help so far.
To be more specific, I generate a simplified data set that is similar to my real world data: set.seed( 123 ) data <- data.frame( x = runif( 200 ), y = NA ) for( i in 1:200 ){ data$y[ i ] <- rweibull( 1, 1, 70 + 10 * data$x[ i ] ) - 30 } data$y[ data$y < 0 ] <- 0 data$y[ data$y > 100 ] <- 100 Applying an interval censored tobit model based on the normal distribution works: estNorm <- tobit( y ~ x, left = 0, right = 100, data = data ) Since my data are obviously not normally distributed, I tried the Weibull distribution, but this does not work (as I wrote before). estWeibull <- tobit( y ~ x, left = 0, right = 100, dist = "weibull", data = data ) I have tried to implement Terry's suggestion. > [...] Using Surv(t1, t2, type='interval2'), you can have > a left censored observation where time of event < t: represented as (NA, > t) > a right censored observation where time of event >t: represented as (t, > NA) > an interval censored observations t1<=time <= t2 : represented as > (t1,t2) > estWeibull2 <- survreg( Surv( ifelse( y == 0, NA, y ), ifelse( y == 100, y, NA), type = "interval2" ) ~ x, data = data ) Is this correct? My endogenous variable is not a time depending variable but percentages which naturally are censored in the interval [0,100]. Unfortunately many data points are 0 or 100 exactly. The rest of the data is asymmetrically distributed. So I would like to apply a two-limit tobit, regressing the percentage (endogenous variable) on several explanatory variables. Best Geraldine ______________________________________________ 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.