Dear R-helpers, excuse me if this is not exclusively an R-related question. I have data from a nested design, both temporally and spatially, and the reponse variable of interest is left-censored. That is, only values > "some treshold" are available, otherwise "LOW" is reported. Are there ways of building a linear model with both fixed and random effects, when the response variable is censored? Can the tobit model be modified to do this? Does anyone have experience with this type of dataset? Help is much appreciated, Remko Duursma ^'~,_,~'^'~,_,~'^'~,_,~'^'~,_,~'^'~,_,~'^'~,_,~' Remko Duursma, Ph.D. student Forest Biometrics Lab / Idaho Stable Isotope Lab University of Idaho, Moscow, ID, U.S.A.
On Wed, 11 Jun 2003, Remko Duursma wrote:> Dear R-helpers, > > excuse me if this is not exclusively an R-related question. > > I have data from a nested design, both temporally and spatially, and the > reponse variable of interest is left-censored. That is, only values > > "some treshold" are available, otherwise "LOW" is reported. > > Are there ways of building a linear model with both fixed and random > effects, when the response variable is censored? Can the tobit model be > modified to do this? Does anyone have experience with this type of > dataset? >For a random intercept model you could use survreg() and frailty() in the survival package. In general the random effects tobit model will be quite hard to fit, involving a numerical integration whose dimension is the number of random effects. Some sort of EM algorithm might work. There is a paper by Pettit in Biometrics some time ago on censored linear mixed models -- I don't have the reference with me. -thomas