Dear R-user: I have a left censored longitudinally measured data set with 4 variables such as sub (which is id), x (only covariate), y (repeatedly measured outcome variable) and w (weights) (note, “-5” indicates the left censored value in the attached data set). I am using following R codes (“survival” library and “survreg” package) for fitting a random effect tobit model for the left censored longitudinal data: library(splines) library(survival) setwd('C:/data') data=read.table('C:/data/Ruser4897.csv', sep=",") names(data)=c("sub", "x", "y", "w") data[1:100,] x=data[,2] y=data[,3] w=data[,4] survreg(Surv(y, y>=0, type='left')~x, dist='gaussian', weight=w) The output is as follows: Call: survreg(formula = Surv(y, y >= 0, type = "left") ~ x, weights = w, dist = "gaussian") Coefficients: (Intercept) x -18.1990038 0.1749655 Scale= 9.831055 Loglik(model)= -23508.9 Loglik(intercept only)= -23947.1 Chisq= 876.48 on 1 degrees of freedom, p= 0 n=4840 (57 observations deleted due to missingness) I am not seeing any estimated variance component in the output. Could you please help me in finding the appropriate argument in survreg function so that I can get the estimated variance component in the output please? FYI, if I put “sub” using “cluster(sub)” in the model to get the variance component estimation, then following error message is giving: > survreg(Surv(y, y>=0, type='left')~x+cluster(sub), dist='gaussian', weight=w) Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid type (closure) for variable 'cluster(sub)' I have another related question please. If it is possible, how may I fit the marginal (or population average) model for this data either by modifying following function or any other function? survreg(Surv(y, y>=0, type='left')~x, dist='gaussian', weight=w) Your suggestion or help could save me from breaking up this endeavor. I would really appreciate you if you could help me in figuring out the error in my coding. Sincerely, Sattar
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