Interaction terms and random effects in tobit regression model in R - r

Question

Can anyone tell me if it is possible to incorporate:
a)an interaction term
b)a random effect
in a Tobit regression model in R?
For the interaction term I have been working on the following script, but that doesn't work.
fit <- vglm(GAG_p_DNA~factor(condition)+factor(time)+factor(condition):factor(time),
tobit(Lower = 0))
Error in if ((temp <- sum(wz[, 1:M, drop = FALSE] < wzepsilon))) warning(paste(temp, :
argument is not interpretable as logical
I have also tried this with dummy variables, created in the following way:
time.ch<- C(time, helmert,2)
print(attributes(time.ch))
condition.ch<-C(condition, helmert, 3)
print(attributes(condition.ch))
but I get the same error.
Part of the dataset (GAG_p_DNA values of zero are left censored) (Warning: to those who may be copying this. The OP used tabs as separators.)
Donor Time Condition GAG_p_DNA cens_GAG_p_DNA
1 1 6 0.97 1
1 1 10 0.93 1
1 7 2 16.65 1
1 7 6 0.94 1
1 7 10 1.86 1
1 28 2 21.66 1
1 28 6 0.07 1
1 28 10 3.48 1
2 1 1 1.16 1
2 1 2 2.25 1
2 1 6 2.41 1
2 1 10 1.88 1
2 7 2 13.19 1
2 7 10 2.54 1
2 28 2 23.93 1
2 28 6 0 0
2 28 10 15.17 1
I most likely need to use a Tobit regression model, as it seems that a Cox model with left censored data is not supported by R...
fit<- survfit(Surv(GAG_p_DNA, cens_GAG_p_DNA, type="left")~factor(condition)+factor(Time))] [Error in coxph(Surv(GAG_p_DNA, cens_GAG_p_DNA, type = "left") ~ factor(condition) + : Cox model doesn't support "left" survival data

Answer 1

Try this:
survreg(Surv( GAG_p_DNA, cens_GAG_p_DNA, type='left') ~
factor(Time)*factor(Condition), data=sdat, dist='gaussian')
(Recommended by Therneau: http://markmail.org/search/?q=list%3Aorg.r-project.r-help+therneau+left+censor+tobit#query:list%3Aorg.r-project.r-help%20therneau%20left%20censor%20tobit+page:1+mid:fnczjvrnjlx5jsp5+state:results )
--- earlier effort;
With that tiny dataset (where I have corrected the use of tabs as separators) you won't get much. I corrected two errors (spelling of "Condition" and using 0 for left censoring where it should be 2 and it runs without error:
sdat$cens_GAG_p_DNA[sdat$cens_GAG_p_DNA==0] <- 2
fit <- survfit(Surv(GAG_p_DNA, cens_GAG_p_DNA, type="left") ~
factor(Condition) + factor(Time), data=sdat)
Warning messages:
1: In min(jtimes) : no non-missing arguments to min; returning Inf
2: In min(jtimes) : no non-missing arguments to min; returning Inf
3: In min(jtimes) : no non-missing arguments to min; returning Inf
4: In min(jtimes) : no non-missing arguments to min; returning Inf
5: In min(jtimes) : no non-missing arguments to min; returning Inf
6: In min(jtimes) : no non-missing arguments to min; returning Inf
7: In min(jtimes) : no non-missing arguments to min; returning Inf
8: In min(jtimes) : no non-missing arguments to min; returning Inf
9: In min(jtimes) : no non-missing arguments to min; returning Inf
> fit
Call: survfit(formula = Surv(GAG_p_DNA, cens_GAG_p_DNA, type = "left") ~
factor(Condition) + factor(Time), data = sdat)
records n.max n.start events median
factor(Condition)=1, factor(Time)=1 1 2 2 0 1.16
factor(Condition)=2, factor(Time)=1 1 2 2 0 2.25
factor(Condition)=2, factor(Time)=7 2 3 3 0 14.92
factor(Condition)=2, factor(Time)=28 2 3 3 0 22.80
factor(Condition)=6, factor(Time)=1 2 3 3 0 1.69
factor(Condition)=6, factor(Time)=7 1 2 2 0 0.94
factor(Condition)=6, factor(Time)=28 2 2 2 2 0.00
factor(Condition)=10, factor(Time)=1 2 3 3 0 1.41
factor(Condition)=10, factor(Time)=7 2 3 3 0 2.20
factor(Condition)=10, factor(Time)=28 2 3 3 0 9.32
0.95LCL 0.95UCL
factor(Condition)=1, factor(Time)=1 NA NA
factor(Condition)=2, factor(Time)=1 NA NA
factor(Condition)=2, factor(Time)=7 13.19 NA
factor(Condition)=2, factor(Time)=28 21.66 NA
factor(Condition)=6, factor(Time)=1 0.97 NA
factor(Condition)=6, factor(Time)=7 NA NA
factor(Condition)=6, factor(Time)=28 0.00 NA
factor(Condition)=10, factor(Time)=1 0.93 NA
factor(Condition)=10, factor(Time)=7 1.86 NA
factor(Condition)=10, factor(Time)=28 3.48 NA
The other aspect which I would call an error as well would be not using a data argument to regression functions. Trying to use "attached" dataframes, with any regression function but especially with the 'survival' package, will often cause strange errors.
I did find that putting in an interaction by way of hte formula method generated this error:
Error in survfit.formula(Surv(GAG_p_DNA, cens_GAG_p_DNA, type = "left") ~ :
Interaction terms are not valid for this function
And I also found that coxme::coxme, which I had speculated might give you access to mixed effects, did not handle left censoring.
fit <- coxme(Surv(GAG_p_DNA, cens_GAG_p_DNA, type="left")~factor(Condition)*factor(Time), data=sdat)
Error in coxme(Surv(GAG_p_DNA, cens_GAG_p_DNA, type = "left") ~ factor(Condition) * :
Cox model doesn't support 'left' survival data