I would like to do some imputation in a situation like this:
Knowing that a value is missing is highly informative itself: if a certain variable has a missing value, it must be between zero and some positive constant c. This c is smaller (usually quite a lot smaller) than any of the observed values. Constant c varies between the variables, though. Because of this informative nature of missing values, I consider that uniformly distributed random numbers would suffice.
The problem is how to do this in the framework of mice package because 'runif imputation' is not built in mice. Even I (with my humble coding skills) could easily do the actual imputation part without the package but I would like to exploit the very convenient framework in the analyses.
I found a topic a bit similar (Multiple imputation for missing values) where some custom imputation methods are implemented but they actually refer to a method already implemented in mice (PMM), so it appears not to be very helpful.
I also tried to study the post-processing feature (https://www.gerkovink.com/miceVignettes/Passive_Post_processing/Passive_imputation_post_processing.html) but the "squeeze" example is not quite the thing I am looking for and I do not know how to modify it to my case.
So, what could be the easiest way to do this 'runif imputation' in mice package?
A toy model of the data and the corresponding constants c are below:
v1<-c(NA,NA,196684.6,8266.4,12403.1,NA,315621.8,163686.0,267788.9,2818.6,101087.4,NA,49193.2,178748.6,NA,40129.9,137476.2,253865.2,NA,13409.9) v2<-c(4786.9,NA,4181.1,10038.5,13842.5,21692.1,15093.4,NA,5662.9,8000.2,8426.2,5160.7,NA,NA,16904.2,10409.6,7379.6,30973.0,NA,NA) v3<-c(NA,2512989,407434.2,436502.7,931959.3,528485.7,1319345.0,826987.4,258134.1,413298.0,2976947.4,484316.1,NA,205461.1,1808292.0,NA,374079.4,2413572.0,131438.5,311361.4) dat<-data.frame(v1,v2,v3) c<-c(4000,20,25000)