I have a Dataset containing information about spendings of customers in various shops. There are 10 spending variables related to some categories (like spendings on clothing, spendings on hardware, spendings on service) and one variable which is spendings_overall. Spendings_overall should be the sum of the 10 single subcategory spendings. There are some additional variables describing the customers (age, sex, customergroup, ...)
The Problem: Participants have the possibility to say "I don't know the amount I've spent any more, but I know that I have spent something". So in some cases all 10 subcategory-spendings and the overall spendings variables might be not NA. In some cases some of the variable might be NA and in some cases all of the variables could be NA.
My goal is to impute the missing data, but i have no idea how to deal with the constraint of spendings_subcategorie_1 + spendings_subcategorie 2 + ... + spedings_subcategorie_10 = spendings_overall.
Usually i would try to hit the missings values with missForest, but i don't think, that there is any possibility to include the constraint i need (or at least i have no idea how to do so).
So i would like to ask which approaches i could try for the given problem. Any hints and tips are very welcome.
Additional Information about the data collecting process: In a first step participants were asked if they had any spendings or not for each of the 10 subcategories (not the overall spendings, since only "true" customers and not "just visitors" were asked). If they answered with yes they were asked about how much they did spend for the given subcategory. Here it was possible to leave the input-field blank and instead select the checkbox "i cant remember my spendings any more". After this there was a final question about the total amount spent (again with the possibility to answer "i cant remember my spendings"). Unfortunately the sum of subcategory spendings and the answered spendings overall do not add up to the same total amount in some cases (considering only cases without any missing data in subcategory and overall spendings). But we want to "unify" sum of subcategories and overall spendings before starting data-imputation (either by averaging the two values or selecting one of both and adapting the sub-categorie spendings on the total spendings). We also talked about inputing before "unifying", but decided against it.
Unfortunately i cant share the original data, but the dataframe looks like this:
set.seed(123)
data_spendings = data.frame(matrix(rep(NA, 140), ncol = 14))
names(data_spendings) = c("age", "sex", "customergroup", "spendings_overall", paste0("spendings_subcat_", 1:10))
data_spendings$age = round(rnorm(10, 50, 20)) # participants age
data_spendings$sex = sample(c("male", "female"), 10, replace = T) # participants gender
data_spendings$customergroup = sample(c(1:5), 10, replace = T) # grouping of customers, depending on crs data
data_spendings[5:14] = matrix(rnorm(100, 100, 20), ncol = 10) # spendings on 10 different subcategories (like spendings_clothing, spendings_hardware, spendings_service etc.)
data_spendings$spendings_overall = rowSums(data_spendings[5:14]) # overall spendings of the person (which should be the sum of the single subcategorie spendings)
# Problem: People had the option to say "i know i spent something, but i can't remember how much it was"
cant_remebers = rep(FALSE, NROW(data_spendings)*11)
cant_remebers[sample(1:length(cant_remebers), round(length(cant_remebers)) *0.3)] = TRUE # approximately 30% of the spendings cant be remembered
data_spendings[4:14][matrix(cant_remebers, ncol = 11, byrow = T)] = NA
data_spendings
