MICE package in R: passive imputation - r

Question

I aimed to handle missing values with multiple imputation and then analyse with mixed linear model.
I am stacked by passive imputation for "BMI" (body mass index) and "BMI category". "BMI" was calculated by height and weight and then categorized into "BMI category".
How to impute 'BMI category'?
The database looks like below:
sub_eu_surf[1:5, 3:12]
age gender smoking exercise education sbp dbp height weight bmi
1 41 1 1 2 18 120 80 185 107 31.26370
2 46 1 3 2 18 130 70 182 102 30.79338
3 46 1 3 2 18 130 70 182 102 30.79338
4 47 1 1 2 14 130 80 178 78 24.61810
5 47 1 1 1 14 150 80 175 85 27.75510
Since 'bmi category' is not a predictor of my imputation, I decided to create it after imputation. And details are below:
1. To define method and predictor
ini<-mice(sub_eu_surf, maxit=0)
meth<-ini$meth
meth["bmi"]<-"~I(weight/(height/100)^2)"
pred <- ini$predictorMatrix
pred[c("pm25_global", "pm25_eu", "pm10_eu", "no2_eu"), ]<-0
pred[,c("bmi", "hba1c", "pm25_eu", "pm10_eu")]<-0
pred[,"tc"]<-0
pred[c("smoking", "exercise", "hdl", "glucose"), "tc"]<-1
pred[c("smoking", "exercise", "hdl", "glucose"), "ldl"]<-0
vis <- ini$vis
imp_eu<-mice(sub_eu_surf, meth=meth, pred=pred, vis=vis, seed=200, print=F, m=5, maxit=5)
long_eu<- complete(imp_eu, "long", include=TRUE)
long_eu$bmi_category<-cut(as.numeric(long_eu$bmi), breaks=c(0, 18.5, 25, 30, 72))
complete_eu<-as.mids(long_eu)
But I received an error when analyzing my data:
test1<-with(imp_eu, lme(sbp~pm25_global+gender+age+education+bmi_category, random=~1|centre))
Error in eval(expr, envir, enclos) : object 'bmi_category' not found
How does this happen?

Answer 1

You are running your analyses on the original mids object imp_eu, not on the modified complete_eu. Try:
test1<-with(complete_eu, lme(sbp~pm25_global+gender+age+education+bmi_category, random=~1|centre))