Specifying Imputation Model [Warning Messages]

[shind10]

Hello. I have been working on this for almost 2 weeks now and I couldn't find the answer.. so I decided to ask here.

This question is about specifying a imputation model (Multi level MI) with two explanatory variable, one at the individual level (variable name C) and one at the group level (variable name W), including a random slope and interaction effects. Response variable name is R.

The Model that I am interested is following (in lmer syntax).

lmer(R~MC+C_MC+W+MC:C_MC+W:C_MC+(C_MC | id) )
where MC is group mean of C,
C_MC is deviation from group mean MC (Also C_MC has random slope)

and I also want to specify my imputation model to be exactly same as the model that I am interested in.

Note that there are only 4 variable (with missing in R and C and fully observed W and id which indicates the group number) in the dataset I simulated. (missing depends on W)

Below is how I specified imputation model, and it produces warning message that I cannot fix.

d=data.frame(method1, MR=NA, R_MR=NA, MR.R_MR=NA, W.R_MR=NA, MC=NA, C_MC=NA, MC.C_MC=NA, W.C_MC=NA)
meth=make.method(d)
meth[c("R","C")]="2l.norm"
meth[c("W","id")]="2l.pmm"
meth[c("MR","MC")]="2l.groupmean"
meth["R_MR"]="~I(R-MR)"
meth["C_MC"]="~I(C-MC)"
meth["MR.R_MR"]="~I(MR * R_MR)"
meth["MC.C_MC"]="~I(MC * C_MC)"
meth["W.R_MR"]="~I(W * R_MR)"
meth["W.C_MC"]="~I(W * C_MC)"

pred=make.predictorMatrix(d)
pred[,]=0
pred[,"id"]=-2
pred["R",]=c(-2,0,0,1,0,0,0,0,1,2,1,1)
pred["C",]=c(-2,0,0,1,1,2,1,1,0,0,0,0)
pred["MR", c("id", "R")]=c(-2,1)
pred["MC", c("id", "C")]=c(-2,1)

visit=c("R","MR","R_MR", "W.R_MR","C","MC","C_MC","MC.C_MC","W.C_MC")
imp1 <- mice(d, pred = pred, meth = meth, visit = visit, m = 10, allow.na = TRUE)

Predication matrix looks below.

However, when I run the code, it generates warning messages that number of items to replace is not a multiple of replacement length.

Can anyone please tell me what is wrong with my imputation model and if possible, how to fix it?

P.S
Surprisingly, when I added interaction effect between W:MC ( W= level 2 variable, MC = group mean of C) into imputation model like below prediction matrix, it worked without an error.
I don't understand why it worked and have very strong desire to understand the reason. Please help.