So I tried to impute some missing data and there is a problem. I want three variables to be used as predictors but I don't want them to be imputed. Even though I specified the predictormatrix as the following:
# Initialize
m0 = mice(datimp, maxit = 0)
predmat = m0$predictorMatrix
m0$predictorMatrix
m0$method
meth = m0$method
meth["cm.dep.0"] = "2l.pan"
meth["cm.dep.1"] = "2l.pan"
meth["cm.dep.2"] = "2l.pan"
# Group is added as a fixed effect to all variables
predmat[,"trial"] = -2
predmat[,"group"] = 1
predmat["sess",]= 0
predmat["depmed",]= 0
predmat["prevpsychoth",]= 0
predmat
trial group sex age sess degree rel child depmed prevpsychoth cm.dep.0 cm.dep.1
trial -2 1 1 1 1 1 1 1 1 1 1 1
group -2 1 1 1 1 1 1 1 1 1 1 1
sex -2 1 0 1 1 1 1 1 1 1 1 1
age -2 1 1 0 1 1 1 1 1 1 1 1
sess 0 0 0 0 0 0 0 0 0 0 0 0
degree -2 1 1 1 1 0 1 1 1 1 1 1
rel -2 1 1 1 1 1 0 1 1 1 1 1
child -2 1 1 1 1 1 1 0 1 1 1 1
depmed 0 0 0 0 0 0 0 0 0 0 0 0
prevpsychoth 0 0 0 0 0 0 0 0 0 0 0 0
cm.dep.0 -2 1 1 1 1 1 1 1 1 1 0 1
cm.dep.1 -2 1 1 1 1 1 1 1 1 1 1 0
cm.dep.2 -2 1 1 1 1 1 1 1 1 1 1 1
cm.dep.2
trial 1
group 1
sex 1
age 1
sess 0
degree 1
rel 1
child 1
depmed 0
prevpsychoth 0
cm.dep.0 1
cm.dep.1 1
cm.dep.2 0
In the end sess, depmed and prevpsychoth are being imputed. Any ideas why this happens?
Setting the column rather than the row to zero as well as emptying the method of not-to-be-imputed variables should work. Example with nhanes data set from mice
library(mice)
m0 <- mice(nhanes, maxit=0)
meth <- m0$method
meth[names(meth) %in% c("bmi")] <- ""
pred <- m0$predictorMatrix
pred[, colnames(pred) %in% c("bmi")] <- 0
imp <- mice(nhanes, predictorMatrix=pred, method=meth)
imp$imp
complete(imp, "long")[1:25, ]
imp <- mice(nhanes, predictorMatrix=pred, method=m0$method)
complete(imp, "long")[1:25, ]
# .imp .id age bmi hyp chl
# 1 1 1 1 NA 1 238
# 2 1 2 2 22.7 1 187
# 3 1 3 1 NA 1 187
# 4 1 4 3 NA 1 206
# 5 1 5 1 20.4 1 113
# 6 1 6 3 NA 1 184
# 7 1 7 1 22.5 1 118
# 8 1 8 1 30.1 1 187
# 9 1 9 2 22.0 1 238
# 10 1 10 2 NA 1 186
# 11 1 11 1 NA 1 238
# 12 1 12 2 NA 1 186
# 13 1 13 3 21.7 1 206
# 14 1 14 2 28.7 2 204
# 15 1 15 1 29.6 1 238
# 16 1 16 1 NA 1 238
# 17 1 17 3 27.2 2 284
# 18 1 18 2 26.3 2 199
# 19 1 19 1 35.3 1 218
# 20 1 20 3 25.5 2 199
# 21 1 21 1 NA 1 187
# 22 1 22 1 33.2 1 229
# 23 1 23 1 27.5 1 131
# 24 1 24 3 24.9 1 186
# 25 1 25 2 27.4 1 186