Block-wise imputation

[alexanderrobitzsch]

Hi Stef,

I tried the functionality of block-wise imputation. I used your example with the "jomoImpute" imputation method. My syntax is as follows (only slightly adapted from the manual)

data(nhanes, package="mice")
dat <- nhanes
dat$age[c(1,2)] <- NA

blocks <-  list( block1=c("bmi", "chl", "hyp"), "age")
method <- c("jomoImpute", "pmm")

dat <- dat[, unlist(blocks)]
ini <- mice::mice(dat, blocks = blocks, method = method, maxit = 0)
pred <- ini$pred
imp <- mice::mice(dat, blocks = blocks, method = method, pred = pred, maxit = 1, m=1)

So, I want to inpute bmi, chl and hyp (say X1, X2, X3) in a block and age (say Z) based on a single conditional model. My understanding is that I want to impute the first block based on the distribution P(X1, X2, X3|Z) and age based on P(Z|X1,X2,X3). I played a bit with the source code (which is here

https://github.com/stefvanbuuren/mice/blob/master/R/mice.impute.jomoImpute.R

However, it seems the for imputing X=(X1,X2,X3), the original data data is used and not the updated values of Z. Is this a bug or is this by intention implemented in this way?

Best,
Alexander

[alexanderrobitzsch]

An additional note. The lines 91-96 in the function mice:::sampler() are

# multivariate imputation - type and formula
if (mult) {
   mis <- !r
   mis[, setdiff(b, colnames(data))] <- FALSE
   data[mis] <- NA
  # [...]
}

I think that the following change would fix the issue

if (mult) {
   mis <- !r
   for (j in b){
	data[ mis[,j] , j ] <- NA
  }
  # [...]
}

[stefvanbuuren]

Alexander, as you suspected, this is a bug. Thanks for catching this.

In mice 3.4.3 I have corrected this as

mis <- !r
mis[, setdiff(colnames(data), b)] <- FALSE
data[mis] <- NA

The bug ignores imputed values in variables outside the blocks when using multivariate imputation. It happens when using block-wise imputation with multivariate missing data.

My mistake was in interchanging the roles of the arguments in setdiff(), something that bugs me more often.

[alexanderrobitzsch]

Thank you for the fix.