Comments (1)
This is expected behaviour, and a consequence of the new where
facility. Your specification does not impute chl
, and mice
will now propagate the missings in the predictors to the target variable, and hence we now find missings also in bmi
and hyp
for those rows with missing values in chl
.
If, in addition, you remove chl
as predictor, then all cells of bmi
and hyp
will be imputed.
I don't think it is necessary to create a warning. This is just how it works now.
from mice.
Related Issues (20)
- Requesting support for GLMMadaptive HOT 1
- Highlight imputed cells in printed data
- Error in pooling ZINB estimates
- Exception needed for multicollinearity error "`No predictors were left`..." for mean imputation? HOT 1
- Error: If no blocks are specified, predictorMatrix must have same number of rows and columns HOT 1
- `ampute()` should preserve the structure of the original data matrix HOT 2
- Reference table of predictor matrix codes
- Error in colMeans(as.matrix(imp[[j]]), na.rm = TRUE) : 'x' must be numeric HOT 4
- Default behavior of `make.predictorMatrix()` outputs `1`s for complete variables HOT 2
- Accidentally repeated roxygen comments HOT 3
- Add `cluster` argument to `make.method()` and `make.predictorMatrix()`
- ampute.discrete failing when input data set contains character/categorical variables HOT 1
- pooling parameters df_error = "Inf" HOT 1
- pool() on lavaan objects gives error: illegal arguments passed to lavaan::parameterEstimates HOT 1
- pool() on lavaan fit objects gives error: coef() not available on S4 object HOT 2
- Add cluster variable to `ampute()` for multilevel amputation
- Change reference level in logistic regression following MI using MICE HOT 2
- Chi square tests following multiple imputation
- mice::ampute() not working properly when adding character variables HOT 1
- futuremice error with blocks - possibly due to how ibind deals with blocked imputations
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from mice.