Comments (1)
Normally we wouldn't call mice.impute.pmm()
directly. The problem here is that reg
is a factor, while mice.impute.pmm()
expects that x
is a model.matrix
not a data.frame
. When you call mice(...)
the proper expansion is done for you.
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.