Reputation: 65
I am experimenting with the mice package in R and am curious about how i can leave columns out of the imputation.
mice.impute.mean(y, ry, x = NULL, ...)
function seems to be what I would use. I'm struggling to understand what i need to include as the third argument to get this to work. I've been using the mice dataset
nhanes
for my exploration.
Thanks
Upvotes: 3
Views: 3863
Reputation: 7730
I don't know your data thus I can't create a example for you, but you are looking exactly for this parameters of the mice() function
predictorMatrix
A numeric matrix of length(blocks) rows and ncol(data) columns, containing 0/1 data specifying the set of predictors to be used for each target column. Each row corresponds to a variable block, i.e., a set of variables to be imputed. A value of 1 means that the column variable is used as a predictor for the target block (in the rows). By default, the predictorMatrix is a square matrix of ncol(data) rows and columns with all 1's, except for the diagonal. Note: For two-level imputation models (which have "2l" in their names) other codes (e.g, 2 or -2) are also allowed.
With this parameter you can define, which columns you want to use to impute a specific column.
where
A data frame or matrix with logicals of the same dimensions as data indicating where in the data the imputations should be created. The default, where = is.na(data), specifies that the missing data should be imputed. The where argument may be used to overimpute observed data, or to skip imputations for selected missing values.
Here you can define, for which columns you want to create imputation.
Upvotes: 7