Description Usage Arguments Details References Examples
View source: R/mice_extentions.R
A wrapper around the mice function with different defaults and added functionality around excluding predictors that would unacceptibly slow down the imputation.
1 | ImputeData(data, m = 10, maxit = 15, droplist = NULL)
|
data |
A data frame with at least one missing (NA) value. |
m |
The number of complete data sets generated by the imputation procedure |
maxit |
The number of iterations in the Gibbs "chained equations" imputation procedure. the mice package defaults to 5, but their documentation mentions that 15-20 should be sufficient. |
droplist |
A new feature added to |
The procedure works like this...
Stef van Buuren, Karin Groothuis-Oudshoorn (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. URL http://www.jstatsoft.org/v45/i03/.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | data(testdata)
head(testdata)
# a multiply imputed data set, or mids object
mids <- ImputeData(testdata)
# a single imputation
complete.1 <- complete(mids, 1)
head(complete.1)
# another imputation
complete.2 <- complete(mids, 2)
head(complete.2)
mids.2 <- ImputeData(testdata, m = 5, maxit = 5, droplist = c("w"))
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.