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How to impute missing values with MICE package in R?
Impute missing values with MICE package in R. Political scientists are beginning to appreciate that multiple imputation represents a better strategy for analysing missing data to the widely used method of listwise deletion. A very clear demonstration of this was a 2016 article by Ranjit Lall, an political economy professor in LSE.
Is there a package for sensitivity analysis in RStudio?
I also saw that there is an older package, SensMice (from 2011), which performs sensitivity analysis after the mice imputation has already been done. However, it no longer seems to be compatible with my version of Rstudio and there doesn’t seem to be much recent word on this package.
What does mice stand for in imputation package?
Unlike what I initially thought, the name has nothing to do with the tiny rodent, MICE stands for Multivariate Imputation via Chained Equations. Rather than abruptly deleting missing values, imputation uses information given from the non-missing predictors to provide an estimate of the missing values.
Which is the correct method for sensitivity analysis?
Alternatively, I have seen that the mice package has a method called mice.impute.ri which can be used with ‘non-ignorable data’. I also saw that there is an older package, SensMice (from 2011), which performs sensitivity analysis after the mice imputation has already been done.
What kind of data can the mice algorithm impute?
The MICE algorithm can impute mixes of continuous, binary, unordered categorical and ordered categorical data. In addition, MICE can impute continuous two-level data, and maintain consistency between imputations by means of passive imputation. Many diagnostic plots are implemented to inspect the quality of the imputations.
Which is the software norm for multiple imputation in R?
The standalone Software NORM now also has an R-package NORM for R (package). Another R-package worth mentioning is Amelia (R-package). Now, we turn to the R-package MICE („multivariate imputation by chained equations“) which offers many functions to generate imputed datasets based on your missing data.