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Can a mouse handle 80% of missing data?
Mice can handle a large amount of missing data. Especially if there are a lot of columns with few missing data, one with 80% is no problem. You can also expect that in most of the times adding this variable leads to better imputation results than leaving it out.
How does the mice imputation function work in R?
I was wondering if anyone had experience using the mice function, as described in mice: Multivariate Imputation by Chained Equations in R (JSS 2011 45 (3))? I have a dataset with a number of variables, each with varying degrees of missing data.
Is there a cutoff for missing data in multiple imputation?
That’s an advantage of having multiple imputations and analyzing results from all of the imputations. More important than a “cutoff” for missing data is to consider carefully (1) the intended use of your model and (2) whether the “missing-at-random” assumptions needed for multiple imputation holds in your case.
What happens when there are too many missing data points?
Variables with lots of missing data points would be expected to end up with larger error terms than those with fewer missing data points, so your ability to detect significant relations to those variables would be limited accordingly. That’s an advantage of having multiple imputations and analyzing results from all of the imputations.
Is the MICE package really a multiple imputation algorithm?
Thanks for helping! mice is a multiple imputation package. Multiple Imputation itself is not really a imputation algorithm – it is rather a concept how to impute data, while also accounting for the uncertainty that comes along with the imputation.
How to use multiple imputation for missing data?
Multiple Imputation itself is not really a imputation algorithm – it is rather a concept how to impute data, while also accounting for the uncertainty that comes along with the imputation. If you just want one imputed dataset, you can use Single Imputation packages like VIM (e.g. the function irmi () or kNN () ).
Which is an example of multiple imputation in R?
A typical example would be, that people refuse to answer questions on their income if it exceeds a certain amount. Or if you ask for the number of sex partners a person had and people with high numbers don´t answer it. In this case your data is not missing at random.