So far Google hasn't turned up anything.
I have a dataset that has up to 17% missing values for some variables. I'd like to be able to compare the values for all my variables between the complete cases and the cases with any missing values, to determine if my cases with missing data are different from the complete cases. So for continuous variables, I'd like to use a t-test, and for factor variables I'd like to use chi-square. 44% of my data are complete cases.
I will also perform multiple imputation with 20 imputations, to compare those results to my complete case analysis. But I'd really like to know if my complete cases differ from the cases with missing values on all my variables of interest. I can select just the complete cases, but haven't been able to figure out how to compare them to the cases with missing data.
Thanks!
Check any book of statistics and find equation to estimate missing values,then analize statistically.Regards
Hello Christine,
I regularly to this in two steps
1) Create a binary missing indicator based on an ifelse command (actually I use the case_when() function. That is "if variable A and B and ... X are missing, let them be missing = 1, otherwise = 0).
Of course you can be more specific up to create a missing indicator for specific variables
2) Conduct a logistic regression predicting the missing indicator.
HTH
Holger
The problem is not R, but the incompleteness of the dataset.
Looking over how the dataset was built, in your case you need to deal with the missed responses first.
In any decent manual of application statistics you will find techniques for dealing with non-responses.
Christine Marie Mills , you haven't said what you mean by compare, so I'll just assume you have a function comparestuff that does what you want, comparing the complete and incomplete data. It may or may not be something like logistic regression; you haven't provided enough information to say. And let X be your dataset. It is just
comparestuff(X[complete.cases(X)==FALSE,],X[complete.cases(X),])
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