Tests the null hypothesis that the missing data is Missing Completely At Random (MCAR). A p.value of less than 0.05 is usually interpreted as being that the missing data is not MCAR (i.e., is either Missing At Random or nonignorable). See What are the Different Types of Missing Data? for more information about MCAR and other types of missing data.
Example
The default "Summary" output is shown below:
Options
Variables The variables to appear in the rows, as categories.
Output

 Summary Shows a nicely formatted table of the test results (default).
 R The original textbased output from the LittleMCAR function.
Variable names Display Variable Names in the output, instead of Variable Labels.
More decimal places Display numeric values with 8 decimal places.
Filter The data is automatically filtered using any filters prior to estimating the model.
Technical information
This test starts by using the EM algorithm to estimate the means and covariances. These estimates are approximate, and, consequently, the test is also approximate (it may get slightly different results on different computers).
This test does not take weights into account.
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How to Check Missing Data Using Little's MCAR Test in Displayr