Little's missing completely at random (MCAR) testSource:
Use Little's (1988) test statistic to assess if data is missing completely
at random (MCAR). The null hypothesis in this test is that the data is
MCAR, and the test statistic is a chi-squared value. The example below
shows the output of
mcar_test(airquality). Given the high statistic
value and low p-value, we can conclude the
airquality data is not
missing completely at random.
tibble::tibble() with one row and four columns:
Chi-squared statistic for Little's test
Degrees of freedom used for chi-squared statistic
P-value for the chi-squared statistic
Number of missing data patterns in the data
Code is adapted from LittleMCAR() in the now-orphaned BaylorEdPsych package: https://rdrr.io/cran/BaylorEdPsych/man/LittleMCAR.html. Some of code is adapted from Eric Stemmler: https://web.archive.org/web/20201120030409/https://stats-bayes.com/post/2020/08/14/r-function-for-little-s-test-for-data-missing-completely-at-random/ using Maximum likelihood estimation from norm.
Little, Roderick J. A. 1988. "A Test of Missing Completely at Random for Multivariate Data with Missing Values." Journal of the American Statistical Association 83 (404): 1198--1202. doi:10.1080/01621459.1988.10478722 .
Andrew Heiss, email@example.com
mcar_test(airquality) #> # A tibble: 1 × 4 #> statistic df p.value missing.patterns #> <dbl> <dbl> <dbl> <int> #> 1 35.1 14 0.00142 4 mcar_test(oceanbuoys) #> # A tibble: 1 × 4 #> statistic df p.value missing.patterns #> <dbl> <dbl> <dbl> <int> #> 1 747. 31 0 6 # If there are non-numeric columns, there will be a warning mcar_test(riskfactors) #> Warning: NAs introduced by coercion to integer range #> # A tibble: 1 × 4 #> statistic df p.value missing.patterns #> <dbl> <dbl> <dbl> <int> #> 1 1741. 1319 3.32e-14 48