MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)

Mortaza Jamshidian, Siavash Jalal and Camden Jansen

Journal of Statistical Software, 2014, vol. 056, issue i06

Abstract: Researchers are often faced with analyzing data sets that are not complete. To properly analyze such data sets requires the knowledge of the missing data mechanism. If data are missing completely at random (MCAR), then many missing data analysis techniques lead to valid inference. Thus, tests of MCAR are desirable. The package MissMech implements two tests developed by Jamshidian and Jalal (2010) for this purpose. These tests can be run using a function called TestMCARNormality. One of the tests is valid if data are normally distributed, and another test does not require any distributional assumptions for the data. In addition to testing MCAR, in some special cases, the function TestMCARNormality is also able to test whether data have a multivariate normal distribution. As a bonus, the functions in MissMech can also be used for the following additional tasks: (i) test of homoscedasticity for several groups when data are completely observed, (ii) perform the k-sample test of Anderson-Darling to determine whether k groups of univariate data come from the same distribution, (iii) impute incomplete data sets using two methods, one where normality is assumed and one where no specific distributional assumptions are made, (iv) obtain normal-theory maximum likelihood estimates for mean and covariance matrix when data are incomplete, along with their standard errors, and finally (v) perform the Neyman’s test of uniformity. All of these features are explained in the paper, including examples.

Date: 2014-01-25
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v056i06/v56i06.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... issMech_1.0.1.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v056i06/v56i06.R

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:056:i06

DOI: 10.18637/jss.v056.i06

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum (baum@bc.edu).