title: How to exclude missing values from MCA (multiple correspondence analysis) in R?
body: <p>When carrying out multiple correspondence analysis using SPSS, one has three options for how to deal with missing values: one may exclude missing values, impute missing values, or exclude objects with missing values (<a href="http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Fidh_mcan_missing.htm" rel="nofollow">http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Fidh_mcan_missing.htm</a>). However, the R package FactoMineR (<a href="http://www.inside-r.org/packages/cran/FactoMineR/docs/MCA" rel="nofollow">http://www.inside-r.org/packages/cran/FactoMineR/docs/MCA</a>) by default treats missing values as an extra level. </p>
  
  <p>To make up for this problem, the missMDA package exists, which enables the user to impute missing values (<a href="http://math.agrocampus-ouest.fr/infoglueDeliverLive/developpement/missMDA" rel="nofollow">http://math.agrocampus-ouest.fr/infoglueDeliverLive/developpement/missMDA</a>). Wonderful if that's what you want to do with those values, but what if you want to exclude them instead? How can one achieve that using R? (I am open to using a different package than FactoMineR.)</p>