I have a data-set containing only Categorical Variables. I needed to do Principal Component Analysis on the data set. Eventually, I found Multiple Correspondence Analysis and learnt it. But, in MCA, it's assumed that, each of the Categorical variable has only one of the levels as True and others as False, i.e an observation can not belong more than 1 category of the same variable.

But in my data-set, I have multiple value for a categorical variable. For example, a disease has a set of symptoms, so, symptoms feature is a multi-valued categorical variable, where each of the symptom creates a different level. So for a disease, symptom feature can have multiple levels as true.

How do I run PCA/MCA/FAMD on such data-set? Is there any solution to this issue?

Thanks a lot for helping.

  • $\begingroup$ Are the symptoms mutually exclusive? $\endgroup$ – eric_kernfeld Apr 2 '18 at 18:40
  • $\begingroup$ Why do you need to do PCA? $\endgroup$ – eric_kernfeld Apr 2 '18 at 18:40
  • $\begingroup$ Yes, for this purpose we're assuming that the symptoms are mutually exclusive. This is for my undergraduate thesis and my supervisor wanted me to find the main factors that can be used to classify and may be even predict potential disease outbreaks. And the categorical variable in question is one of the features. $\endgroup$ – stormblessed Apr 3 '18 at 15:29
  • $\begingroup$ So you want to predict the presence/absence of symptoms, given some other feature? Or you only have the symptom indicators, and you want to infer the underlying disease status? $\endgroup$ – eric_kernfeld Apr 3 '18 at 16:57

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