CODEWITHSUNDEEP
rpcacorrespondence-analysisfactominer
HP_17
HP_17

Reputation: 203

How many principal components does MCA outputs in R using FactorMineR library?

I'm not sure about the output of the MCA() function in the FactoMineR library in R.

For example I want 10 components, so I'm running the following code:

X1 <- MCA(data, ncp = 10)

Does ncp (which stands for number of dimensions) give me 10 components??

Upvotes: 0

Views: 245

Answers (1)

streamOfThought
streamOfThought

Reputation: 21

npc is the argument in MCA that specifies the number of dimensions you want to store, so when you look at the results or the summary you can see the values (cos2, contrib, etc) for the variables.

Definition you can find by typing ?MCA in R:

  • ncp - Number of dimensions kept in the results (by default 5)

Upvotes: 0

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