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I have a character matrix for some different plant species, in which most species are missing data for at least a few characters. I want to do a principal components analysis, so I tried to impute the missing values, but when I try to do that I get the error message: 

Error in eigen(crossprod(X, X), symmetric = TRUE) : infinite or missing values in 'x'

I thought having missing values was kind of the point! Does anyone know what I'm doing wrong?

Joe
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Mireille Farjo
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  • How did you impute te missing values? Also provide sample data using `dput(your data)` and sample code. – NelsonGon Dec 13 '18 at 03:43
  • When asking for help, you should include a simple [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) with sample input and desired output that can be used to test and verify possible solutions. But I don't think PCA can be performed with missing data. You'd have to do the decomposition with complete cases only. – MrFlick Dec 13 '18 at 05:46
  • See this question for useful information on imputation. https://stackoverflow.com/questions/39862778/imputation-with-column-medians-in-r – Joe Dec 13 '18 at 13:34

2 Answers2

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You should use the function imputePCA available in the package missMDA.

For more information: http://factominer.free.fr/missMDA/index.html

Best

Francois

Husson
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I have the same issues when I try to impute dataset digits with missing values. One reason that could lead to the above Error is that you have features with 0 standard deviations (because their values are the same). Check this discussion: https://community.rstudio.com/t/problem-with-missmda/90809/7

Vu Mai Anh
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