Cluster Analysis in R with missing data

Question

So I spent a good amount of time trying to find the answer on how to do this. The only answer I have found so far is here: How to perform clustering without removing rows where NA is present in R

Unfortunately, this is not working for me.

So here is an example of my data (d in this example):

Q9Y6X2           NA -6.350055943 -5.78314068
Q9Y6X3           NA           NA -5.78314068
Q9Y6X6  0.831273549  4.875151493  0.78671493
Q9Y6Y8  4.831273549  0.457298979  5.59406985
Q9Y6Z4  4.831273549  4.875151493          NA

Here is what I tried:

> dist <- daisy(d,metric = "gower")
> hc <- hclust(dist)
Error in hclust(dist) : NA/NaN/Inf in foreign function call (arg 11)

From my understanding daisy should be able to handle NA values, but I am still receiving an error when trying to cluster my results.

Thanks.

Answer 1

If you look at the dist matrix, you will see that there is an NA present, because samples Q9Y6X3 and Q9Y6Z4 have no overlap. This results in an NA in your dist matrix, which hclust doesn't like. You could potentially force the NAs to be 0 or something, but I am not sure if that wouldn't leave statistical bias.

Answer 2

Mixture models permit clustering of data set with missing values, by assuming that values are missing completely at random (MCAR). Moreover, information criteria (like BIC or ICL) permit to select the number of clusters. You can use the R package VarSelLCM to cluster these data (there is a Shiny application to interpret the results). A tutorial of this package is available here

Answer 3

Within 2nd answer to the following post: How to perform clustering without removing rows where NA is present in R, such bug in the "daisy" function was reported. Formerly the function was coded by:

if (any(ina <- is.na(type3))) 
stop(gettextf("invalid type %s for column numbers %s", 
    type2[ina], pColl(which(is.na))))

The intended error message was not printed, as which(is.na) was wrongly used instead of which(ina).

The author of this function contained in the "cluster" package acknowledged the issue and fixed the code back in June 2015. http://svn.r-project.org/R-packages/trunk/cluster/R/daisy.q