Hi,

Please don't cross-post. It's also not necessary to post more than once to
the same list if you don't get an immediate response, especially if you've
posted on the weekend.

On Sunday, December 8, 2013, Gundala Viswanath wrote:

> Hi,
>
>
> According to daisy function from cluster documentation, it can compute
> dissimilarity when NA (missing) value(s) is present.
>
> http://stat.ethz.ch/R-manual/R-devel/library/cluster/html/daisy.html
>
> But why when I tried this code
>
> library(cluster)
> x <- c(1.115,NA,NA,0.971,NA)
> y <- c(NA,1.006,NA,NA,0.645)
> df <- as.data.frame(rbind(x,y))
> daisy(df,metric="gower")
>
> It gave this message:
>
> Dissimilarities :
>    x
> y NA
>
> Metric :  mixed ;  Types = I, I, I, I, I
> Number of objects : 2
> Warning messages:
> 1: In min(x) : no non-missing arguments to min; returning Inf
> 2: In max(x) : no non-missing arguments to max; returning -Inf


The third column of your dataframe (note that df() is a base function and
thus a bad name for a user object) is all NA, so it's impossible to apply
the Gower standardization. daisy() does handle NA values, but it can't read
minds to figure out what you expect if all values are NA.


> I welcome other alternative than gower.
>
> I expect the dissimilarity output gives a non-NA value e.g. 0. What's
> the right way to do it?


If a column has all NA values then it adds nothing to the analysis except
problems, and you need to remove it.

Sarah




-- 
Sarah Goslee
http://www.stringpage.com
http://www.sarahgoslee.com
http://www.functionaldiversity.org

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