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 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
