Hi Paco, I got the same problem with you before. Thus, I just impute the missing values For example:
newdata<-as.matrix(impute(olddata, fun="random")) then I believe that you could analyze your data. Hopefully it helps. Chunhao Quoting pacomet <[EMAIL PROTECTED]>:
Hello R users It's some time I am playing with a dataset to do some cluster analysis. The data set consists of 14 columns being geographical coordinates and monthly temperatures in annual files latitutde - longitude - temperature 1 -..... - temperature 12 I have some missing values in some cases, maybe there are 8 monthly valid values at some points with four non valid. I don't want to supress the whole row with 8 good/4 bad values as I wanna try annual and monthy analysis. I first tried kmeans but found a problem with missing values. When trying without omitting missing values kmeans gives an error and when excluding invalid data too many values are excluded in some years of the data series. Now I have been reading about pam, pamk and clara, I think they can handle missing values. But can't find out the way to perform the analysis with these functions. As I'm not an statistics nor an R expert the fpc or cluster package documentation is not enough for me. If you know about a website or a tutorial explaining the way to use that functions, with examples to check if possible, please post them. Any other help or suggestion is greatly appreciated. Thanks in advance Paco -- _________________________ El ponent la mou, el llevant la plou Usuari Linux registrat: 363952 ------- Fotos: http://picasaweb.google.es/pacomet [[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.
______________________________________________ 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.
