Hi all, 

 

i have a data set cintaining 14 columns  and 11 rows. Rows represent single point and columns represent the parameter measured. 

I wiuld like to compare the data to see which are more alike. I used the cluster analysis, but now i ma wondering if there are some other methods, since the cluster analysis did not give me the result i like.

I tried to use factanal() for factor analysis but i geta an error saying that it can only be used on numerical data (which is odd since all i have is numerical data). 

For factanal i used this code :

 

DFm<-read.csv("kemijaM.csv", sep=";", dec=".")           # input data

DF.m<-as.matrix(DFm)                                                               # transform it to matrix                

factanal(DF, factors=5)                                                                # factor analysis

 

Here is what data looks like....

	

'data.frame':  11 obs. of  14 variables:

$ labSt            : Factor w/ 11 levels "2011/9496","2011/9497",..: 1 2 3 4 5 6 7 8 9 10 ...

$ Mangan           : num  0.1313 0.6364 0.0889 0.0028 0 ...

$ Kalcij           : num  0.192 0.566 0.172 0 0.303 ...

$ Magnezij         : num  0.321 0.568 0.29 0.352 0 ...

$ Fluorid          : num  0.6739 0.2283 0.7826 0.0109 0.2609 ...

$ Hidrogenkarbonati: num  0.362 1 0.31 0 0.16 ...

$ Kalij            : num  0.335 0.456 0.335 0.426 0 ...

$ Sulfat           : num  0.692 0 0.517 0.609 0.609 ...

$ Nitrati          : num  0.3934 0.0195 0.5008 0.3835 0.2601 ...

$ Silikati         : num  0.55 1 0.5214 0.0429 0.7143 ...

$ Fe               : num  0.786 0 0.357 0.214 0 ...

$ Natrij           : num  0.1321 0.093 0.093 0.0579 0 ...

$ m.Alkaliteta     : num  0.362 1 0.31 0 0.161 ...

$ Klorid           : num  0.0372 0.02471 0.03895 0.02721 0.00107 ...

	
> 

	

Any ideas would be greatly appreciated. 


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