Hello, I have a large data matrix (68x13112), each row corresponding to one observation (patients) and each column corresponding to the variables (points within an NMR spectrum). I would like to carry out some kind of clustering on these data to see how many clusters are there. I have tried the function clara() from the package cluster. If I use the matrix as is, I can perform the clara analysis but when I call clusplot() I get this error:
Error in princomp.default(x, scores = TRUE, cor = ncol(x) != 2) : 'princomp' can only be used with more units than variables Then, I reduce the dimensionality by using the function prcomp(). Then I take the 13 first principal components (80%< variability) and I carry out the clara() analysis again. Then, I call the clusplot() function again and voilà!, it works. The problem is that clusplot() only represents the two first components of my prcomp() analysis, which represents only 15% of the variability. So, my questions are 1) is clara() a proper way to analyze such a large data set? and 2) Is there an appropiate method for graphic plotting of my data, that takes into account the whole variability if my data, not just two principal components? Many thanks. Best, Dani -- Daniel Valverde Saubí Grup de Biologia Molecular de Llevats Facultat de Veterinària de la Universitat Autònoma de Barcelona Edifici V, Campus UAB 08193 Cerdanyola del Vallès- SPAIN Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) Grup d'Aplicacions Biomèdiques de la RMN Facultat de Biociències Universitat Autònoma de Barcelona Edifici Cs, Campus UAB 08193 Cerdanyola del Vallès- SPAIN +34 93 5814126 ______________________________________________ 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.