I have 316 files storing a number of cycles of the respiratory signal from 316 different patients. Each file record is made up of the following data:
amplitude,phase,timestamp,validflag,ttlin,mark,ttlout amplitude: is the position in cm relative to an arbitrary reference (signal amplitude) phase: is the phase value for the current sample (the time point at which the breathing signal was recorded) timestamp: time of the sample measurement in milliseconds validflag: value of 0 indicates a valid track and a periodic signal, and a value less than 0 indicates either a lost track, a bad video signal, or a non periodic signal ttlin: is the bit value indicating the status of a sensed TTL signal. Possible values include 1 (+5 VDC) and 0 (0 VDC). mark: specifies the sample when the phase value is closest to 0 or PI (180 degrees) . Possible values include Z (0 phase), P (PI phase), "" (null string), and - (neither 0 nor PI phase) ttl_out: is the bit value indicating the status of an output TTL signal. Possible values include 1 (+5 VDC) and 0 (0 VDC) The goal is to perform clustering analysis on such data, that is to group togther those which have common characteristics. Which characteristics and how many groups ... ??? ... well this is to be found out. Is R a good tool for analysing many data and find patterns common to data subsets ? Which other tool do you advice ? Thank you very much, -- Maura E.M ______________________________________________ R-help@stat.math.ethz.ch 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.