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IMHO the Aitcheson approach makes things ticketyboo with respect to statistical assumptions but throws the baby out with the bathwater in terms of getting results of interest to anyone but a statistician. Rich Ulrich wrote: >On 29 May 2003 13:55:10 -0700, [EMAIL PROTECTED] (christoph) wrote: > > > >>sorry, maybe an all too trivial question. But we have power data from J >>frequency spectra and to have the same range for the data of all our >>subjects, we just transformed them into % values, pseudo-code: >> >>power[i,j]=power[i,j]/sum(power[i,1:J]) >> >>of course, now we have perfect collinearity in our x design-matrix, >>since all power-values for each subject sum up to 1. >> >>How shall we solve this problem: just eliminate one column of x, or >>introduce a restriction which says exactly that our power data sum up to >>1 for each subject? >> >> > >Compositional data. >A few days ago, someone posted news of a new edition (I >think it was this book) by Aitchison. My stats-FAQ at > http://www.pitt.edu/~wpilib/statfaq/compfaq.html >includes about 8 other references. > >@Article{aitchison82, > author = "J. Aitchison", > title = "The statistical analysis of compositional data", > journal = jrssb, > year = 1982, > volume = 44, > number = 2, > pages = "139-177", > annote = "With discussion." > >I my own experience with power-spectra, I found it >stabilizing to use the logit of the relative power. Of course, >that got rid of the sum-to-1 problem. > > > --------------060504030600030501060501 Content-Type: text/html; charset=us-ascii Content-Transfer-Encoding: 7bit <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <meta http-equiv="Content-Type" content="text/html;charset=ISO-8859-1"> <title></title> </head> <body> IMHO the Aitcheson approach makes things ticketyboo with respect to statistical assumptions but throws the baby out with the bathwater in terms of getting results of interest to anyone but a statistician.<br> Rich Ulrich wrote:<br> <blockquote type="cite" cite="[EMAIL PROTECTED]"> <pre wrap="">On 29 May 2003 13:55:10 -0700, <a class="moz-txt-link-abbreviated" href="mailto:[EMAIL PROTECTED]">[EMAIL PROTECTED]</a> (christoph) wrote: </pre> <blockquote type="cite"> <pre wrap="">sorry, maybe an all too trivial question. But we have power data from J frequency spectra and to have the same range for the data of all our subjects, we just transformed them into % values, pseudo-code: power[i,j]=power[i,j]/sum(power[i,1:J]) of course, now we have perfect collinearity in our x design-matrix, since all power-values for each subject sum up to 1. How shall we solve this problem: just eliminate one column of x, or introduce a restriction which says exactly that our power data sum up to 1 for each subject? </pre> </blockquote> <pre wrap=""><!----> Compositional data. A few days ago, someone posted news of a new edition (I think it was this book) by Aitchison. My stats-FAQ at <a class="moz-txt-link-freetext" href="http://www.pitt.edu/~wpilib/statfaq/compfaq.html";>http://www.pitt.edu/~wpilib/statfaq/compfaq.html</a> includes about 8 other references. @Article{aitchison82, author = "J. Aitchison", title = "The statistical analysis of compositional data", journal = jrssb, year = 1982, volume = 44, number = 2, pages = "139-177", annote = "With discussion." I my own experience with power-spectra, I found it stabilizing to use the logit of the relative power. Of course, that got rid of the sum-to-1 problem. </pre> </blockquote> <br> </body> </html> --------------060504030600030501060501-- . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
