search for: _constant_

...gt; I suspect it comes about via a mental short-circuit: If we try to control p using a tolerance, then that amounts to saying that the remaining PCs are effectively zero-variance, but that is (usually) not the intention at all. > > The common case is that the remainder terms have a roughly _constant_, small-ish variance and are interpreted as noise. Of course the magnitude of the noise is important information. > But then you should use Factor Analysis which has that concept of ?noise? (unlike PCA). Cheers, Jari Oksanen >> On 25 Mar 2016, at 00:02 , Steve Bronder <sbronder at s...

I agree with Kasper, this is a 'big' issue. Does your method of taking only n PCs reduce the load on memory? The new addition to the summary looks like a good idea, but Proportion of Variance as you describe it may be confusing to new users. Am I correct in saying Proportion of variance describes the amount of variance with respect to the number of components the user chooses to show? So

dear vorbis developers, it would be very useful, if oggenc would support constant low-bandwith bitrates for testing. something from 20kbps to 44kbps, so anyone could test streaming over modem and isdn. audio quality doesn't matter. mörk --- >8 ---- List archives: http://www.xiph.org/archives/ Ogg project homepage: http://www.xiph.org/ogg/ To unsubscribe from this list, send a message to

...inly wrong. I suspect it comes about via a mental short-circuit: If we try to control p using a tolerance, then that amounts to saying that the remaining PCs are effectively zero-variance, but that is (usually) not the intention at all. The common case is that the remainder terms have a roughly _constant_, small-ish variance and are interpreted as noise. Of course the magnitude of the noise is important information. -pd > On 25 Mar 2016, at 00:02 , Steve Bronder <sbronder at stevebronder.com> wrote: > > I agree with Kasper, this is a 'big' issue. Does your method of takin...

...spect it comes about via a mental short-circuit: If we try to control p using a tolerance, then that amounts to saying that the remaining PCs are effectively zero-variance, but that is (usually) not the intention at all. >> >> The common case is that the remainder terms have a roughly _constant_, small-ish variance and are interpreted as noise. Of course the magnitude of the noise is important information. >> > But then you should use Factor Analysis which has that concept of ?noise? (unlike PCA). Actually, FA has a slightly different concept of noise. PCA can be interpreted a...

...4 thanks to Mikael Jagan. * dummy.coef(.) now also works for lm()-models with character categorical predictor variables rather than factor ones, fixing PR#18635 reported by Jinsong Zhao. * formals(f) <- formals(f) now also works for a function w/o arguments and atomic _constant_ body(f). * Correct as.function(<invalid list>, .)'s error message. * removeSource() is yet more thorough in finding and removing "srcref" and the other source references from parsed R language chunks, fixing PR#18638 thanks to Andrew Simmons. * dgeom()...

...4 thanks to Mikael Jagan. * dummy.coef(.) now also works for lm()-models with character categorical predictor variables rather than factor ones, fixing PR#18635 reported by Jinsong Zhao. * formals(f) <- formals(f) now also works for a function w/o arguments and atomic _constant_ body(f). * Correct as.function(<invalid list>, .)'s error message. * removeSource() is yet more thorough in finding and removing "srcref" and the other source references from parsed R language chunks, fixing PR#18638 thanks to Andrew Simmons. * dgeom()...