similar to: please help with estimation of true correlations and reliabilities

Jens I'm not sure what you intend by "predefined assumptions". 1. If you merely want to conduct an exploratory rather than confirmatory analysis for the relevant paths, there are ways within SEM to do this. (In this case you could use John Fox's SEM package). 2. If you do not wish to assume multivariate normality, then you may use a variety of alternative (to maximum likelihood)

Dear John, Dear Joseph, Thank you for your quick answers and the pointer to semnet. I try to clarify on my assumptions: - yes, I am willing to assume multivariate normality - no, I don't want to assume a single factor model - I assume there is an unknown number of factors, and I do not know which items belong to which factors but I still want to estimate single item reliabilities Is this

hi, am trying to understand speex's algo. have a few questions. 1) autocorrelation: in the function, _spx_autocorr (for floating point version), there is a line ac[0] += 10; correct me if i am wrong, i suppose the addition of 10 is used to condition the autocorrelation matrix. wonder how the value of 10 is arrived at? 2) levinson durbin (LD) algo in the function _spx_lpc, i referred

Dear all, Has someone implemented in R (or any other language) Knol DL, ten Berge JMF. Least-squares approximation of an improper correlation matrix by a proper one. Psychometrika, 1989, 54, 53-61. or any other similar algorithm? Best regards Jens Oehlschl?gel Background: I want to factanal() matrices of polychoric correlations which have negative eigenvalue. I coded Highham 2002

Dear list members, Could any expert on factor analysis be so kind to explain how to calculate AIC on the output of factanal. Do I calculate AIC wrong or is factanal$criteria["objective"] not a negative log-likelihood? Best regards Jens Oehlschl?gel The AIC calculated using summary.factanal below don't appear correct to me: n items factors total.df rest.df model.df

Dear list members, Could any expert on factor analysis be so kind to explain how to calculate AIC on the output of factanal. Do I calculate AIC wrong or is factanal$criteria["objective"] not a negative log-likelihood? Best regards Jens Oehlschl?gel The AIC calculated using summary.factanal below don't appear correct to me: n items factors total.df rest.df model.df

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

Hi all, I'm getting the following error from substring: > substr("<I>Jens Oehlschl\xe4gel-Akiyoshi", 1, 100) Error in substr("<I>Jens Oehlschl\xe4gel-Akiyoshi", 1, 100) : invalid multibyte string at '<e4>gel-A<6b>iyoshi' Is that normal / intended? I've tried setting the Encoding/locale to Latin-1/UTF-8 but that does not help. nchar

Hello Jean-Marc, I solved the variable delay problem, but I still have trouble with speex_echo_cancel(). When i try testecho.c with clean speech for far-end input and same speech with attenuation, a bit of reverb and 50-150 ms delay, all this done in sound editor, for mic input, i get 5-8 db attenuation. But when i use the same speech played and recorded for mic input, i see about 5 db of

In R factanal output, I can't find a function to give me residuals e. I mannually got it by using x -lamda1*f1 -lamda2*f2 - ... -lamdan*fn, but the e I got are not uncorrelated with all the f's. What did I do wrong? Please help. Yijun ____________________________________________________________________________________ Be a better friend, newshound, and

Recently, I was working with some lagged designs where a vector of observations at one time was used to predict a vector of observations at another time using a lag 1 design. In the work, I noticed a lot of negative correlations, so I ran a simple simulation with 2 matched points. The crude simulation example below shows that the correlation can be -1 or +1, but interestingly if you do this

Hi list! My Problem: all calls to international numbers will be dropped after exactly 15 minutes... I have a VoIP-account by Deutsche Telekom. This is what I see when I call someone (my parents) and the connection will be dropped: == Using SIP RTP CoS mark 5 -- Executing [+39015222222 at default:1] Set("SIP/00493511111111-00000125", "newNumber=0039015222222") in new

Hello Everyone, I am using the opus 1.1 multistream APIs to encode a 5.1 surround stream on the server, stream it to client, decode it and capture the pcm data. I noticed that there was severe corruption/attenuation on one of the channels(specifically Back/Rear Right). This would appear to be the last channel in the stream. I am attaching an image of the PCM dumps from the original and the one

Thanks for the quick response Ivan. readLines with encoding='latin1' works for me (on Ubuntu). However I was more concerned with the inconsistency in results between substr and regexpr. I was expecting that if one of them errors because of an unknown encoding then the other should as well. Even better, if regexpr works, why shouldn't substr work as well? Incidentally the analogous

Had a try. The reason why a simple delay is not that good is mainly due to the initialization of the filter parameter that still takes a few seconds (if they are perfectly in sync, you sort of get lucky). Otherwise, you real recording seems to have something odd in it. Are you sampling from a different card then the one that's playing the sound? or maybe the mic (or something else) in the room

On Wed, May 24, 2017 10:45 am, Warren Young wrote: > On May 24, 2017, at 8:52 AM, Chris Adams <linux at cmadams.net> wrote: >> >> Once upon a time, Warren Young <warren at etr-usa.com> said: >>> a. It???s transmitting from a fixed location in a time zone you >>> probably aren???t in ??? US Mountain ??? being the least populous of >>> the lower

I'm pretty much sure of it. When I test inverting the inputs, my output is pretty much the same as my speaker signal. Whereas the way that I normally test the output is my mic signal with very little attenuation. If you are interested I can send my test files; they are about 94KB each. -Jason --- Jean-Marc Valin <jean-marc.valin@usherbrooke.ca> wrote: > Are you sure you're

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

I ran some further tests on mdf and here are the results: 1. reduced tail length to 100ms, aligned mic and speaker signals to within 10ms - almost no echo attenuation 2. aligned mic and speaker signals to within 5 samples - still almost no echo attenuation 3. ran testecho using the same file for mic and speaker - very good echo cancellation (of course this is expected, but I needed to do a sanity