similar to: Non Negative Least Squares Regression with nnls

A new package 'nnls' is now available on CRAN. The package provides an R interface to the Lawson-Hanson NNLS algorithm for non-negative least squares that solves the least squares problem A x = b with the constraint x >= 0. The Lawson-Hanson NNLS algorithm was published in Lawson CL, Hanson RJ (1974). Solving Least Squares Problems. Prentice Hall, Englewood Cliffs, NJ. Lawson CL,

A new package 'nnls' is now available on CRAN. The package provides an R interface to the Lawson-Hanson NNLS algorithm for non-negative least squares that solves the least squares problem A x = b with the constraint x >= 0. The Lawson-Hanson NNLS algorithm was published in Lawson CL, Hanson RJ (1974). Solving Least Squares Problems. Prentice Hall, Englewood Cliffs, NJ. Lawson CL,

A new package 'bvls' is available on CRAN. The package provides an R interface to the Stark-Parker algorithm for bounded-variable least squares (BVLS) that solves A x = b with the constraint l <= x <= u under least squares criteria, where l,x,u \in R^n, b \in R^m and A is an m \times n matrix. The Stark-Parker BVLS algorithm was published in Stark PB, Parker RL (1995).

A new package 'bvls' is available on CRAN. The package provides an R interface to the Stark-Parker algorithm for bounded-variable least squares (BVLS) that solves A x = b with the constraint l <= x <= u under least squares criteria, where l,x,u \in R^n, b \in R^m and A is an m \times n matrix. The Stark-Parker BVLS algorithm was published in Stark PB, Parker RL (1995).

Thank you Brian Ripley, Gardar Johannesson, and Marcel Wolbers for your prompt and friendly help! I will share any further learnings as I move through these suggestions. -Bob Abugov Brian Ripley wrote: I just use optim() on the sum of squares with non-negativity constraints. That did not exist in 1999. Gardar Johannesson wrote: You can always just use the quadratic programing library in R

Hi all, I am trying to do an inversion of electromagnetic data with non-negative least squares method (Tikhonov regularisation) and have got it programmed in S-Plus. However I am trying to move all my scripts from S-Plus to R. Is there an equivalent to nnls.fit in R? I think this can be done with pcls? Right? S-Plus script: A, L and data are matrices, lambda is a vector of possible lambda

I'm trying to get significance of coefficients as for lm() but I news help. Inviato da iPad [[alternative HTML version deleted]]

Dear All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is

Hi, recently I saw but did not pay too much attention to a question that concerned regression with positive coefficients. In Splus, thereis the nnls() function that can be used if I am not wrong, but what about R ? Now I have the same problem: doing a logistic regression under constraint that coefs are non negative. What can I do with R? is there a (weighted) nnls() counterpart available? Thanks

I believe if you add the *decoder* lookahead time to the encoder lookahead time you are already referencing, you will get the numbers that you have calculated. --John On 4/11/2012 1:00 PM, speex-dev-request at xiph.org wrote: > Message: 1 > Date: 10 Apr 2012 22:07:20 +0200 > From: Thilo K?hler<koehlerthilo at gmx.de> > Subject: [Speex-dev] Speex Codec Delay Problem > To:

Hello All! SPEEX introduces an additional delay to the audio data, I found out by reverse enginiering (it is NOT the lookahead time): narrow band : delay = 200 - framesize + lookahead = 200 - 160 + 40 = 80 samples wide band : delay = 400 - framesize + lookahead = 400 - 320 + 143 = 223 samples uwide band : delay = 800 - framesize + lookahead = 800 - 640 + 349 = 509 samples To get the

Hi, We are seeing performance degradation when running the same R script in multiple instances of R on a multi-processor system. We are a bit surprised by this because we figured that each instance of R is running in its own processor, and therefore running a second, third or fourth instance should not affect the performance of the first instance. Here's a test script that exhibits this

1. What to do with the last frame that is smaller then frame size? During encoding, the last frame is often smaller than the required frame size. In the sample code, proper number of zeros are padded at the end. So if I don't want those padded zero after decoding, I assume that it is up to me to keep track of the number of zeros. Is it right? 2. What does SPEEX_GET_LOOKAHEAD do? How to

On 2015-12-18 02:29, Simon Urbanek wrote: > Michael, > > I got access to PDP AIX so I can try to replicate your problem. Can you, please, share exactly your setup - AIX version and well as how exactly you installed the compilers (=where from)? I can then try to replicate it. AFAICS there is no official binary for gfortran nor gcc 4.7 so it must be some 3rd party - which could also be a

Jia Pu a ?crit : > 1. What to do with the last frame that is smaller then frame size? > During encoding, the last frame is often smaller than the required frame > size. In the sample code, proper number of zeros are padded at the end. > So if I don't want those padded zero after decoding, I assume that it is > up to me to keep track of the number of zeros. Is it right? Right.

Hello! I am encoding small snippets of audio (e.g. 100ms) that contain important audio from the first sample till the last one. (means they dont start/end silent). Doing so raised a couple of questions I couldnt solve by reading the docu/faq/internet search. 1. Does the "complexity" parameter influence only the speed of the encoder or also the speed of the decoder? (I need fast

> >> 2. What does SPEEX_GET_LOOKAHEAD do? How to use it? >> In speexenc.c, there is following code: >> >> ... >> speex_encoder_ctl(st, SPEEX_GET_LOOKAHEAD, &lookahead); >> ... >> nb_encoded = -lookahead; >> >> Can someone explain what this means? > > The lookahead is the number of samples you need to discard at the > start.

hi there, I am new to mailing list so excuse me if I don't obey to the 'netiquette'. i am writing voice chat and speex is in the root of it. i write it in Java and use JNI to link with 'C'-based Speex 1.2beta. [I know of JSpeex but there are not implemented some features] recently i decided to use DTX feature of speex as well. the code follows. The problem is that no matter

On Sun, Jun 19, 2005 at 08:07:50PM -0400, Jean-Marc Valin wrote: > Hi Ralph, > > What speexenc does (Speex itself does not know about Ogg) is that it gives > packet N the granulepos "N*frame_size - lookahead". In the case of narrowband, > the first frame would have granulepos "1*160 - 80", so 80. I can't say about > your example because Speex cannot

Dear R users, I am currently attempting to fit logistic regression models in R, where the slopes should be restricted to positive values. Although I am aware of the package nnls (which does the trick for linear regression models), I did not find any solution for logistic regression. If there is any package available for this purpose, I would be interested to know them. Alternatively, I realize