similar to: least squares solution to linear system

I am using the non-linear least squares routine in "R" -- nls. I have a dataset where the nls routine outputs tight confidence intervals on the 2 parameters I am solving for. As a check on my results, I used the Python SciPy leastsq module on the same data set and it yields the same answer as "R" for the coefficients. However, what was somewhat surprising was the the

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

I am using "nls" to fit dose-response curves but am not sure how to approach more robust regression in R to get around the problem of the my error showing increased variance with increasing dose. My understanding is that "rlm" or "lqs" would not be a good idea here. 'Fairly new to regression work, so apologies if I'm missing something obvious.

I need to find out the difference between the way R calculates weighted regression and standard regression. I want to plot a 95% confidence interval around an estimte i got from least squares regression. I cant find he documentation for this ive looked in ?stats ?lm ?predict.lm ?weights ?residuals.lm Can anyone shed light? thanks Chris. -- View this message in context:

Dear R-ians, I want to perform an linear unmixing of image pixels in fractions of pure endmembers. Therefore I need to perform a constrained linear least-squares problem that looks like : min || Cx - d || ? where sum(x) = 1. I have a 3x3 matrix C, containing the values for endmembers and I have a 3x1 column vector d (for every pixel in the image). In theory my x values should all be in the

HI, I'd like to perform a weighted linear least squares fit with R on data with varying errors on both vectors. I can do this with one axis using lm, but have no idea where to go from here. I've tried googling, but no idea. Any suggestions? Thanks, James

Hi, everyone, My question is: It's not every time that you can get a converged result from the nls function. Is there any solution for me to get a reasonable result? For example: x <- c(-0.06,-0.04,-0.025,-0.015,-0.005,0.005,0.015,0.025,0.04,0.06) y <- c(1866760,1457870,1314960,1250560,1184850,1144920,1158850,1199910,1263850,1452520) fitOup<- nls(y ~ constant + A*(x-MA)^4 +

Thanks for the response. I was not very clear in my original request. What I am asking is if in a non-linear estimation problem using nls(), as the condition number of the Hessian matrix becomes larger, will the t-values of one or more of the parameters being estimated in general become smaller in absolute value -- that is, are low t-values a sign of an ill-conditioned Hessian? Typical

Hi, I need help regarding estimating a linear model where restrictions are imposed on the coefficients. An example is as follows: Y_{t+2}=a1Y_{t+1} + a2 Y_t + b x_t + e_t restriction a1+ a2 =1 Is there a function or a package that can estimate the coefficient of a model like this? I want to estimate the coefficients rather than test them. Thank you for your help Ahmad Abu Hammour --------------

Dear list, I have encountered a special case for searching a linear regression where I'm not satisfied with the results obtained using the traditional least squares method (sometimes called OLS) for estimating/optimizing the residues to the regression line (see code below). Basically, a group of my x-y data are a bit off the diagonal line (in my case the diagonal represents the ideal or

Dear all, After a thorough research, I still find myself unable to find a function that does linear regression of 2 vectors of data using the "total least squares", also called "orthogonal regression" (see : http://en.wikipedia.org/wiki/Total_least_squares) instead of the "ordinary least squares" method. Indeed, the "lm" function has a

Hello I would like to calculate a weighted line integral. The integral is calculated by the cells that this lines trasverses (the small cells belong to matrix (m*n) that represent the value that a specific area has. I need to calculate the weights by finding out how much the line touches or impinges inside a cell. The weight is less if a line just touches one of the four edges of a square

Hi, I have always been wondering why S-Plus/R can not fit a linear model to an arbitrary large data set given that, I thought, it should be pretty straightforward. Sometime ago I came across a reference to LM package, http://www.econ.uiuc.edu/~anovo/LM.html, by Roger Koenker and Alvaro Novo. So I thought here it is at last, but to my surprise this project hasn't made to the recommended

I would like to solve the equation is is the sum from k = i to N of choose(N,k) * MR ^ k * (1 - MR) ^ (N - k) - 0.50 = 0 I want to solve for MR. This seems like a non-linear equation to me. But I am having a hard time writing the function that implements the above. I could use 'for(...) as a brute force appoarch but I would like a more "elegant" solution. The variables 'N'

Sorry for all the newbie questions. I really appreciate everyone's help today. Okay I've got outgoing and incoming calls working with no echo. yay! Now I'm having an issue with SIP extension to extension calling. Any time I dial another extension it goes right into voice mail. My extensions.conf is pretty small and rough but, here's what I have right now. Most of it was taken

Dear all, When doing nonlinear regression, we normally use nls if e are iid normal. i learned that if the form of the variance of e is not completely known, we can use the IRWLS (Iteratively Reweighted Least Squares ) algorithm: for example, var e*i =*g0+g1*x*1 1. Start with *w**i = *1 2. Use least squares to estimate b. 3. Use the residuals to estimate g, perhaps by regressing e^2 on

Hi All, I've found a few references in the mailing list and documentation to constrained least squares fitting, but little on restrained least squares. To clarify what I mean, a constraint might limit a parameter to a particular value (e.g. x=5.0, or exactly within the bounds 4.9 - 5.1), whereas a restraint adds some further information to the problem about the certainty of the starting point

Dear R Users, I would like to make a lesast squares regression similar to that what is done by the command "lm". But additionally, I would like to impose some restrictions: 1) The sum of all regression coefficients should be equal to 1. 2) Each coefficient should assume a value between 0 and 1. (inequality restrictions) Which command is the best to use in order to solve this problem

Hi everyone, I am running the 'gls' command (least squares method) for a number of data out of which many are zeros. I strongly believe that the output is wrong and I think that this is due to the large number of zero values included in my dataset. I would like to ask if there is a command that would allow me to run the gls command disregarding all the zero values? Thank you in

Hi guys I have data that contains the variances vt of the yields of 1, 2, 3, 4, 5,10, 20 year bonds. Assuming the Hull-White model for the yield of a t-year zero-coupon bond, I have to estimate the ? of the Hull-White model using nonlinear least squares and give a 95% con?dence interval for each parameter. Please can you guys tell how to find out ? using R. Any suggestion regarding what functions