similar to: Least square with Bounds

Is there a function in R for constrained linear least squares? I used the matlab function LSQLIN: my aim is to obtain non-negative regression coefficients which sum 1. Thanks in advance, domenico vistocco ___________________________________ Yahoo! Mail: gratis 1GB per i messaggi e allegati da 10MB http://mail.yahoo.it

Hi everybody, please, could you give me help ? I scanned the help archives and didn't found hints... I want to solve a large sparse linear system subjected to an inequality constrains (all solutions positive) and an equality constrain (all solutions sum to 1), thus I tried to fool constrOptim using: x[1] + 0 + ... + 0 >= 0 ... 0 + 0 + ... + x[n] >= 0 x[1] + x[2] + ... +

Hi, I have a least square fitting problem with linear inequality constraints. pcls seems capable of solving it so I tried it, unfortunately, it is stuck with the following error: > M <- list() > M$y = Dmat[,1] > M$X = Cmat > M$Ain = as.matrix(Amat) > M$bin = rep(0, dim(Amat)[1]) > M$p=qr.solve(as.matrix(Cmat), Dmat[,1]) > M$w = rep(1, length(M$y)) > M$C = matrix(0,0,0)

Hello! I am running R-2.3.1-i386-1 on Slackware Linux 10.2. I am a former matlab user, moving to R. In matlab, via the cftool, I performed nonlinear curve fitting using the method "nonlinear least squares" with the "Trust-Region" algorithm and not using robust fitting. Is it possible to perform the same analysis in R? I read quite a lot of R documentation, but I could not find

Hello all, I am using the function lm to do my weighted least square regression. model<-lm(Y~X1+X2, weight=w) What I am confused is the r.squared. It does not seem that the r.squared for the weighted case is an ordinary 1-RSS/TSS. What is that precisely? Is the r.squared measure comparable to that obtained by the ordinary least square? <I also notice that model$res is the unweighted

Dear all I am having a linear system of the form A*X=B and I want to find the X by using least squares. For example my A is of dimension [205,3] and my B is of dimension[205,1]  I am looking for the X matrix which is of the size [3,1]. In the matlab I was doing that by the function  X = LSCOV(A,B) returns the ordinary least squares solution to the     linear system of equations A*X = B, i.e., X

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

Hello, I performed a Mantel test and plotted communitiy similarities. I would like to add a least square line. I thought about using abline taking as slope the r-statistic of the Mantel test and calculating the y-intercept analytically. Is this method correct? Is there any function for this calculation? Thank you -- View this message in context:

Dear R Users, I would like to use R to fit a Weighted Least Square model for a binary outcome, say Y. The model is the one widely used for a binary dependent variable when the logistic model has not been proposed. Does anyone know how to specify the weight as the square root of 1/(E(Y)(1-E(Y)) in lm() or any other regression functions? I know that varPower() in the package of gls() can provide

Dear friends I want to estimate an equation using two-stage least square but suspect that the model suffers from autocorrelation. Can someone please advise how to implement bootstrapping method in order to calculate the correct standard errors in R? Thank you. Kind regards Thanaset -- View this message in context:

I would really appreciate it if someone can give suggestions on how to do spectra fitting in R using ordinary least square fitting and non-negativity constraints. The lm() function works well for ordinary least square fitting, but how to specify non-negativity constraints? It wouldn't make sense if the fitting coefficients coming out as negative in absorption spectra deconvolution. Thanks.

Hello R-helpers, I would like to calculate least square means after having built a GLM with quasipoisson errors. In my model the dependent variable is continuous, I have one continuous independent variable and one categorical independent variable (that is the variable for which I would like to calculate the least square means). I've looked around for the command to calculate the least

Hi, I am new to R, and am trying to solve the following optimization problem: This is a nonlinear least squares problem. I have a set of 3D voxels. All I need is to find a least squares fit to this data. The data model actually represent a cube-like structure, consisting of seven straight lines. The lines have some intersections (and at this intersection both of the participating lines end).

I m in front of a problem of simultaneity bias between two equations and would like to apply the two stage level least square....Is there a special command in R ? I didn't found something about that in the R help. Thanks for your help !!!! ************************************************************** Mathieu Vuilleumier - collaborateur scientifique Institut de recherches ??conomique et

Hi to all, Is there a way we can fit a non linear model to a data using non linear least square method without necessarily initialising the parameters of the model. I find it hard to get the initial value of the parameter. Below is a sample of the code I have. *nachman<-nls(OARmedium$OCCUPANCY~1exp(-alpha*OARmedium$MEAN^beta),start=list(alpha=0.2,beta=0.1),data=OARmedium)summary(nachman)*

I have been using R and SAS from past 6 months and i found a interesting thing while doing PLS in R and SAS is that when we use NO SCALE option in SAS and scale=FALSE in R , we see the estimates are matching but if we use scaling option in SAS and R the estimates differ to greater extent , you can try with any data set we will get very different estimates while using the scaling option. can any

Dear R Users, I would like to use R to fit a Weighted Least Square model for a binary response variable, say Y. The model is actually the model widely used for a binary dependent variable when the logistic model has not been invented. The weight is 1/(E(Y)(1-E(Y)). Could someone help me out? Thanks for any replies in advance! Best Regards, Vivian

http://r.789695.n4.nabble.com/file/n4619404/pic1.jpg cesres_ext <- nls(lnGDP85~ intercept + (alpha/(1-alpha-beta)) * lns_ikonngdelta + (beta/(1-alpha-beta)) * lns_ihonngdelta + 0.5 * ((sigma-1)/sigma) * (1/((1-alpha-beta)*(1-alpha-beta))) * (alpha * taylor1 + beta * taylor2 - alpha*beta*taylor3) ,start = list(intercept=8, alpha=0.2, beta=0.4, sigma=1.2),data=data) I have this model. I use

Hi, I'm responding to a post about finding roots of a cubic or quartic equation non-iteratively. One obviously could create functions using the explicit algebraic solutions. One post on the subject noted that the square-roots in those solutions also require iteration, and one post claimed iterative solutions are more accurate than the explicit solutions. This post, however, is about

Dear Mr Graves, Thank you very much for your response. Nobody else from this mailing list ventured to reply to me for the two weeks since I posted my question. "nlminb" and "optim" are just optimization procedures. What I need is not just optimization, but a nonlinear CURVE FITTING procedure. If there is some way to perform nonlinear curve fitting with the