similar to: Help with recursive least squares

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

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 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

Hi everyone, I admit I am a bit of an R novice, and I was hoping someone could help me with this error message: Warning message: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : extra arguments weigths are just disregarded. My equation is: lm( Y ~ X1 + X2 + X3, weigths = seq(0.1, 1, by = 0.1)) -- View this message in context:

As John noted, there are different kinds of weights, and different terminology: * inverse-variance weights (accuracy weights) * case weights (frequencies, counts) * sampling weights (selection probability weights) I'll add: * inverse-variance weights, where var(y for observation) = 1/weight (as opposed to just being inversely proportional to the weight) * weights used as part of an

Hi R-programmers ! I would like to perform a linear model regression month by month using the 'lm' function and i don't know how to do it. The data is organised as below: Month ExcessReturn Return STO 8 0.047595875 0.05274292 0.854352503 8 0.016134874 0.049226941 4.399372005 8 -0.000443869 0.004357305 -1.04980297 9 0.002206554 -0.089068828 0.544809429 9 0.021296551

dear R community: i have been looking but failed to find the following: is there a function in R that updates a plain OLS lm() model with one additional observation, so that I can write a function that computes recursive residuals *quickly*? PS: (I looked at package strucchange, but if I am not mistaken, the recresid function there takes longer than iterating over the models fresh from

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

Howdy, In SPSS, there are 2 ways to weight a least squares regression: 1. You can do it from the regression menu. 2. You can set a global weight switch from the data menu. These two options have no, in my experience, been equivalent. Now, when I run lm in R with the weights= switch set accordingly, I get the same set of results you would see with option #1 in SPSS. Does anybody know how to

Hi, I want to plot the residuals of a least-squares regression. plot(lm(y~x), which=1) does this, but it plots the y-axis of my data on the x-axis of the residuals plot. That is, it plots the residual for each y-value in the data. Can I instead use the x-axis of my data as the x-axis of the residuals plot, showing the residual for a given x? Thanks! Jason Priem University of North

Dear R-helpers, I am looking, quite unsuccesfully, for a number of functions/packages. Firstly, I am interested in a package for partial least squares. I have found that there seemed to exist a package called pls, but which seems not to run any more with modern versions of R. I have not been able to find certain "chemometrics package" I found some people discussing about in this

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

Dear all, I'm struggling with weighted least squares, where something that I had assumed to be true appears not to be the case. Take the following data set as an example: df <- data.frame(x = runif(100, 0, 100)) df$y <- df$x + 1 + rnorm(100, sd=15) I had expected that: summary(lm(y ~ x, data=df, weights=rep(2, 100))) summary(lm(y ~ x, data=rbind(df,df))) would be equivalent, but

Hello guys. I've built and uploaded to CRAN an R interface to MINPACK Fortran library, which solves non-linear least squares problem by modification of the Levenberg-Marquardt algorithm. The package includes one R function, which passes all the necessary control parameters to the appropriate Fortran functions. The package location is

Hello guys. I've built and uploaded to CRAN an R interface to MINPACK Fortran library, which solves non-linear least squares problem by modification of the Levenberg-Marquardt algorithm. The package includes one R function, which passes all the necessary control parameters to the appropriate Fortran functions. The package location is

Hi all, I met with a problem about the weighted least square regression. 1. I simulated a Normal vector (sim1) with mean 425906 and standard deviation 40000. 2. I simulated a second Normal vector with conditional mean b1*sim1, where b1 is just a number I specified, and variance proportional to sim1. Precisely, the standard deviation is sqrt(sim1)*50. 3. Then I run a WLS regression without the

Hello, I have the following model: lm.7 <- lm(Y ~ F + C1 + C2 , data = EM4) F is a 4-level factor, the rest are covariates centered at their mean (Y is a two-column matrix). I have tried to find functions to give the model-adjusted means (adjusted at the covariates'means) and their standard deviations for each. (That is, what I believe is called in SAS "least square or LS-means,

Hi, I want to plot the residuals of a least-squares regression. plot(lm(y~x), which=1) does this, but it plots the y-axis of my data on the x-axis of the residuals plot. That is, it plots the residual for each y-value in the data. Can I instead use the x-axis of my data as the x-axis of the residuals plot, showing the residual for a given x? Thanks! Jason Priem University of North

Hello R experts, I have two questions related to the nnls library (http://www.inside-r.org/packages/cran/nnls), and more broadly to linear regression with positive coefficients. Sample code is below the Qs. Q1: Regular regression (with lm) gives me the significance of each variable. How do I get variable significance with nnls? If there's no ready function, any easy way to manually derive

Hi I am looking for suitable packages in R that do regression analyses using least median squares method (or better). Additionally, I am also looking for packages that implement algorithms/methods for detecting outliers that can be discarded before doing the regression analyses. Although some websites refer to "lms" method under package "lps" in R, I am unable to find such a