similar to: weighted least squares vs linear regression

can you do Logistic regression in R, if so how do you do it and how do you test the fit of a model? -- View this message in context: http://n4.nabble.com/Logistic-regression-tp1059870p1059870.html Sent from the R help mailing list archive at Nabble.com.

Is this book worth its dollar? If so, why?, if not, why not? Cheers. -- View this message in context: http://n4.nabble.com/Ggplot2-Elegant-Graphics-for-Data-Analysis-Use-R-2009-Paperback-tp931702p931702.html Sent from the R help mailing list archive at Nabble.com.

Whats happening? everthings changing round here...plus alot less posts/users.. -- View this message in context: http://n4.nabble.com/Whats-happening-to-the-forum-tp913858p913858.html Sent from the R help mailing list archive at Nabble.com.

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 got simple x,y pairs of data and simple scatterplot and just cannot figure how to do it , there are many examples but always there is error popping out please show me an example stripped with additional data just core of what I need to do to get this damn line -- View this message in context:

Hello, is it possible in R to calculate a Weighted averaging partial least squares regression? I'm not firm in statistics and didn't found anything about weighted averaging in combination with PLS in the help archives. Or is it possible to develop a workaround with the pls-package? thanks for help in advance Andreas Plank -- _____________________________________________ Dipl. Biol.

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

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

Hello, I have a data set of data which is best fit by an exponential decay function. I would like to use a nonlinear weighted least squares regression. What function should I be using? Thank you! [[alternative HTML version deleted]]

Dear all, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of 'nls' one can see that 'nls' generates a vector

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:

Hi there, I'm trying to fit a growth curve to some data and need to use a weighted least squares estimator to account for heteroscedasticity in the data. A weights argument is available in nls that would appear to be appropriate for this purpose, but it is listed as 'not yet implemented'. Is there another package which could implement this procedure? Regards, Robert Brown

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

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

Dear R users, I 'm working on a dataset consisting of 4 different dataframes with tree, leaf, fruit and seed measurements made on 300 trees, coming from 10 provenances (30 trees per provenance, 10 leaves/fruits/seeds per tree). Provenances are fixed effects (they were not randomly chosen), but trees within provenances and leaves/fruits/seeds within trees were randomly assigned. I wanted to

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

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

Does anyone can provide a code to implement least median squares regression in R (not using the lqs function or calling C functions)? Reason: teaching/learning purposes Thanks PM

I need some help with using recursive least squares lm.fit.recursive {quantreg}. I found some references online but cannot find a reproducible example as to how to use recursive least squares. I'd really appreciate if anyone can point me to a reproducible example. [[alternative HTML version deleted]]

Hi all, A good new year for everybody. Could somebody help me on a question? The Singular Value Decomposition of a matrix A gives A = U * D * t(V) I A is a M X N matrix, U is the left singular matrix (M X N), D is a diagonal singular values matrix (N X N) and V is the transpose right singular ortogonal matrix (N X N). By taking the first l columns of V, with gives a (l X l) matrix, i know