similar to: WLS regression, lm() with weights as a matrix

How does one specify the weights for WLS in the systemfit command ? That is, there is a weight option in lm(), but there doesn't seem to be weight option for systemfit("WLS") Thanks!

I would like to obtain Confidence Intervals for the estimates (unstandardized beta weights) of each predictor in a WLS regression: m1 = lm(x~ x1+x2+x3, weights=W, data=D) SPSS offers that output by default, and I am not able to find a way to do this in R. I read through predict.lm, but I do not find a way to get the CIs for multiple independent variables. Thank you Torvon [[alternative HTML

I might be sorry for asking this question :-) I have two equations and I tried to estimate them individually with "lm" and "gls", and then in a system (using systemfit) with "OLS", "WLS" and "SUR". Quite surprisingly (for myself at least) the results are identical to the last digit. Could someone (please!) give a hint as to what am I

Hi, I am fitting a weighted least square regression and trying to compute SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know what I am coding wrong. Can you help please? xnam <-colnames(X) # colnames Design Matrix fmla1 <- as.formula(paste("Y ~",paste(xnam, collapse=

Hi all, I understand that gls() uses generalized least squares, but I thought that maybe optimum weights from gls might be used as weights in lm (as shown below), but apparently this is not the case. See: library(nlme) f1 <- gls(Petal.Width ~ Species / Petal.Length, data = iris, weights = varIdent(form = ~ 1 | Species)) aa <- attributes(summary(f1)$modelStruct$varStruct)$weights f2 <-

R 1.6.2 for Windows, Win2k: I have fitted a weighted least squares model using the code "wls.out <- gls(y ~ x1 + x2 + x3 + x4 + x5 + x6 - 1, data = foo.frame, weights = varConstPower(form = ~ fitted(.), fixed = list(power = 0.5), const = 1))" The data has 62 rows and the response is zero when the covariates are zero. The purpose of the model was to account for the the fact that

I have a WLS regression with 1 dependent variable and 3 independent variables. I wish to constrain the predicted values (the fitted values) so that they are greater than zero (i.e. they are positive). I do not know how to impose this constraint in R. Please respond if you have any suggestions. There are some previous postings about constraining the coefficients, but this won't accomplish

Hi, I load my data set and separate it as folowing: presu <- read.table("C:/_Ricardo/Paty/qtdata_f.txt", header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dep<-presu[,3]; exo<-presu[,4:92]; Now, I want to use it using the wls and quantreg packages. How I change the data classes for mle and rq objects? Thanks a lot,

Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)??

Dear maintainers, I'm reporting a bug in qr.coef that mishandles the colnames of matrix. A minimal reproducible example is as follows: x <- cbind(rep(0, 10), rep(0, 10)) y <- rep(0, 10) q <- qr.default(x) qr.coef(q, y) [1] NA NA If x has colnames, then qr.coef will end up with an error: x <- cbind(x1 = rep(0, 10), x2 = rep(0, 10)) y <- rep(0, 10) q <- qr.default(x)

Hi, please help!! I've been trying to fit variogram models using the REML method in the gstat package. Every time the Windows GUI crashes. For example library(gstat) data(meuse) x <- variogram(zinc ~ 1, ~x + y, meuse) v <- vgm(140000, "Sph", 800, nug = 10000) plot(x, model = fit.variogram(x, model = v, fit.method=5)) Other fit methods are non problematic (eg. fit.method=7

Greetings from Rio de Janeiro, Brazil. I am looking for advice / references on binary logistic regression with weighted least squares (using lrm & weights), on the following context: 1) unbalanced sample (n0=10000, n1=700); 2) sampling weights used to rebalance the sample (w0=1, w1=14.29); e 3) after modelling, adjust the intercept in order to reflect the expected % of 1?s in the population

Hello. I'm am trying to follow a recommendation to deal with a dependent variable in a linear regression. I read that, due to the positive trend in my dependent variable residual vs mean function, I should 1) run a linear regression to estimate the standard deviations from this trend, and 2) run a second linear regression and use 1 / variance as weight. These might be terribly stupid

I run 9 WLS regressions in R, with 7 predictors each. What I want to do now is compare: (1) The strength of predictors within each model (assuming all predictors are significant). That is, I want to say whether x1 is stronger than x2, and also say whether it is significantly stronger. I compare strength by simply comparing standardized beta weights, correct? How do I compare if one predictor is

Hi! I have used the Matrix package (Version: 0.995-10) successfully to obtain the OLS solution for a problem where the design matrix X is 44000x6000. X is very sparse (about 80000 non-zeros elements). Now I want to do WLS: (X'WX)^-1X'Wy I tried W=Diagonal(length(w),w) and wX=solve(X,W) but after various minutes R gives a not enough memory error (Im using a 64bit machine with 16Gigs

Dear Users, I loaded my dataset as following: presu <- read.table("C:/_Ricardo/Paty/qtdata_f.txt", header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dep<-presu[,3]; exo<-presu[,4:92]; When I try: rq(dep ~ exo, ...) or mle.stepwise(dep ~ exo, ...) I got the same error: > rq(dep ~ exo) Error in model.frame(formula, rownames,

Dear Statisticians--- This is not even an R question, so please forgive me. I have so much ignorance in this matter that I do not know where to begin. I hope someone can point me to documentation and/or a sample. I want to compute a covariance as quickly as non-humanly possible on an Intel core processor (up to SSE4) under linux. Alas, I have no idea how to engage CPU vectorization. Do I need

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

Hi, The OpenSSH_3.7.1p2, Solaris 8 case: non-interactive authentication method (publickey) works for root only ---------------------------------------------------------------------------- --------- We installed OpenSSH_3.7.1p2, SSH protocols 1.5/2.0, OpenSSL 0.9.7c We need to copy a file by SFTP from App server to a DB server with passwordless method. [cbfe-dev-app01 (client), user cbfesit]

Hi all, I am working on modeling an instruction similar to SystemZ's 'BRCT', which takes a register, decrements it, and branches if the register is nonzero. I saw that the LLVM backend for SystemZ generates the instruction in a MachineFunctionPass as part of a pass intended to eliminate or combine compares. I then looked at ARM, where it uses the HardwareLoops pass first, and then a