similar to: Error message

Hi I have a huge array with series of data. For each cell in the array I fit a linear model, either using lm() or gls() with lm() there is no problem, but with gls() I get an error: Error in glsEstimate(glsSt, control = glsEstControl) : computed gls fit is singular, rank 2 as soon as there are data like this: > y1 <- c(0,0,0,0) > x1 <- c(0,1,1.3,0) > gls(y1~x1)

Dear R developers, first of all let me join the chorus of congratulations for the release of R 1.0.0. Well, done! Unfortunately, I find it necessary to e-mail in a bug report regarding the `nlme' package. On my office machine I experience the following trouble: bossiaea:/opt/R$ R CMD check -c nlme Checking package `nlme' ... Massaging examples into `nlme-Ex.R' ... Running

Dear All -- I am trying to use within a little table producing code an anova comparison of two gls fitted objects, contained in a list of such object, obtained using nlme function gls. The anova procedure fails to locate the second of the objects. The following code, borrowed from the help page of anova.gls, exemplifies: --------------- start example code --------------- library(nlme) ##

Hi, I need to correct for ar(1) behavior of my residuals of my model. I noticed that there are multiple gls models in R. I am wondering if anyone has experience in choosing between gls models. For example, how should one decide whether to use lm.gls in MASS, or gls in nlme for correcting ar(1)? Does anyone have a preference? Any advice is appreciated! Thanks, -- Tom [[alternative HTML

I'm getting some odd results computing log-likelihoods with gls using splines with increasing degrees of freedom -- the deviance *increases* substantially with increasing df. (Since spline models with increasing df aren't nested, it need not decline monotonically but I would expect it to have a decreasing trend!) I may just be confused, but I *think* the issue is somewhere within the

Hi: I have data from an assay in the form of two vectors, one is response and the other is a predictor. When I attempt to fit a 5 parameter logistic model with "nls", I get converged parameter estimates. I also get the same answers with "gnls" without specifying the "weights" argument. However, when I attempt to use the "gnls" function and try to

Hi, I am not sure this is a bug but I can repeat it, The functions and data are below. I know this is nasty data, and it is very questionable whether a 4pl model is appropriate, but it is data fed to an automated tool and I would have hoped for an error. Does this repeat for anyone else? My details: > version _ platform i686-pc-linux-gnu

Hi, I want to test for independence in my GLS model fitp2, but when I try to use the dwtest function in the lmtest library, I get the error message "Error in terms.default(formula) : no terms component". The model and data set are below. Any suggestions would be really helpful! Thanks a lot in advance, M. Grace fitp2:

Hi, I would like to use a weighted lm model to reduce heteroscendasticity. I am wondering if the only way to generate the weights in R is through the laborious process of trial and error by hand. Does anyone know if R has a function that would automatically generate the weights need for lm? Thanks, -- Tom [[alternative HTML version deleted]]

Hello everybody, I have a dataset where each row has number of subjects and that gives me natural weights for the variance function. Additionally I see that variance increases with Age, which is a regressor. So using gls I have weights=varFixed(~1/n) but don't know how to include the extra effect of the regressor. Fitted values show a quadratic curve vs. age, not sure if that helps.

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

Dear R-help, I don't understand why Gls gives me an error when trying to fit a model with AR(2) errors, while gls (from nlme) does not. For example: library(nlme) library(rms) set.seed(1) d <- data.frame(x = rnorm(50), y = rnorm(50)) gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error # Error in

Dear All, I would like to be able to estimate confidence intervals for a linear combination of coefficients for a GLS model. I am familiar with John Foxton's helpful paper on Time Series Regression and Generalised Least Squares (GLS) and have learnt a bit about the gls function. I have downloaded the gmodels package so I can use the estimable function. The estimable function is very

Could someone tell me how to access the log likelihood of a poisson model? I've done the following.... <BEGIN R STUFF> freq.mod <- glm(formula = nfix ~ gls.gls + pol.gls + pol.rel + rac.gls + rac.pol + rac.rac + rac.rel + white + gls.gls.w + pol.gls.w + pol.rel.w + rac.gls.w + rac.pol.w + rac.rac.w + rac.rac.w + rac.rel.w, family = poisson, data = Complex2.freq, offset = lnoffset)

A couple weeks ago I posted a message on this topic to r-help, the response was that this seemed like odd behavior, and that I ought to post it to one of the developer lists. I posted to r-sig-mixed-models, but didn't get any response. So, with good intentions, I decided to try posting once more, but to this more general list. The goal is (1) FYI, to make you aware of this issue, in case it

Dear List: I am estimating a gls model and am having to make some rather unconventional modifications to handle a particular problem I have identified. My aim is to fit a GLS with an AR1 structure, obtain the variance-covariance matrix (V), modify it as needed given my research problem, and then reestimate the GLS by brute force using matrix operations. All seems to be working almost perfectly,

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

Hi all, I wrote a piece of code that generates simulated variables. after variable generation I use them in several analyzes. However, when I use a for to repeat the procedure 1000 times I get an erro message in one of the "for" steps, precisely at this time: gls.temp<- gls(y2 ~ x2,correlation=corExp(form=~coord2[,1]+coord2[,2])) # coord 2 are spatial coordinates and the error

Full_Name: W.B.Kloke Version: 1.8.0 OS: FreeBSD-4.7 Submission from: (NULL) (195.253.22.63) I found a parameter evaluation inconsistency in NLME package. I tried to use gls() inside a function, and I wanted use gls() for different subsets of a data frame: prgls <- function(name){ gls( log10(Y)~(cond-1)+(cond-1):t ,pr,subset=subject==name)} Applying this function with a string as parameter

Dear R-List users, Can anyone explain exactly the difference between Weights options in lm glm and gls? I try the following codes, but the results are different. > lm1 Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 > lm2 Call: lm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.04193 7.30660 > lm3 Call: