similar to: Question of nlme package

Dear R-help, The following nlme example, from help(Variogram.gls), does not work on my system. > data(BodyWeight) > fm1 <- gls(weight ~ Time * Diet, BodyWeight) > Variogram(fm1, form = ~ Time | Rat)[1:10,] Error in FUN(X[[1]], ...) : unused argument(s) (method ...) It looks like I might not have loaded a necessary package. I did load nls and lattice (the latter not actually being

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R How to estimate the panel data quantile regression if the regression contains no constant term? I tried to change the code of

Hi all, I am trying to get a handle on gls (package nlme). I have a toy problem: 3 fixed factors (A, B, C), two levels each, 5 replicates per treatment. The response variable is continuous, normal. I have a correlation matrix of the form: > mat A B C A 1.00 0.75 0 B 0.75 1.00 0 C 0.00 0.00 1 which is common to all observations. How do I construct the call to gls? I think I need to

Hello All, This is my third time attempting to post this message. I don't see it in the archive, so I'm guessing it is not getting through. If I am wrong, my apologies. I am trying to do a GLS regression, (X'V^-1X)^-1X'V^-1y, using the gls() function from the nlme package. I have the covariance matrix V. I have been searching for a way to specify the correlation structure

Dear List: My question is more statistical than R oriented (although it originates from my work with nlme). I know statistical questions are occasionally posted, so I hope my question is relevant to the list as I cannot turn up a solution anywhere else. I will frame it in the context of an R related issue. To illustrate the problem, consider student achievement test score data with multiple

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

Hi, Well after trying to fix the assign commands in version 3.1.7 of your nlme package I still cannot get the plot functions to work. Basically I have no other packages installed (apart from those provided with R) so nlme is the first. my R version info is; platform sparc-sun-solaris2.7 arch sparc os solaris2.7 system sparc, solaris2.7 status

I have spatial data on a sphere (the Earth) for which I would like to run an gls model assuming that the errors are autcorrelated, i.e. including a corSpatial correlation in the model specification. In this case the distance metric should be calculated on the sphere, therefore metric = "euclidean" in (for example) corSpher would be incorrect. I would be grateful for help on how to

Hi folks, I am tearing my hair out on this one. I am using an example from Pinheiro and Bates. ### this works data(Orange) mod.lis <- nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange ) ### This works mod <- nlme(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange, fixed = Asymp + xmid + scal ~ 1, start =

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

Hello, I am comparing models made with nlme functions and non-nlme functions, based on Akaike's AIC. The AIC values I get for exactly the same model formulation --for example a linear model with no random effects fit with gls and lm, respectively-- do not fit, although the values of the four model parameters are exactly the same. For example: m1 <- gls(height ~ age, data = Loblolly) m2

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 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) ##

> myfit1 <- gls(col1 ~ col2+col3+col4+col5+col6-1, data=data2, corr=corAR1( 0.3202), method='ML') Error: cannot allocate vector of size 199712 Kb if I get rid of the "corr=corAR1(0.3202)" option, it works okay... can anybody help me? thanks a lot! [[alternative HTML version deleted]]

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

Hello, Does anyone know whether the gls function in the nlme library uses the Newton-Raphson or EM algorithm to find the restricted log-likelihood or maximum log-likelihood estimates? Brendan Klick bklick@jhsph.edu [[alternative HTML version deleted]]

Hello all. I was wondering if the logLike.nls() and logLike.nlme() functions are still being used. Neither function seems to be available in the most recent release of R (1.9.1). The following is contained in the help file for logLik(): "classes which already have methods for this function include: 'glm', 'lm', 'nls' and 'gls', 'lme' and others in

Dear all I have undertaken some phylogenetic and non-phylogenetic regressions with gls() in nlme with single preictor variables. A p value is associated with the intercept (upper p value) and another with the predictor variable (lower). Which p value is important? What does it mean if the intercept p value is insignificant but the predictor is still significant? Thanks a lot, and sorry for my

Dear members of the R-Help Team, we are reaching out to you with a question regarding using intervals()<https://www.rdocumentation.org/packages/nlme/versions/3.1-163/topics/intervals> to create inference for nlme()<https://www.rdocumentation.org/packages/nlme/versions/3.1-164/topics/nlme> function. The maintainer for the nlme package is listed as the R-Core Team and we were advised

lme() and gls() models from the nlme package are all crashing with R.3.4.0. Identical code ran correctly, without error in R 3.3.3 and earlier versions. The behavior is easily demonstrated using one of the examples form the lme() help file, along with two simple variants. I have commented the errors generated by these calls, as well as the lines of code generating them, in the code example below.